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SIMULATION, MODELING AND ANALYSIS OF A WATER TO AIR HEAT PUMP By ARUN SHENOY Bachelor of Engineering Bangalore University Bangalore, India 2001 Submitted to the Faculty of the Graduate College of the Oklahoma State University in partial fulfillment of the requirements for the Degree of MASTER of SCIENCE December, 2004 ii SIMULATION, MODELING AND ANALYSIS OF A WATER TO AIR HEAT PUMP Thesis Approved: ________________Dr. D.E.Fisher____________________ Thesis Advisor _________________Dr.J.D.Spitler___________________ __________________ Dr. D.G.Lilley_________________ _________________Dr.Emslie Gordon________________ Dean of the Graduate College iii ACKNOWLEDGEMENTS Many people have been a part of my graduate education, as friends, teachers, and colleagues. Dr. Fisher, first and foremost, has been all of these. The best advisor and teacher I could have wished for, he is actively involved in the work of all his students, and clearly always has their best interest in mind. Many thanks to the members of my thesis committee, Dr. Spitler and Dr. Lilley for helping to supervise me, providing resources and subjects, and offering direction and criticism. I have also benefited from many course discussions with the members. Special thanks to Sankar and Haider; their comments, encouragement and criticism helped me many times. I cannot end without thanking my father Late Shri.H Gourishanker Shenoy my mother Late Smt.H. Geetha Shenoy, and my sister H.Kiran Shenoy on whose constant love and memory I have relied throughout my time at OSU. iv TABLE OF CONTENTS Chapter Page 1. INTRODUCTION……………………………………………………………...1 1.1.Background………………………………………………………………....1 1.2.Thesis objective and scope………………………………………………...2 2. LITERATURE REVIEW…………………….………………………………...3 2.1. Equation fit water to air heat pump and chiller models…….........................3 2.1.1. Lash model……………………………………………..……………3 2.1.2. Allen and Hamilton model...………………………………………...5 2.1.3. DOE2 model………………………………………………………..7 2.1.4. BLAST model……………………………………………………….7 2.1.5. Hamilton and Miller model……………………………………….. ..9 2.1.6. Parent and Larue development of Domanski model………………..10 2.2. Jin and Spitler’s parameter estimation water to air heat pump model.…....11 2.2.1. Overview…………………………………………………………....11 2.2.2. Parameter estimation procedure...……………………...… ….…….14 2.2.3. Model implementation……………………………………………...15 3. OVERVIEW OF ENERGYPLUS……………………………………………..17 3.1. Introduction………………………………………………………………...17 3.2. Simulation environment…………………………………………………...17 3.3. Water source heat pumps in EnergyPlus……………………..…………...20 3.3.1. Water to air heat pump simulation………………..………….……...20 3.3.2. Water to water heat pump simulation…………………………….....21 3.4. Implementing watertoair heat pump models in EnergyPlus……….……..22 v 4. MODEL DEVELOPMENT AND IMPLEMENTATION………………..…....24 4.1. Development of the simplified water to air heat pump model……………..24 4.2. Design Basis………………………………………………………...….......24 4.2.1. Cooling mode equations...……………………………………….….27 4.2.2. Heating mode equations...……………………………………….….31 4.2.3. Accommodating variable mass flow rates.…………..…….……..…32 4.2.4. Determination of performance coefficients……………………….....33 4.2.5. Performance coefficients calculation procedure…………………......34 4.3. Model implementation……………………………………………...…........35 4.3.1. Input configuration…………………………………………………..36 4.3.2. Input specification………………………………...............................37 4.3.3. Flow of control/ implementation algorithm………………………...41 4.3.4. Output arrangement………………………………..............................45 5. MODEL VERIFICATION……………………………………………………..46 5.1. Introduction……………………………………………….……………... ...46 5.2. Cooling mode verification…………………………….……………...........46 5.3. Heating mode verification…………………………….……………...........51 5.4. Verification using correction factors……………………..………...….... ..55 5.4.1. Correction factors..…………………………....…………………......55 5.4.2. Comparison of simulated and measured data using correction factors………………………………………………57 5.4.2.1.Comparison procedure………………………………………….58 5.4.2.2.Results……………….………………………………………….60 5.4.3. Application of the model outside the catalog data sets ….………..…64 6. MODEL APPLICATION………………………………………………………67 6.1. Using Manufacturers’ data…………………………………………….....…67 6.1.1. Example 1 Florida Heat Pump……………………………………...68 6.1.2. Example 2 ClimateMaster……………….……………………….....70 6.1.3. Example 3 TRANE………………………………………...…….....72 vi 6.2. Case Study……………... ………………………………………………….76 6.2.1. Example building and system description……………………..........76 6.2.2. System connections and configuration….…………………………...78 6.2.3. Annual and design day building load profiles …………………….79 6.2.4. Selection of heat pump…………………............................................82 6.2.5. Cooling mode operation and analysis…………………......................83 6.2.6. Heating mode operation and analysis…………………......................86 6.2.7. Model validation by comparison with the detailed model………......89 6.2.8. Model performance under off design conditions...………..…….......92 6.2.9. Annual simulation computation time………………………………94 7. PERFORMANCE COEFFICIENT CALCULATOR………………………….95 7.1. Need for an interface…………………………………….……………... …95 7.2. Front end architecture……………………………….……………........ …..95 7.2.1. Workspace……………………………………………………….......95 7.2.2. Input format……………….………………………………………...100 7.2.3. Reporting results…………………………………….........................104 7.3. Backend architecture……………………………….……………... ……...106 8. CONCLUSIONS AND RECOMMENDATIONS……..……………………..107 8.1. Conclusions………..…………………………………….………………...107 8.2. Recommendations..……………………………….…………….................109 References……………………………………………………………………...111 Appendix A…………………………………………………………………….113 vii LIST OF FIGURES Figure Page 2.1. Heat Pump system nodes (Lash 1990)…………………….. ……………….…4 2.2. Water chiller system schematic………………………… …………………….5 2.3. Schematic of an air conditioning system showing connecting points between components…………………………………………………………..10 2.4. Flow diagram for model implementation computer program ...………………13 2.5. Information flowchart for model implementation (Jin 2002)…………………14 3.1. Overall hierarchy of EnergyPlus………………………………………………18 3.2. EnergyPlus Nodal Connections (EnergyPlus guide for module developers (EnergyPlus2002)………….……..19 3.3. HVAC loop connections for a water to water heat pump simulation ……...….20 3.4. HVAC loop connections for a water to air heat pump simulation ……………21 3.5. Schematic of blowthrough Water to air heat pump …………………………22 4.1. Manufacturer’s catalog for Florida Heat Pump GT030………...……………..25 4.2. Water to air heat pump cycle…………………………...……………………..29 4.3. Informational flowchart for model implementation………………………......34 4.4. Correspondence required between the IDD and the IDF…..………………….37 4.5. Input object for the simple water to air heat pump model defined in the IDD..39 4.6. Input object for the heating and cooling coils as defined in the IDD ……..…40 4.7. Hierarchical flow of simulation……………………………………………….42 4.8. Flow of code for the simple water to air heat pump model…………...………44 5.1. Catalog cooling capacity v/s calculated cooling capacity (heat pump #1) …....47 5.2. Catalog power consumption v/s calculated power consumption viii (heat pump #1)…………………………………………………………………48 5.3. Catalog sensible capacity v/s calculated sensible capacity (heat pump#1) ....48 5.4. Catalog v/s calculated heat transfer rate (heat pump#1)………………….…..49 5.5. Catalog cooling capacity v/s calculated cooling capacity (heat pump #2)……49 5.6. Catalog power consumption v/s calculated power consumption (heat pump #2)…………………………………………………………………50 5.7. Catalog sensible capacity v/s calculated sensible capacity (heat pump#2) ….50 5.8. Catalog v/s calculated heat transfer rate (heat pump#1)……………….……..51 5.9. Catalog heating capacity v/s calculated heating capacity (heat pump #1) …...52 5.10. Catalog power consumption v/s calculated power consumption (heat pump #1)…………………………………………………………….……52 5.11. Catalog v/s calculated heat transfer rate (heat pump#1) ……………………....53 5.12. Catalog heating capacity v/s calculated heating capacity (heat pump #2) ……53 5.13. Catalog power consumption v/s calculated power consumption (heat pump #2)…………………………………………………………….……54 5.14. Catalog v/s calculated heat transfer rate (heat pump#1) ………………….…..54 5.15. Influence of air flow correction factors on performance…………………..…....56 5.16. Influence of wet bulb correction factors on performance…...……………..…....56 5.17. Correction factors for GC ClimateMaster series…………………………...…...59 5.18. Comparison of catalog cooling v/s calculated cooling capacities with and without the correction factors for wet bulb temperatures………….….61 5.19. Comparison of catalog cooling v/s calculated sensible cooling capacities with and without the correction factors for wet bulb temperatures……….….…62 5.20. Comparison of catalog cooling v/s calculated power consumption with and without the correction factors for wet bulb temperatures………….….62 5.21. Comparison of catalog cooling v/s calculated cooling capacities with and without the correction factors for variable air flow rates………….…..63 5.22. Comparison of catalog cooling v/s calculated sensible cooling capacities ix with and without the correction factors for variable air flow rates……….….…64 5.23. Comparison of catalog cooling v/s calculated power consumption with and without the correction factors for variable air flow rates………….….64 5.24. Comparison of catalog v/s calculated cooling capacity for all points with the correction factors(simplified model  equation(415))…………………66 5.25. Comparison of catalog v/s calculated sensible capacity for all points with the correction factors(simplified model equation(417))………………...66 5.26. Comparison of catalog v/s calculated power consumption for all points with the correction factors(simplified model equation(415))……................…67 6.1. Florida Heat Pump (GT018) catalog data……………………………………….70 6.2. ClimateMaster catalog data………………..…………………………………….72 6.3. Trane heating catalog data…………………….......…………………………….74 6.4. Trane cooling catalog data…………………….......…………………………….75 6.5. Isometric north east view of the building plan………………………………….76 6.6. Schematic of the building and system plan implemented in EnergyPlus………..78 6.7. Annual hourly loads for the example building in Chanute,IL……………...……80 6.8. Building load profile for the winter design day…………………………….……81 6.9. Building load profile for the summer design day……………………….….……81 6.10. Demands for the summer design day……………………….……………………83 6.11. Fan electric power consumption (cooling mode)………………………………...84 6.12. Cooling demands met by the heat pump……………………………..…………..85 6.13. Controlled zone temperatures (cooling mode)………………………..………….86 6.14. Demands for the winter design day……………………….…………………..…87 6.15. Fan electric power consumption (heating mode)………………………………...87 6.16. Heating demands met by the heat pump……………………………..…………..88 6.17. Controlled zone temperatures (heating mode)……………………..…………….88 6.18. Comparison of demand v/s met in the cooling mode……………………………90 x 6.19. Comparison of demand v/s met in the heat ing mode……………………………90 6.20. Comparison of duty factor v/s power consumption in the cooling mode……….91 6.21. Comparison of duty factor v/s power consumption in the heating mode……….92 6.22. Heating mode under off design conditions………………………………………93 6.23. Cooling mode under off design conditions………………………………………93 7.1. Application window of the performance coefficient calculator………...……….97 7.2. Window when the user clicks on cooling mode………...………………………..98 7.3. The main interfacing window………………………...…………………………..99 7.4. A typical model Input file…………..……………………………………...……101 7.5. A typical model definition file…………………………………………..……..102 7.6. Preprocessed interface window……………………..…………………………..103 7.7. Output window with the coefficients and comparison plots…………...…...…...105 xi LIST OF TABLES Table Page 4.1 Manufacturer’s catalog for Florida Heat pump GT030 in the cooling mode.…..26 4.2 Manufacturer’s catalog data for cooling mode…………..…………….………30 4.3 Implementation of the simple water to air heat pump model..…………………36 5.1 List of heat pumps in cooling mode for model verification……………….…... 46 5.2 List of heat pumps in heating mode for model verification……………………52 5.3. Correction factors for the air flow rate………..…………………………...……55 5.4. Correction factors for the wet bulb temperatures..………………………...……55 5.5. Range of wet bulb temperatures and air flow rates for GC series………………60 5.6. Comparison of model RMS errors for ClimateMaster HS006…………………..67 6.1. Distribution of coefficients in model equations………………………………...82 xii NOMENCLATURE PLR = Part load ratio() QE = Evaporator cooling load Btu/h (W) QENOM = Nominal evaporator cooling load Btu/h (W) PNOM = Nominal Compressor power Btu/h (W) P = Compressor power Btu/h (W) ANCR = Available to nominal capacity ratio() FFL = Fraction of full load power() FLPR = Full load Power ratio() PFL = Full load compressor power Btu/h (W) E = Energy rate Btu/h (W) f = Functional relationship P = Pressurepsia(Pa) m& = Mass flow ratelbm/h(kg/s) T = TemperatureoF(oC) X = Refrigerant quality() U = Energy flow rate Btu/h (W) R = gas constant(J/K.s) PD = Piston displacementCFM(m3/s) C = Clearance factor() dis P = Discharge pressure psia(Pa) suc P = Suction pressure psia(Pa) = Isentropic exponent() xiii hcoAo = External heat transfer coefficientBtu/ oF(J/K) a m& = Air mass flow ratelbm/h(kg/s) a Cp = Specific heat of airBtu/lbm oF(J/kgK) • s Q = Source side heat transfer rate Btu/h (W) • L Q = Load side heat transfer rate Btu/h (W) •W = Compressor power input Btu/h (W) cat W = Catalog power consumption Btu/h (W) W = Model power consumption Btu/h (W) cat QL = Catalog load side heat transfer Btu/h (W) QL = Model load side heat transfer Btu/h (W) TWiL = Entering water load side temperatureoF(oC) TWiS = Entering water Source side temperatureoF(oC) WiL m • = Entering water load side mass flow rateoF(oC) WiS m • = Entering water Source side mass flow rateoF(oC) S = Thermostatic Signal Tref = Reference Temperature(K or R) TWin = Entering water temperature (K or R) Tdb = Dry bulb air temperatures (Kor R) Twb = Wet bulb air temperatures (Kor R) Qbase = Base capacity of the heat pump unit(W) Qc = Cooling capacity (W) Qh = Heating capacity (W) A = Polynomial regression coefficient() B = Polynomial regression coefficient() xiv C = Polynomial regression coefficient() CT = Polynomial regression coefficient() LT = Polynomial regression coefficient() QT = Polynomial regression coefficient() A2 F2 = Equation fit coefficients for the heating mode() A1 J1 = Equation fit coefficients for the cooling mode() 1 CHAPTER 1 INTRODUCTION 1.1. Background Watertoair ground source heat pump systems have been an ideal choice for design engineers since they provide a promising ecofriendly alternative for heating and cooling of residential and commercial buildings. These units accept energy from or reject energy to a common ground loop depending on whether the zone has a requirement for heating or cooling. The single package reverse cycle watertoair heat pump uses ground as the heat source in the heating mode. In the cooling mode, the ground acts as the heat sink by the application of a refrigerant reversing valve. An expansion device maintains the pressure difference between the high pressure condenser side and the low pressure evaporator sides of the watertoair heat pump refrigeration system. In heating mode, the refrigerant under high pressure is directed through the refrigerant to air heat exchanger and the closed loop heat exchanger absorbs heat from the ground (typically 55F to 70F). Heat transfers to the refrigerant via the water to refrigerant heat exchanger and the condensation of the refrigerant results in heating. In the cooling mode, the high pressure refrigerant is channeled through the water to refrigerant heat exchanger connected to the ground loop and cooling is provided by the evaporation of the refrigerant in the refrigerant to air heat exchanger. With increasing energy costs, many potential heat pump applications require frequent reassessment in terms of design and modeling. Modeling heat pumps for design and simulation is significant since it allows cost effective design solutions and permits the designer to quantitatively compare a variety of design strategies. Also, it 2 facilitates modifications at any stage in the design process before the final documents are produced. 1.2.Thesis objective and scope The objective of the thesis is to develop a simplified model that can simulate a watertoair ground source heat pump and implement the model in EnergyPlus. EnergyPlus (Crawley et al; 1997) is a new energy analysis and thermal simulation engine capable of performing subhourly simulation of the building, fan system and the ground source heat pump system. It is a highly modularized, platform independent engine. It is developer friendly and is based on the best features of BLAST (BSO, 1991) and DOE2 (LBNL, 1980). The steady state behavior and performance characteristics of the water loop heat pump system developed by Lash (1990) for the BLAST program have been investigated. The model which includes a ground loop configuration is significantly modified for implementation in the EnergyPlus program. Also, the model is extended to calculate sensible and latent capacity splits for the heat pump while operating in the cooling mode. The performance of the simplified model is compared with the parameter estimation watertoair heat pump model (Jin 1999) to determine the usefulness of implementing a simple model. The simplified and the detailed models are compared in a case study and the results are summarized and analyzed. The performance data generated using the model is compared with the manufacturer’s catalog data to check the validity of the simulation. A visual basic graphical user interface that generates performance coefficients from manufacture’s data was also developed. 3 CHAPTER 2 LITERATURE REVIEW One of the biggest challenges facing the simulation and design of performance oriented ground source heat pump systems is the degree of complexity involved in modeling the individual components of the system. The challenge also impinges on the successful implementation of the model in a computer program. Several heat pump models have been implemented in the past. However a detailed review of the literature uncovers a few limitations in the existing models. 2.1.Equation fit watertoair heat pump and chiller models 2.1.1. Lash model Lash(1990) developed a model for a water loop heat pump system(WLHPS) in which a network of zoned reversible packaged water source heat pumps operate independently of each other to control individual zone loads. These units are capable of supplying both heating and cooling. Fig 2.1 is an illustration of the simplified loop model used by Lash. The water loop is modeled by breaking up the loop into two sections or nodes where node1 represents the water mass between the central plant outlet and the first heat pump inlet and node2 represents the water mass between the first heat pump inlet to the central plant inlet. The Lash water loop heat pump model as implemented in the BLAST energy analysis program uses four nondimensional performance equations to describe the heat pump as discussed in chapter 4. 4 Figure 2.1. Heat pump system nodes (Lash 1990) The Heat pump performance is based on entering air temperatures, entering water temperatures and the inlet mass flow rate of water. Coefficients for the nondimensional equations are obtained by a least square fit of manufacturer’s data to the form of the equation. The biggest advantage of the model is that the data is readily available. Also, since refrigeration properties are not required the model is very robust, resulting in quick execution time. However the model is not applicable beyond a particular data range and it also fails to account for the latent and sensible capacity splits in the cooling mode. 5 2.1.2. Allen and Hamilton model Allen and Hamilton (1983) developed a steady state reciprocating compressor water chiller model. Fig 2.2 shows a schematic of the water chiller with the basic pieces of equipment and the variables labeled at appropriate points. MC QC QE ME P TC1 TC2 TE1 TE2 TD TS Evaporator Condenser Expansion Compressor Valve QL Figure 2.2. Water chiller system schematic The equation fit model is capable of simulating the energy rate characteristics of the system under full load and part load conditions. The evaporator heat transfer rate and the compressor power are expressed in terms of a polynomial which is a function of the evaporator and condenser exit temperatures. The coefficients are determined from experimental data by polynomial regression. Given a water chiller, an evaporator inlet water temperature, TE1, an evaporator waterside mass flow rate, ME, a condenser inlet water temperature, TC1, and a condenser waterside mass flow rate, Mc, then the five system equations can be given as follows. The evaporator heat transfer rate is given by 6 ( ) 2 1 Q m C TE TE E = & E p ……………………………………………..(2.1) 6 2 5 2 2 1 2 2 2 3 2 2 4 2 QE b T E b T C b T E T C b T E b T C b = + + + + + ………..……. (2.2) And the compressor power is 12 2 11 2 2 7 2 8 2 9 2 2 10 2 P b T E b T C b T E T C b T E b T C b = + + + + + ……….…….(2.3) Also, the condenser heating rejection rate is Q Q P C E = + …………………………………………….………...(2.4) ( ) C c p C2 C1 Q =m& C T T ……………………………………………..(2.5) Where b1b12 = Coefficients fitted by polynomial regression TE1,TE2, = Evaporator water entering and leaving temperature TC1,TC2 = Condenser water entering and leaving temperature Ts, Td = Refrigerant temperature at compression suction and discharge Cp = Specific heat at constant pressure P = Compressor power QE = Evaporator heat transfer rate QC = Condenser heat transfer rate E m& = Evaporator mass flow rate C m& = Condenser mass flow rate Although the model does not include the complexity of modeling individual components it is not a useful model due to the fact that its two main biquadratic equations are based on condenser and evaporator outlet temperatures which are not readily available from catalog data. In addition to this, the model neglects the power losses QL, shown in Figure 2.2, assuming well designed systems available from the major manufacturers. 7 2.1.3. DOE2 Model The water chiller model used in the DOE2 computer simulation program (DOE2 Engineers Manual, 2002) is the simplest model with a minimum number of performance coefficients. The DOE2 model eliminates the dependency of water chiller performance on condenser and evaporator temperatures. Here, the power consumption is fit as a quadratic function of the evaporator heat transfer rate, QE. The part load ratio is given as ENOM E Q Q PLR = ………………………………...……………..(2.6) C L PLR Q PLR2 P P T T T Nom = + + …………................................(2.7) Where PLR = Part load ratio QE = Evaporator heat transfer rate QENOM = Nominal evaporator heat transfer rate PNOM = Nominal Compressor power CT, LT, QT = Polynomial regression coefficients P = Compressor power Although the model exhibits simplicity with only two equations and four constants, it does not account for the effect of temperature variations on performance. This results in a large predictive error. 2.1.4. BLAST Model The BLAST chiller model (BLAST Users Manual, 1991) slightly improves on the DOE2 model. The model was actually developed to counter the demerits of the DOE2 model. This model accounts for variations in performance due to condenser 8 and evaporator temperatures. The model is based on the observation that under full load, the constant evaporator heat transfer rate lines are approximately linear and parallel. It also introduces a variable known as the equivalent temperature difference, T, which is an expression involving the user input condenser and evaporator exit temperatures, TC2 and TE2, and the slope, k, of the constant evaporator heat transfer rate at full load performance. The available capacity to nominal capacity ratio, ANCR, is modeled as a quadratic function of T. The water chiller power PFL is modeled as a function of ANCR. ( ) ( ) 2 2 2 2 E E NOM C C NOM T T k T T T = ……………………………………..(2.8) b1 bb2. TT 3. 2 Q Q ANCR ENOM EFL = = + + …………………………….…….(2.9) (c1 c2.ANCR c3.ANCR2 ) Q P Q PFL FLPR ENOM NOM EFL + + = = …………….……(2.10) EFL E Q Q PLR = ……………………………………………………….……...(2.11) a1 a2.PLR a3.PLR2 PFL P FFL = = + + …………………………….……..(2.12) P = RatedCOP ANCR Q FLPR FFL ENOM * * * ………………………………………...(2.13) Where a1a3, b1b3, c1c3 = Polynomial regression coefficients ANCR = Available capacity to nominal capacity ratio FFL = Fraction of full load power FLPR = Full load Power ratio PFL = Full load compressor power PLR = Part load ratio 9 QEFL = Full load evaporator heat transfer rate PLR = Part load ratio QE = Evaporator heat transfer rate QENOM = Nominal evaporator heat transfer rate PNOM = Nominal Compressor power P = Compressor power 2.1.5. Hamilton and Miller model The Hamilton and Miller model (1990) is a typical detailed equation fit model. The steady state model incorporates functional fits of manufacturers’ catalog performance data of various individual standard components including evaporators, compressors, condensers, capillary tubes and fans. The system model shown in Fig 2.3 is a combination of mathematical models of individual components with mass and energy flow at each component connection and conformity of pressure/temperature values at each component connection. The equations expressing the behavior and operation of each component are based on assumed steady state conditions. The components are connected such that the input to one component is the output of the previous component and the values of the state variables are updated by each component model. Each node is primarily characterized by the pressure and temperature of the refrigerant. Together the equations form a set of nonlinear simultaneous equations depicting the response of the vapor compression system to fan inlet conditions. The computer model is valuable for simulating the response of air conditioning systems for a range of ambient and inside conditions. Although the model provides the flexibility to specify various coils, compressors and capillary tubes, it is important to notice that comprehensive individual component performance data is not readily available. 10 P10 T10db T10wb U10 10 9 P8 T8db T8wb U8 8 P9 T9db T9wb U9 Condenser Condenser fan 1 Compressor qc Wcd Wcp P1 T1 U1 4 Evaporator qe Evaporator fan P7 T7db T7wb U7 7 mw7 P6 T6db T6wb U6 6 mw6 P5 T5db T5wb U5 mw5 X4 mr4 T4 U4 P3 T3 U3 3 mr3 P2 2 T2 U2 mr2 Capillary tube 5 Figure 2.3. Schematic of an air conditioning system showing connecting points between components 2.1.6. Parent and Larue development of the Domanski Model Parent and Larue(1989) developed a steady state watertoair heat pump modeling program called SIMPAC which uses Domanski’s (1986) model of an airtoair heat pump as the starting point. The program involves a driver program that links the independent models of each system component together. Thermodynamic properties of pure refrigerants and zeotropic mixtures are evaluated by the application of Carnahan Starling Desantis (CSD) equation of state. The SIMPAC program requires detailed input for the individual components of the heat pump. The SIMPAC algorithm is computationally intensive and may not converge. 11 2.2. Jin and Spitler’s parameter estimation watertoair heat pump models 2.2.1 Overview Jin and Spitler (2002) proposed a steady state simulation model for watertowater and watertoair heat pumps. The model uses a multivariable unconstrained optimization algorithm to estimate a number of parameters. The aim of the model is to determine the geometric parameters and operation of each component and replicate the performance of the actual unit in operation. The model algorithm and the equations are shown in Fig 2.4. Where TWiL = Entering water Load side temperature TWiS = Entering water Source side temperature WiL m • = Entering water Load side mass flow rate WiS m • = Entering water Source side mass flow rate Tc = Condenser temperature Te = Evaporator temperature S = Thermostatic Signal mwl = Load Mass flow rate mws = Source Mass flow rate QL = Load side Heat transfer rate QS = Source side Heat transfer rate The parameters included in the model are shown in Figure 2.5 where: PD = Piston displacement C = Clearance Factor 12 P = Pressure drop across the suction and discharge valves = Loss factor Tsh = Superheat in C or F loss W = Constant part of the electromechanical losses (UA)S = Source side heat transfer coefficient (UA)L = Load side heat transfer coefficient (hco,Ao) = External heat transfer coefficient[Cooling mode only] 13 Parameters Inputs Enter the initial guesses for 1)Heat transfer rate in condenser 2)Heat transfer rate in evaporator Effectiveness for evaporator and condenser = pw wL L L C m UA 1 exp = pw wS s s C m UA 1 exp Temperatures for evaporator and condenser L pw wL L e wiL C m Q T T • = S pw wS S c wiS C m Q T T • = + Is Evaporator pressure < Low pressure cutoff Condenser pressure > High pressure cutoff End yes no Identify Condenser and evaporator exit points Apply suction and discharge drops P P P suc = e P P P dis c = + Is Suction pressure < Low pressure cutoff Discharge pressure > High pressure cutoff End yes no Find refrigerant mass flow rate 1 (1 ( ) suc dis suc P P C C PD m = + • Find Power consumption Cooling capacity Heat rejection Is End yes Abs QS QS QS err Abs QL QL QL err guess guess guess guess < < ( / ( / NO Figure 2.4. Flow diagram for model implementation computer program 14 Parameter estimation based Watertoair Heat Pump Tdbil Twbil Vil Twis mwiS S Tdbol Twbol Vol Twos mwos W F (UA)s (UA)l PD C P Tsh Wloss Phi Plo (UA)s (UA)l PD C P Tsh Wloss Phi Plo hcoAo Inputs Outputs Heating Mode Parameters Cooling Mode Parameters Figure 2.5. Information flowchart for model implementation (Jin 2002) 2.2.2. Parameter estimation procedure A set of parameters for the cooling mode are defined on the basis of the equations used in the model. An information flowchart indicating the parameters, inputs to the model and the resulting outputs are shown in Fig 2.5. The estimation of parameters is conducted using the catalog data. The parameter estimation procedure incorporates an objective function which computes the difference between the model outputs and the catalog outputs. The objective function is then minimized by using a multivariable, unconstrained, multimodal, NelderMead optimization algorithm. The inputs to the model include the entering water temperatures and mass flow rates on the load side 15 and the source side. The sum of the square of the errors (SSQE) for a given set of parameter values which will be minimized is given by 2 1 2 + = = cat cat i i cat cat i QL QL QL W W W SSQE ………………………..(2.23) Where cat W = Catalog power consumption Btu/h (W) W = Model power consumption Btu/h (W) cat QL = Catalog load side heat transfer Btu/h (W) QL = Model load side heat transfer Btu/h (W) i = Number of points 2.2.3. Model implementation A thermostat signal is used as an input parameter to tell the model which set of parameters (heating mode or cooling mode) should be used. Also, the objective function evaluation takes advantage of the fact that the heat transfer rates are known, using the catalog data as an initial guess, then minimizing the difference between the calculated and catalog heat transfer rates. However, for the model implementation, the heat transfer rates are obtained by simultaneous solution with successive substitution. An information flow chart of the model implementation is presented in Figure 2.5. Extrapolation beyond the catalog data grants the parameter estimation model an upper hand in comparison with the equation fit models. However, the model although advantageous, carries a high overhead cost associated with repeated calls to refrigerant data routines. This makes the process computationally more intensive and time consuming. In addition, refrigerant properties may go out of bounds while the model attempts to converge on the final solution. Running the model successfully in a 16 general simulation environment requires considerable effort to hold the refrigerant properties within bounds for every iteration of the simulation. Finally the convergence on a valid set of parameters is largely dependent on the initial guesses. This places an undue burden on users who typically have little knowledge of appropriate ranges for the model parameters. 17 CHAPTER 3 OVERVIEW OF ENERGYPLUS 3.1.Introduction EnergyPlus (Crawley et al; 1997) is a new energy analysis and thermal simulation engine developed in Fortran 90. It is capable of modeling HVAC systems, central plants, ideal controls and building heat transfer. It is a highly modularized, platform independent engine that was originally based on the best features of BLAST (BSO1991) and DOE2 (LBNL1980). Program features include variable time step, user configurable modular systems and an integrated system / zone simulation. Recently, a number of components have been developed and implemented in the program to support ground source heat pump analysis. Rees(2002) implemented the shallow pond model developed by Chiasson(1999). Murugappan(2002) implemented the vertical ground loop heat exchanger model developed by Yavuzturk and Spitler(1999). Murugappan(2002) also implemented the watertowater heat pump model developed by Jin and Spitler(2002). Most recently, the watertoair heat pump model developed by Jin and Spitler has been implemented in EnergyPlus. 3.2.Simulation environment Figure 3.1 shows the overall hierarchy of EnergyPlus. On the whole, the simulation environment is based on fundamental heat balance principles and is a simultaneous solution of the coupled building, system and the plant simulations. 18 Input Building Description Simulation Manager Heat Balance Manager Surface Heat Balance Manager Air Heat Balance Manager Input Building Use Information Input Building Thermostatic control Information Input HVAC Operation and Central plant schemes HVAC Manager Air loop simulation Plant loop simulation Zone equipment simulation Convergence Condenser Loop simulation HVAC Iteration No Figure 3.1. Overall hierarchy of EnergyPlus 19 Figure 3.2. EnergyPlus nodal connections (EnergyPlus guide for module developers (EnergyPlus2002) The HVAC simulation in EnergyPlus is loop based (Fisher, 1999). Equipment such as boilers, chillers, thermal storage tanks and watertowater heat pumps are simulated on a “Plant loop”. Environmental heat exchangers such as cooling towers, ground loop heat exchangers, pavement heat exchangers and ponds are simulated on a “Condenser loop”. Heating coils, cooling coils as well as unitary equipment such as watertoair heat pumps and airtoair heat pumps are simulated on an “Air loop”. The components are connected to the loops by defining nodes at the connections as shown in Fig 3.2 .The nodes are in turn defined in the FORTRAN program as data structures which hold state variable and control information for the node location on the loop. 20 3.3.Water source heat pumps in EnergyPlus 3.3.1. Watertowater heat pump simulation Fig 3.3 shows the HVAC loop connections for a watertowater heat pump simulation in EnergyPlus. The watertowater heat pump is coupled both to the plant loop and the condenser loop as shown in the Figure. Zone Water to Water Heat Pump Zone/AirLoop Condenser Loop Ground loop Heat exchanger Condenser demand side Condenser supply side Plant Loop Plant demand side Plant supply side Figure 3.3. HVAC loop connections for a watertowater heat pump simulation Plant Loop The plant loop is divided into two sections, so that it can be simulated using a successive substitution solver. The demand side couples coils and other components to the zone/air loop, and the supply side models energy conversion equipment such as 21 boilers, chillers and heat pumps. The plant supply side supplies hot or cold water to meet the demands of the plant demand side and controls the flow rate and the temperature of the loop. 3.3.2. Watertoair heat pump simulation Fig 3.4 shows the HVAC loop connections for a watertoair heat pump simulation in EnergyPlus. The watertoair heat pump is coupled directly to the condenser loop. The system consists of two loops, the Zone/Air loop and the Condenser loop. Zone Water to Air Heat Pump Zone/AirLoop Condenser Loop Ground loop Heat exchanger Condenser demand side Condenser supply side Figure 3.4. HVAC loop connections for a watertoair heat pump simulation Zone/Air loop The watertoair heat pump model is called from the zone air loop manager. The zone thermostat turns the heat pump on or off depending on the sensible demand for that zone. The most recent source side inlet temperature is taken from the condenser loop and the water flow request based on the air loop simulation is passed as a demand to the condenser loop. Condenser Loop 22 The condenser loop is divided into two sections, the demand side where energy is transferred to the air stream by various components in the zone/air loop and the supply side where energy is transferred to the environment by various components. The loop attempts to meet the flow request made by the heat pump. The condenser loop temperature is determined by the ground loop heat exchanger simulation. The overall HVAC simulation manager calls the zone/air loop and the condenser loop successively until convergence is achieved. 3.4.Implementing Watertoair heat pump models in EnergyPlus In EnergyPlus, the unitary watertoair heat pump is a virtual component that consists of a single speed fan, a cooling coil, a heating coil and a gas or electric supplementary heating coil as shown in Figure 3.5. Fan Auxiliary Cooling Coil Heating Coil Heating Coil Figure 3.5. Schematic of Blow thru Watertoair heat pump virtual component The heat pump components are accessed with a single call to the unitary equipment manager from the air loop manager. Air properties are evaluated at nodes between each of the heat pump components. The outlet node of one model forms the inlet node to the next model within the virtual component. The virtual component is connected to the air loop as specified in the user generated input file. This means that the inlet node for the heat pump is also the inlet node for the fan component. In the draw thru configuration, the fan will be located between the coils and the supplemental heating coils. Although it is convenient to cycle the heat pump at the system time step, 23 typically, EnergyPlus time steps will be too long to avoid overheating or overcooling the zone using this control scheme. EnergyPlus, therefore, approximates cycling as a part load by adjusting the system air flow rate to meet the demand of the controlling (thermostat) zone. The fraction of total system volumetric airflow that goes to the controlling zone along with the controlling zone load determines the total load that must be met by the heat pump as: ControlZoneAirFlowFraction ControlZoneHeatingLoad HeatPumpHeatingLoad = …………………….…(3.1) ControlZoneAirFlowFraction ControlZoneCoolingLoad HeatPumpCoolingLoad = …………………..…...(3.2) The “part load” system flow rate is computed on the basis of the heat pump heating or cooling load. The run time fraction of the heat pump in its heating/cooling mode is estimated by the ratio of the sensible heating/cooling demands to the base heating/cooling capacities. The base capacities can be determined from the manufacturers’ catalog data. The equation fit performance model discussed in Chapter 4 is used to determine performance variables such as the heating/cooling capacities, power consumption, energy efficiency ratio and the coefficient of performance. The actual energy extracted or provided is then calculated as the product of the equation fit performance variables for energy extracted or provided and the run time fraction for the respective modes. The power consumption is computed in the same manner. The output data is then moved from the heat pump data structures to the node data structure and are readily available to other computational and reporting modules. 24 CHAPTER 4 MODEL DEVELOPMENT AND IMPLEMENTATION 4.1. Development of the watertoair heat pump model The model implemented in EnergyPlus is based on the water loop heat pump system model developed by Lash (1990). This section describes the development of the model equations and how they were incorporated in the EnergyPlus simulation engine. 4.2. Design basis Water source heat pumps are required to meet the standards of ARI or ISO 132561. The certification program rates water source heat pump performance at specified entering water temperatures. The standard simplifies the use of rating data for heat pump performance modeling in energy analysis calculations and allows for direct rating comparisons across applications. High efficiency water source heat pumps are designed to operate over a range of entering water temperatures in either the heating or the cooling mode. The manufacturer’s catalog provides heat pump performance data within a specific range of entering fluid temperatures. The catalog provides sufficient data to develop a correlation for heat pump performance as a function of entering water temperature, entering air temperatures and inlet water mass flow rates. The catalog data were used to fit the coefficients for the watertoair heat pump described later in this chapter. A typical set of manufacturer’s data for the Florida heat pump in the heating and cooling mode is shown in Figure 4.1. 25 FHP GT030 Data All performance at 1000 CFM and 7.5 GPM Entering Ent. Air Total H e a t Sensible Capacity BTUH EER Fluid Wet Bulb Capacity Watts ERnet.j e c t i o n Air Dry Bulb Temp. Temp. Temp. BTUH Input BTUH 75o 80o 85o 50 61 29514 1209 33639 22460 26946 29514 24.4 50 64 30944 1236 35162 21011 26674 29583 25 50 67 32399 1263 36709 19342 25271 29159 25.7 50 70 33878 1290 38280 16126 22292 29000 26.3 50 73 35381 1318 39877  19000 26041 26.9 60 61 28380 1360 33022 21597 25911 28380 20.9 60 64 29756 1391 34501 20204 25650 28447 21.4 60 67 31155 1421 36003 18599 24301 28039 21.9 60 70 32577 1452 37530 15507 21436 27886 22.4 60 73 34022 1482 39080  18270 25041 23 70 61 27246 1512 32404 20735 24876 27246 18 70 64 28567 1545 33840 19397 24625 27310 18.5 70 67 29910 1579 35298 17857 23330 26919 18.9 70 70 31276 1613 36779 14887 20580 26772 19.4 70 73 32664 1647 38284  17540 24040 19.8 85 61 25546 1739 31478 19441 23324 25546 14.7 85 64 26785 1777 32848 18187 23088 25606 15.1 85 67 28044 1816 34240 16742 21874 25239 15.4 85 70 29324 1855 35654 13958 19295 25101 15.8 85 73 30625 1894 37089  16446 22540 16.2 100 61 23846 1966 30552 18147 21771 23846 12.1 100 64 25002 2009 31857 16976 21552 23902 12.4 100 67 26177 2053 33182 15628 20418 23560 12.8 100 70 27372 2097 34528 13029 18011 23431 13.1 100 73 28587 2142 35894  15351 21040 13.3 Figure 4.1. Manufacturer’s catalog for Florida Heat Pump GT030 Using manufacturer’s catalog data in the model equations requires conversion since the units vary for the input parameters. For example, in Figure 4.1 British IP units are used for all reported parameters except the power input which is in SI units. The catalog data must be converted to the units required by the model equations—SI units in the case of the EnergyPlus model. The converted catalog data for the Florida heat pump GT030 in the cooling mode is shown in Table 4.1. 26 Table 41.Manufacturer’s catalog for Florida Heat Pump GT030 in the cooling mode Since it is extremely cumbersome to obtain data at every single point within the range specified, manufacturers often assume rated flow rates or temperatures for convenience. Different manufacturers assume different input variables as rated constants. For example, the Florida heat pump catalog assumes rated air flow rates and water flow rates, whereas the ClimateMaster heat pump catalog creates the set of catalog data at constant wet bulb temperatures over a range of water flow rates. The heat pump model was developed to accommodate both data formats. In Table 4.1 All performance data is obtained at a constant air volumetric flow rate of 0.471 m3/s and a constant water mass flow rate of 0.47 kg/s. A detailed explanation of the methodical approach in using manufacturer’s data along with a list of different heat pump manufacturers is provided in Chapter 5. Following Lash, Heat pump performance based on entering air temperatures, entering water temperatures and inlet water mass flow rate were developed as follows: 27 4.2.1. Cooling Mode Equations + = + wbase w wb ref ref Win base c m m T T C1 T T A1 B1 Q Q & & ………………………...(41) + = + wbase w wb ref ref Win base m m T T F1 T T D1 E1 EER EER & & ..…..……...……….….(42) + + = + wbase w db ref wb w wbase ref ref Win Sensbase Sens m m T T J1 T m m T I1 T T G1 H1 Q Q & & & & … (43) Ql c Sens Q =Q …………………………………………….……………(44) Where: A1 J1 = Equation fit coefficients for the cooling mode Tref = 283K or 511 R TWin = Entering water temperature (K or R) w m • = Mass flow rate of water through the heat pump w base m • = Base mass flow rate of water through the heat pump Tdb,Twb = Entering Dry bulb and wet bulb air temperatures (K or R) Qbase = Base capacity of the heat pump unit(W) Qc = Cooling capacity (W) Ql = Latent cooling capacity (W) Qsens = Sensible cooling capacity (W) In cooling mode the heat pump rejects heat to the ground loop. The power input and the heat rejected by the heat pumps are functions of the entering water temperature, the water mass flow rate and the entering air temperature. The power input to the heat 28 pump is computed from the energy efficiency ratio(EER), which is defined as the ratio of net cooling capacity to the total input rate of electric energy, as follows: EER Q P = c …………………………………………(49) The amount of heat rejected to the loop is given by Equation(410) EER Q Q Q c Reject c= + ………………………………..(410) Equations 4.1 4.3 take into account critical heat pump performance parameters and are cast in a form that accommodates both variable wet bulb and variable water flow rate data. By defining a reference temperature and a ‘base case’, the equations are cast in nondimensional form. For the FHP cooling mode catalog data shown in Figure 4.1, the sensible capacity is a function of wet bulb temperatures and the dry bulb temperatures while the total capacity is a function of the wet bulb temperature only. On a psychrometric chart, the lines of constant air enthalpy follow almost exactly the lines of constant wet bulb temperature. Therefore wet bulb temperature variation, which can be easily measured, provides a relatively accurate measurement of the enthalpy difference: h WB …………………………………….(4.5) The air (‘load side’) coil heat transfer rate is directly proportional to this change in enthalpy. The inlet air wet bulb temperature is therefore an appropriate scale for the air side heat transfer rate in the total capacity equation (41). For a given heat pump (specified coil geometry, compressor and expansion device), the air and water inlet conditions as shown in Fig 42, completely determine the total operating capacity of the unit. The inlet water temperature is therefore an appropriate scale for the source side heat transfer rate. 29 Twin mW Twout Twbin Tdbin Twbout Tdbout Air (‘Load’) Side Water (‘Source’) Side Figure 4.2. Water to air heat pump cycle (cooling mode) When the load increases, the entering wet bulb temperature increases. If the heat pump has to meet this increase in load, with a constant source side water mass flow rate, the water temperature has to be reduced. This establishes an inverse relationship between the entering water temperature and the entering wet bulb temperature as shown in equation 4.6. wb win T T 1 …………………………………………………….(4.6) The C1 term in equation (41) shows the expected relationship between the water mass flow rate and the entering wet bulb temperature as illustrated in Table 42. As the mass flow rate decreases the total cooling capacity and the sensible cooling capacity also decrease. This establishes a direct relationship between the heat pump capacities and the water mass flow rate as shown by equation 4.7. TC m& w ………………………………….…..(4.7) 30 Table 42.Manufacturer’s catalog data for cooling mode 0.9 GPM 1.1 GPM 1.7 GPM EWB EWT TC SC KW HR TC SC KW HR TC SC KW HR 60 60 6.52 5.42 0.46 9 6.69 5.42 0.45 9.1 6.86 5.52 0.43 9.2 65 60 7.33 4.47 0.47 9.1 7.52 4.47 0.46 9.2 7.71 4.56 0.43 9.3 66.2 60 7.49 4.19 0.47 9.2 7.69 4.19 0.46 9.3 7.89 4.27 0.44 9.4 67 60 7.60 4.00 0.48 9.3 7.80 4.00 0.47 9.4 8.00 5.20 0.44 9.5 70 60 7.97 3.21 0.49 9.4 8.18 3.21 0.48 9.5 8.39 3.28 0.45 9.6 Where: EWB= Entering wet bulb temperature (oF) EWT= Entering water temperature (oF) TC = Total cooling capacity (kBtu/hr) SC = Sensible cooling capacity (kBtu/hr) KW= Kilowatts input HR = Heat rejected to water (kBtu/hr) Table 42 also shows that as the mass flow rate decreases the sensible cooling capacity decreases. This establishes a direct relationship between the heat pump capacities and the water mass flow rates as shown by equation 4.8. SC m& w ……………………………………...(4.8) Since most data sets show either a range of entering wet bulb temperatures or a range of mass flow rates, but not both, the C1 term may introduce significant error in simulation applications where either the water mass flow rate or the entering wet bulb temperature will vary beyond the range of the single point shown in the catalog data. This concern is discussed in detail in Chapter 5. 31 4.2.2. Heating Mode Equations The Heating mode equations are analogous to the cooling mode equations. The dry bulb temperature is used in place of the wet bulb temperature and the COP is used in place of the EER as shown in equation (411) through (413). + = + wbase w db ref ref Win base h m m T T C2 T T A2 B2 Q Q & & ………………………...(411) + = + wbase w db ref ref Win base m m T T F2 T T D2 E2 COP COP & & ..…..……...……….….(412) The amount of heat transferred to the ground loop is given by: COP Q Q Q h absorb= h ……………………………………….(413) The power consumption is given by equation (414) COP Q P = h …………………………………………………(414) Where: A2 F2 = Equation fit coefficients for the heating mode Tref = 283K or 511 R TWin = Entering water temperature (K or R) •m = The mass flow rate of water through the heat pump Tdb,Twb = The dry bulb and wet bulb air temperatures (K or R) Qbase = The base capacity of the heat pump unit(W) Qh = Heating capacity (W) The base capacity is typically selected as the maximum capacity at which the heat pump unit can operate. Hence it can be sorted easily by looking at the peak values of 32 the capacity in the manufacturer’s catalog. The base mass flow rate is the water flow rate at the maximum capacity. Similarly, the base EER and COP are the respective values at the maximum capacity. 4.2.3. Accommodating Variable air flow rates Manufacturers rate the heat pump assuming a “constant volume” fan which means that the fan runs at a constant rpm and delivers a relatively constant volumetric flow rate over a range of air temperatures for a given duct configuration. For rating purposes, the manufacturers assume a zero pressure drop across the fan which is an approximated minimum for an independent water source unit without any ductwork. However, if the heat pump is simulated with rated air flow, predicted performance of the heat pump will be high. With no measured data readily available, air flow correction factors provided by the manufacturer are the only means of estimating the effect of offdesign air flow rates. These correction factors may be used to generate extra catalog points as discussed in the next chapter. In order to investigate the sensitivity of the performance variables at variable air flow rates, equations 4.14.3 are proposed and implemented in the modified form by including the air mass flow rates as follows: Cooling Mode: + = + wbase w wb ref ref Win base c m m T T C1 T T A1 B1 Q Q & & & & a a base m m ………………..(415) + = + wbase w wb ref ref Win base m m T T F1 T T D1 E1 EER EER & & & & a a base m m ..…..……...…. .(416) + + = + wbase w db ref wb w wbase ref ref Win Sensbase Sens m m T T J1 T m m T I1 T T G1 H1 Q Q & & & & & & a a base m m ……...(417) 33 Heating Mode: + = + wbase w db ref ref Win base h m m T T C1 T T A1 B1 Q Q & & & & a a base m m ………………………..(418) + = + wbase w db ref ref Win base m m T T F1 T T D1 E1 COP COP & & & & a a base m m ..…..……...……… .(419) Since only a few data points are available to verify this form of the model, it is proposed for future consideration, but was not implemented in EnergyPlus. 4.2.4. Determining the performance Ccefficients Performance coefficients in the heating and cooling modes for the equation fit model are determined by implementing a generalized least square equation fitting method. The method uses a minimal sum of the deviations squared from a given set of data. The performance coefficients A1J1 and A2F2 in equations (41) (42) (43) (411) and (412) are generated by this method. The coefficients are generated by using the available catalog data for the heating mode and the cooling mode. Since watertoair heat pump systems seldom operate at the catalog specified water temperature, a correlation for the heat pump performance as a function of the entering water temperature, wet bulb and dry bulb air temperature and mass flow rate must be derived from manufacturer’s data as discussed in the previous section. An information flowchart showing all the input parameters is shown in Figure 4.3. 34 Entering Air Temperature (K or R) Entering Water Temperature (K or R) Reference Temperature (K or R) Base Mass flow rate (kg/s) Entering Water Mass flow rate (kg/s) Cooling Mode Heating Mode Base Cooling Capacity (W) Base Heating Capacity (W) A1 B1 C1 D1 E1 F1 Base Energy Efficiency Ratio Base Coefficient of Performance A2 B2 C2 D2 E2 F2 COP Performance Coefficients Capacity Performance Coefficients Simple Water to Air Heat Pump Model Capacity Performance Coefficients EER Performance Coefficients Total Cooling Capacity(W) Total Heating Capacity(W) Power Consumption(W) Exit Water Temperature (K or R) Exit Air Temperature (K or R) Inputs Outputs Figure 4.3. Information flow chart 4.2.5. Performance coefficients calculation procedure For each unit at the specified operating conditions, the following catalog and input data is needed: • Sensible cooling capacity and Latent cooling capacity (Cooling Mode) • Heating capacity (Heating mode) • Energy efficiency ratio • Coefficient of performance • Nominal capacity of the heat pump unit • Nominal water mass flow rate • Inlet water mass flow rate • Entering air wet bulb and dry bulb temperature 35 • Entering water temperature • Nominal coefficient of performance of the heat pump unit • Nominal energy efficiency ratio of the heat pump unit Once the data at each operating point is obtained, they are input into a subroutine performing the LU decomposition of the n data points in the matrix. A procedure for decomposing a N x N matrix formed from n data points into a product of lower triangular matrix L and upper triangular matrix U is called the LU decomposition. The upper triangular matrix results from reducing the original matrix by way of elementary row operations excluding row interchange. The lower triangular matrix is created by storing a representative of each elementary row operation .The standard procedure for solving X for the linear system AX=B is shown in Appendix A. 4.3. Model implementation The model is implemented using the equations (41) and (44) for the cooling mode and (411) and (412) for the heating mode. The input/output reference specified in the EnergyPlus guide for module developers (2002) lists guidelines for new module implementation. The watertoair heat pump model was implemented as a single module with both heating and cooling subroutines. The single module approach with cooling/heating mode switching within the module makes the execution computationally less intensive than the detailed model. The zone sensible demand tells the model which set of coefficients (heating mode or cooling mode) and which input parameters must be used. The state variable information is read by the model from the loop nodes Table 43 shows the modifications made in accordance with the EnergyPlus standards to implement the watertoair heat pump simulation. EnergyPlus psychometric property routines were used to calculate the enthalpy, humidity ratios etc. 36 Table 43 Implementation of the simple watertoair heat pump model Source file/Program Modifications Purpose IDF IDD Added 2 Keywords UNITARYSYSTEM:HEATPUMP:SIMPLE COIL:WATERTOAIRHP:SIMPLE Defines the inputs for the simple watertoair heat pump model. Added 1 Module WATERTOAIRHPSIMPLE Encapsulates the data and algorithms required to manage the watertoair heat pump component 5 Subroutines SimsimpleWatertoAirHP Main calling subroutine at the top of the module hierarchy GetSimpleWatertoAirHPInput Obtains input data for the heat pump and stores it in heat pump data structures InitSimpleWatertoAirHP Initializes the watertoair heat pump components CalcSimpleWatertoAirHP Simulates the cooling and the heating mode of the watertoair heat pump EnergyPlus UpdateSimpleWatertoAirHP Updates the watertoair heat pump outlet nodes 4.3.1. Input configuration Energyplus is characterized by two formats of the input specification file, the input data dictionary file (IDD) and the input data file (IDF). The IDD defines a list of all possible EnergyPlus objects (‘keywords’). The IDD has an ‘.idd’ extension. The IDF provides the user description of a specific building and its underlying HVAC system components. The IDF has an ‘.idf’ extension. 37 4.3.2. Input specification The keywords may appear in the IDF in any order, but the data under each keyword must follow the format specified in the IDD. Each data value in the IDF must go hand in hand with the keyword fields of the IDD. This means that the type of data (Alpha or Numeric) and the order of the data must match the specifications of the IDD. Figure 4.4 provides a pictorial view of the correspondence required between the IDD and the IDF. IDD File Object Keyword Alphabetical Field Definition ……………….. ……………….. Numerical Field Definition Example FAN:SIMPLE:ONOFF Fan Name Available Schedule Fan Total Efficiency Delta Pressure IDF File Object Keyword Alphabetical Value ……………….. ……………….. Numerical Value Example FAN:SIMPLE:ONOFF, Supply Fan 1 FanAndCoilAvailSched 0.7 300.0 One to One correspondence Figure 4.4. Correspondence required between the IDD and the IDF Accordingly, two new objects UNITARYSYSTEM:HEATPUMP:SIMPLE and COIL:WATERTOAIRHP:SIMPLE are created for the model of the watertoair heat pump as shown in Figure 4.5 and 4.6. The first 15 fields in UNITARYSYSTEM:HEATPUMP:SIMPLE (A1A15) are alphabetic strings representing the heat pump name, schedule, air nodes, control zone etc. In Figure 4.6 38 the numeric fields (N1N18) indicate various numeric inputs that the coil requires to meet the zone demands. N7N18 are the performance coefficients of the model equations discussed in Section 4.1. The coefficients are generated using the performance coefficient calculator described in detail in chapter 7. Some of the fields carry optional values in them such as the Fan placement which can be a blowthrough or a drawthrough configuration. The inputs in the Fortran environment are handled by the EnergyPlus Input processor. The Input processor reads the IDD and IDF and supplies the simulation routines with the data contained in the input files. During the course of processing the inputs, the input processor validates keyword formats and node connections. 39 UNITARYSYSTEM:HEATPUMP:SIMPLE, A1, \field Name of Water to air heat pump A2, \field Availability schedule A3, \field Air Side Inlet Node A4, \field Air Side Outlet Node A5, \field Controlling zone or thermostat location A6, \field Supply air fan type A7, \field Supply air fan name A8, \field Fan placement A9, \field Simple Heating or Cooling coil type A10, \field Simple Heating or Cooling coil name A11, \field Supplemental heating coil type A12, \field Supplemental heating coil name A13, \field Fan Side Inlet Node A14, \field Fan Side Outlet Node A15, \field Heat pump operating mode N1, \field Fraction of the total volume flow that goes through controlling zone N2, \field Air Volumetric Flow Rate N3, \field Supplemental heating coil capacity N4, \field Maximum supply air temperature from heat pump supplemental heater N5; \field Maximum outdoor drybulb temperature for supplemental heater operation Figure 4.5. Input object for the simple watertoair heat pump model defined in the IDD 40 COIL:WATERTOAIRHP:SIMPLE, A1, \field Name of Coil A2, \field Water Side Inlet Node A3, \field Water Side Outlet Node A4, \field Air Side Inlet Node A5, \field Air Side Outlet Node N1, \field Design Water mass flow rate N2, \field Base water Mass Flow Rate N3, \field Base EER N4, \field Base COP N5, \field Base Capacity for the Heating mode N6, \field Base Capacity for the Cooling mode N7, \field Capacity Coefficient Heating 1 N8, \field Capacity Coefficient Heating 2 N9, \field Capacity Coefficient Heating 3 N10, \field COP Coefficient 1 N11, \field COP Coefficient 2 N12, \field COP Coefficient 3 N13, \field Capacity Coefficient cooling 1 N14, \field Capacity Coefficient cooling 2 N15, \field Capacity Coefficient cooling 3 N16, \field EER Coefficient 1 N17, \field EER Coefficient 2 N18; \field EER Coefficient 3 Figure 4.6. Input object for the heating and cooling coils as defined in the IDD 41 4.3.3. Flow of control / implementation algorithm The watertoair heat pump simulation is located in the module SIMPLEWATERTOAIRHP.. Once the heating/cooling latent and sensible demands are determined, the watertoair heat pump simulation routine is called. SIMSIMPLEWATERTOAIRHP is the main driver routine for the watertoair heat pump. The sensible and latent coil demands are passed as arguments to the driver routine. The driver routine, as shown in Figure 4.7, calls specific service subroutines to execute various tasks discussed below. • GetSimpleWatertoAirHPInput This subroutine obtains input data for the watertoair heat pump and stores it in the data structures. The routine also reads the data, checks whether they conform to IDD and IDF specifications, allocates arrays, initializes data structures and sets up report variables for the heat pump. • InitSimpleWatertoAirHP This subroutine initializes the watertoair heat pump components at the beginning of each environment, day, hour or time step. It uses status flags to trigger appropriate initializations. It also updates local simulation variables with the latest node data. • CalcSimpleWatertoAirHP This subroutine simulates the cooling and the heating mode of the watertoair heat pump. It simulates the model for the inlet conditions using the calculated performance coefficients and predicts the outlet conditions. The main subroutine employs 3 routines called HEAT, COOL and OFF which simulate the three operating modes of the heat pump. The check for the type of mode depends on the sign associated with the coil 42 SimFurnace simulates different Unitary euipment types Air to Air Heat pump Water to Air Heat pump Furnaces ……………… ……………… SimSimplewatertoairHP GetSimpleWatertoAirHPInput InitSimpleWatertoAirHP CalcSimpleWatertoAirHP UpdateSimpleWatertoAirHP Simulate Air Loop ……………… ……………… Simple Water to air Heat pump model Simulate Fan CalcCoolingcoil OFF CalcHeatingcoil Figure 4.7. Hierarchical flow of simulation demand, which has been discussed earlier as the thermostatic signal. The duty factor is calculated as the ratio of the zone load to the nominal capacity of the heat pump. Once all the outlet variables are computed, the control is transferred to the update routines. 43 • UpdateSimpleWatertoAirHP This subroutine updates the watertoair heat pump outlet nodes. Data is moved from the heat pump data structure to the heat pump outlet nodes. Output variables like the heating and cooling capacities, power consumption and the load side and the source side temperatures are computed. A detailed flowchart of the computational algorithm is shown in Figure 4.8.The algorithm uses the building hourly loads and the base heat pump capacity to compute the runtime fraction. The building hourly loads are equated to the heat pump hourly loads. Depending on the hourly load (Cooling/Heating), the algorithm switches between the operating modes. Then the model equations and the generated performance coefficients are used to calculate the performance variables such as capacities, COP and EER. The actual power, is the product of the calculated capacity and the runtime fraction. If the heat pump capacity exceeds the demand, the demand is met and the heat pump is switched off. However, if the demand exceeds the capacity, the duty factor of the heat pump equals 1, and and the heat pump runs at its maximum capacity. 44 Obtain Building Hourly loads and equate them to Heat pump hourly loads Runtime Fraction=HeatPumpLoad/BaseCapacity Calculate steady state heat transfer Qss=Basecap*(A1+B1*(Tin/Tref)+C1(mdot/mbase)(Tref/Tdb) Basecap is the Nominal capacity of the Heat pump A1,B1,C1 are coefficients obtained from performance trends Mbase is the rated mass flow rate multiplied by the base capacity Calculate COP COP=BaseCOP*(D1+E1*(Tin/Tref)+F1(mdot/mbase)(Tref/Tdb) BaseCOP is the Nominal COP of the Heat pump D1,E1,F1 are coefficients obtained from performance coefficient calculator Compute Heat Pump Power consumption Power=Qss/COP(or EER) HeatPumpload>0.0001 HeatPumpload<0.0001 & >0.0001 HeatPumpload<0.0001 Heating Cycle Cooling Cycle Switch HeatPump off Calculate EER EER=BaseEER*(D2+E2*(Tin/Tref)+F2(mdot/mbase)(Tref/Twb) BaseEER is the Nominal EER of the Heat pump D1,E1,F1 are coefficients obtained from performance coefficient calculator TotalCapacity>=HeatPumpLoad Switch HeatPump off Yes Compressor is cyclicfancyclic Yes Get the enthalpy, Humidity ratio from the nodes No Get the enthalpy, Humidity ratio from the nodes. Use Runtime fraction in calculations Update water to air Heat pump outlet nodes Actual Power = Calculated power*Runtime fraction Actual energy added/extracted= Calculated energy added/extracted*Runtime fraction Calculate steady state heat transfer Qss=Basecap*(A2+B2*(Tin/Tref)+C2(mdot/mbase)(Tref/Twb) Basecap is the Nominal capacity of the Heat pump A1,B1,C1 are coefficients obtained from performance trends Mbase is the rated mass flow rate multiplied by the base capacity Figure 4.8. Flow of code for the simple watertoair heat pump model 45 4.3.4. Output arrangement EnergyPlus has an efficient way of handling outputs. The output formats of the files are simple and text based. The output variables of the watertoair heat pump can be reported at every HVAC time step. EnergyPlus automatically saves all node data for each timestep. This information is available upon user request in the IDF. In addition, any simulation variable can be specified as a report variable in the computational module and included in the output reports. Output reporting is enhanced by the READVARS.EXE application which converts the text format of the output file to a readable excel table format. 46 CHAPTER 5 MODEL VERIFICATION 5.1. Introduction The simple watertoair heat pump is verified by using catalog data from two different heat pump manufacturers. The model has been verified for both heating and cooling modes. The verification exercise is intended to check both the form of the model and the generation of the performance coefficients. The performance coefficients for the model have been generated using the performance coefficient calculator, a graphical user interface written in visual basic and described in detail in Chapter 7. 5.2. Cooling mode verification Table 5.1 summarizes the results of the cooling mode verification exercise. The RMS error was less than 1.3% for the cooling capacity. The RMS error for the model prediction of power consumption in comparison with the catalog data ranged from 0.2% to 1.2%. The comparison shows good agreement between the data predicted by the model and the catalog data. The model also predicts sensible and latent capacity splits with good accuracy. The RMS error is higher when the nominal capacity of the heat pump increases drastically. This may be due to small inconsistencies in the catalog data. Table 5.1. List of heat pumps in cooling mode for model verification No Manufacturer Nominal Cooling Capacity RMS Error for Capacity W Btu/h RMS Error for Power RMS Error for Energy Added to ground 1 FHP024 7000 24000 0.42% 0.21% 0.33% 2 ClimateMasterGSH024 7000 24000 1.28% 1.15% 1.23% 47 Also, in the case of heat pump#1(Florida Heat Pump), with all data reported for a single water flow rate, all the calculated points lie on the diagonal to match the catalog data points as shown in Figures 5.15.4. However, in the case of heat pump#2 (ClimateMaster) as shown by Figures 5.55.8, some of the points group and cluster away from the diagonal. This error is small and may be a result of either the computer model used to generate the tables from a few measured points or of the testing and rating methods used by the manufacturer. Water source heat pumps are subjected to the rating requirements of ARI standard 320 which allows a tolerance of ±5% from the catalog data. Also, the ARI rated condition forms one data point among the various data points in the catalog. As previously noted, many of the catalog points are generated by computer models. This creates a built in uncertainty in the catalog data. 6 6.5 7 7.5 8 8.5 9 6.0 6.5 7.0 7.5 8.0 8.5 9.0 Catalog Total cooling capacity(kW) Calculated Total cooling capacity(kW) 20.5 22.5 24.5 26.5 28.5 30.5 20.5 22.5 24.5 26.5 28.5 30.5 Catalog Total cooling capacity(kBtu/Hr) Calculated Total cooling capacity(kBtu/hr) 10% +10% Figure 5.1. Catalog cooling capacity v/s calculated cooling capacity (heat pump #1) 48 0.3 0.4 0.5 0.6 0.7 0.3 0.4 0.5 0.6 0.7 Catalog Power Consumption(kW) Calculated Power Consumption(kW) 1 1.5 2 2.5 1 1.5 2 2.5 Catalog Power Consumption(kBtu/Hr) Calculated Power Consumption(kBtu/hr) 10% +10% Figure 5.2. Catalog power consumption v/s calculated power consumption (heat pump #1) 2 3 4 5 6 7 8 9 10 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 Catalog Total Sensible cooling capacity(kW) Calculated Total Sensible cooling capacity(kW) 10 12 14 16 18 20 22 24 10 12 14 16 18 20 22 24 Catalog Total Sensible cooling capacity(kBtu/hr) Calculated Total Sensible cooling capacity(kBtu/hr) 10% +10% Figure 5.3. Catalog sensible capacity v/s calculated sensible capacity (heat pump #1) 49 6 6.5 7 7.5 8 8.5 9 6.0 6.5 7.0 7.5 8.0 8.5 9.0 Catalog Heat transfer rate(kW) Calculated Heat transfer rate(kW) 20.5 22.5 24.5 26.5 28.5 30.5 20.5 22.5 24.5 26.5 28.5 30.5 Catalog Heat transfer rate(kBtu/Hr) Calculated Heat transfer rate(kBtu/hr) 10% +10% Figure 5.4. Catalog v/s calculated heat transfer rate (heat pump #1) 4500 5500 6500 7500 4500.0 5500.0 6500.0 7500.0 Catalog Total cooling capacity(W) Calculated Total cooling capacity(W) 15500 16500 17500 18500 19500 20500 21500 22500 23500 15500 16500 17500 18500 19500 20500 21500 22500 23500 Catalog Total cooling capacity(Btu/Hr) Calculated Total cooling capacity(Btu/hr) 10% +10% Figure 5.5. Catalog cooling capacity v/s calculated cooling capacity (heat pump #2) 50 200 250 300 350 400 450 500 200.0 250.0 300.0 350.0 400.0 450.0 500.0 Catalog Power Consumption(W) Calculated Power Consumption(W) 900 1200 1500 1800 2100 2400 2700 3000 900 1200 1500 1800 2100 2400 2700 3000 Catalog Power Consumption(Btu/Hr) Calculated Power Consumption(Btu/hr) 10% +10% Figure 5.6. Catalog power consumption v/s calculated power consumption (heat pump #2) 2500 3500 4500 5500 6500 7500 2500.0 3500.0 4500.0 5500.0 6500.0 7500.0 Catalog Total Sensible cooling capacity(W) Calculated Total Sensible cooling capacity(W) 13500 14500 15500 16500 17500 18500 19500 20500 21500 13500 14500 15500 16500 17500 18500 19500 20500 21500 Catalog Total Sensible cooling capacity(Btu/Hr) Calculated Total Sensible cooling capacity(Btu/hr) 10% +10% Figure 5.7. Catalog sensible capacity v/s calculated sensible capacity (heat pump #2) 51 4500 5500 6500 7500 4500.0 5500.0 6500.0 7500.0 Catalog Total Heat transfer rate(kW) Calculated Heat transfer rate(kW) 15500 16500 17500 18500 19500 20500 21500 22500 23500 24500 15500 16500 17500 18500 19500 20500 21500 22500 23500 24500 Catalog Heat transfer rate(kBtu/Hr) Calculated Heat transfer rate(kBtu/hr) 10% +10% Figure 5.8. Catalog v/s calculated heat transfer rate (heat pump #2) 5.3. Heating mode verification Table 5.2 summarizes the results of the heating mode verification exercise. There is a good agreement between the model predicted data and the catalog data for the heating mode of the simple model. The RMS error was less than 0.8% for the heating capacity. The RMS error for the model prediction of power consumption in comparison with the catalog data ranged from 0.2% to 0.65%. The comparison shows good agreement between the model predicted data and the catalog data. The heating model as shown by the Figures 5.95.14 performs better than the cooling model with most of the points lying on the diagonal. The error is small and probably occurs due to the uncertainty in the catalog data as discussed in the previous section. 52 Table 52. List of heat pumps in heating mode for model verification No Manufacturer Nominal Heating Capacity RMS Error for Capacity W Btu/h RMS Error for Power RMS Error for Energy extracted from ground 1 FHP024 7000 24000 0.41% 0.26% 0.31% 2 ClimateMasterGSH024 7000 24000 0.73% 0.62% 0.71% 1 3 5 7 9 11 1.0 3.0 5.0 7.0 9.0 11.0 Catalog Total Heating capacity(kW) Calculated Total Heating capacity(kW) 10 15 20 25 30 35 40 45 10 15 20 25 30 35 40 45 Catalog Total Heating capacity(kBtu/Hr) Calculated Total Heating capacity(kBtu/hr) 10% +10% Figure 5.9.Catalog heating capacity v/s calculated heating capacity (heat pump #1) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Catalog Power Consumption(kW) Calculated Power Consumption(kW) 1 1.5 2 2.5 1 1.5 2 2.5 Catalog Power Consumption(kBtu/Hr) Calculated Power Consumption(kBtu/hr) 10% +10% Figure 5.10. Catalog power consumption v/s calculated power consumption (heat pump #1) 53 1 3 5 7 9 11 1.0 3.0 5.0 7.0 9.0 11.0 Catalog Heat transfer rate(kW) Calculated Heat transfer rate 10 15 20 25 30 35 40 45 10 15 20 25 30 35 40 45 Catalog Heat transfer rate(kBtu/Hr) Calculated Heat transfer rate(kBtu/hr) 10% +10% Figure 5.11.Catalog v/s calculated heat transfer rate (heat pump #1) 2000 3000 4000 5000 6000 2000.0 3000.0 4000.0 5000.0 6000.0 Catalog Total Heating capacity(W) Calculated Total Heating capacity(W) 11500 13500 15500 17500 19500 21500 23500 25500 11500 13500 15500 17500 19500 21500 23500 25500 Catalog Total Heating capacity(Btu/Hr) Calculated Total Heating capacity(Btu/hr) 10% +10% Figure 5.12. Catalog heating capacity v/s calculated heating capacity (heat pump #2) 54 800 1300 1800 800.0 1300.0 1800.0 Catalog Power Consumption(W) Calculated Power Consumption(W) 2500 3100 3700 4300 4900 5500 2500 3100 3700 4300 4900 5500 Catalog Power Consumption(Btu/Hr) Calculated Power Consumption(Btu/hr) 10% +10% Figure 5.13. Catalog power consumption v/s calculated power consumption (heat pump #2) 2000 3000 4000 5000 6000 7000 8000 2000.0 3000.0 4000.0 5000.0 6000.0 7000.0 8000.0 Catalog Heat transfer rate(kW) Calculated Heat transfer rate(kW) 7000 12000 17000 22000 7000.0 12000.0 17000.0 22000.0 Catalog Heat transfer rate(kBtu/Hr) Calculated Heat transfer rate(kBtu/hr) 10% +10% Figure 5.14.Catalog v/s calculated heat transfer rate (heat pump #2) 55 5.4. Verification using Correction Factors 5.4.1. Correction Factors As noted in Section 4.3, some manufacturers, such as ClimateMaster, provide correction factors to account for the effect of parameters that are held constant in their data sets. These parameters include the air flow rates and the wet bulb temperatures. If the units are operating at conditions different than the rated conditions, the heat pump capacities can be approximated using these correction factors. Table 5.3 and 5.4 show air flow rate(CFM) and wet bulb temperature correction factors for total capacity (TC), sensible capacity (SC), power and heat rejection rate (HR) to the ground for cooling operation of the ClimateMaster GS series units. Table 5.3. Correction factors for the air flow rate CFM TC SC Power HR 350 0.957 0.946 0.994 0.964 375 0.979 0.969 0.997 0.982 400 1 1 1 1 425 1.021 1.029 1.003 1.018 450 1.043 1.058 1.006 1.036 Table 5.4. Correction factors for the wet bulb temperatures WB TC SC (80DB) Power HR 60 0.899 1.192 0.984 0.899 65 0.94 1.106 0.991 0.949 66.2 0.976 1.043 0.997 0.98 67 1 1 1 1 70 1.012 0.933 1.002 1.01 75 1.024 0.8 1.005 1.019 The influence of the correction factors on the performance variables is observed by plotting the correction factors against the air flow rates and the wet bulb temperatures as shown by Figure 5.15 and 5.16. 56 0.9 1 1.1 300 325 350 375 400 425 450 475 Air flow rate(CFM) Correction Factors TC SC Power HR Rated Rated Air Flow rate Figure 5.15. Influence of air flow correction factors on performance 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 55 61 67 73 79 Wet bulb Temperatures (F) Correction Factors TC SC (80DB) Power HR Rated Figure 5.16.Influence of wet bulb correction factors on performance Figure 5.15 shows that for a variation of 50 CFM (12.5%) in the air flow rate, the performance of the GS series heat pump changes by less than 10%. For the same change in air flow rate, the change in power is negligible. Since the sensible cooling capacity is highly dependent on the wet bulb temperature, it shows a significant variation (20%) over a wet bulb range of 15o F (6075oF) as shown in Figure 5.16. For low wet bulb depression (high relative humidity) a change in the wet bulb affects Rated Wet bulb 57 the latent/sensible split, but not the total capacity. At low relative humidity, Fig. 516 shows that a change in wet bulb temperature affects both the total capacity and the latent/sensible split. The plots suggest that the standard manufacturers’ data sets should be extended to include a range of air flow rates and inlet wet bulb temperatures (in addition to water mass flow rates, water inlet temperatures and inlet dry bulb temperatures). Since in the simulation environment, neither the wet bulb temperature nor the air flow rate are expected to always be at the rated condition, using the correction factors to extend the data set is necessary to ensure that the model is not applied outside the range of the catalog data. 5.4.2. Comparison of simulated and measured data using correction factors In this section, the effect of applying the model outside the range of the manufacturer’s standard data set is investigated. Since experimental data is not available, data points generated using correction factors are used.. The procedure is as follows: • Generate model coefficients using the standard data set (no correction factor data) • Compare predicted outputs with calculated outputs at nonrated (correction factor) conditions. • Generate model coefficients using extended data set (standard + correction factor data). • Compare predicted outputs with calculated outputs at nonrated (correction factor) conditions. 58 5.4.2.1. Comparison Procedure ClimateMaster GC series performance and correction data was used as shown in Fig 5.17. Nine data points were generated using the correction factors for the total cooling capacity at different air flow rates. Similarly, thirty one data points were generated using the correction factors for the sensible cooling capacity over the range of wet bulb temperatures. The data points were merged with the catalog data set characterized by constant air flow rate and wet bulb. Then the entire data set was used to generate a new set of coefficients for equations 41 through 43 and equations 415 through 417 by applying the least square technique. A set of coefficients based on the regular catalog data set (without the correction factors) and a set of coefficients (with the correction factors) were then used in the model equations to calculate heat pump capacities and power use at variable air mass flow rates and wet bulb temperatures. 59 Figure 5.17. Correction factors for GC ClimateMaster series (used with permission) The ClimateMaster GC018 data set was used in the verification process. The total and sensible capacities were calculated over a range of wet bulb temperatures and air mass flow rates as shown in Table 5.5. The 3rd data point was introduced by the 60 manufacturers to meet the European standards of 19oC(66.2oF) wet bulb and 27oC(80.6oF) dry bulb. Table 5.5. Range of wet bulb temperatures and air flow rates for GC series 5.4.2.2. Results Figure 5.185.23. summarizes the complete set of coefficients generated for the study. Two data sets were used to generate coefficients for six model equations: total capacity, sensible capacity and power for both forms of the model presented in section 4.3..By merging the extra data points generated using correction factors with the existing data set, coefficients for the total cooling capacity, sensible capacity and the power consumption equations are calculated. The coefficients generated using constant wet bulb temperature of 67 oF,(rated condition) diverge from the catalog data as shown in Figure 5.185.20. The one point that lies on the diagonal is the rated condition. With the inclusion of the corrected wet bulb temperature points, the total cooling capacity, sensible capacity and the power consumption were in good agreement with the catalog data. As expected for all three figures, the two models (equations 41 – 43 and equations 415417) show identical results for coefficients generated with and without correction factor data. As shown in figure 518, the total capacity is accurately predicted within 10% by the water mass flow rate alone. That is, generating model coefficients at the rated wet 61 bulb temperature then applying the model beyond the data set (over a 15 oF range of wet bulb temperatures) results in less than 1% error in the predicted total capacity. 5000 6000 7000 8000 5000.0 6000.0 7000.0 8000.0 Catalog Total cooling capacity(W) Calculated Total cooling capacity(W) Eqn(41) With correction factors Eqn(41) Without correction factors Eqn(415) With correction factors Eqn(415) Without correction factors +10% 10% Figure 5.18. Comparison of catalog v/s calculated cooling capacity with and without the correction factors for wet bulb temperatures Figure 519 shows that the sensible/latent split cannot be accurately predicted without including a range of wet bulb temperatures in the data set used to generate the coefficients. This is the expected result based on Figure 5.16 and 5.17. As shown in Figure 5.19 the error in the predicted sensible heat transfer rate is in the range of 30% to 40%. The error in the predicted power on the other hand is small (less than 2%) as shown in Figure 5.20. This result is also expected from Figure 5.16 and 5.17. 62 1000 3000 5000 7000 9000 1000.0 3000.0 5000.0 7000.0 9000.0 Catalog Sensible cooling capacity(W) Calculated Sensible cooling capacity(W) Eqn(43) With correction factors Eqn(43) Without correction factors Eqn(417) With correction factors Eqn(417) Without correction factors +40% 40% Figure 5.19. Comparison of catalog v/s calculated sensible cooling capacity with and without the correction factors for wet bulb temperatures 1300 1325 1350 1375 1400 1300.0 1325.0 1350.0 1375.0 1400.0 Catalog Power(W) Calculated Power(W) Eqn(41) With correction factors Eqn(41) Without correction factors Eqn(415) With correction factors Eqn(415) Without correction factors +2% 2% Figure 5.20. Comparison of catalog v/s calculated power consumption with and without the correction factors for wet bulb temperatures 63 Equations 4.14.3 are independent of the air mass flow rate and hence will not respond to any load side flow variation. However, at rated conditions (600 CFM), the calculated point coincides with the catalog data point as shown by Figures 5.215.23. Equation (4.15)(4.17) developed in section 4.3 accounts for air flow rate variation and the correction factors make possible a few data points to verify this form of the model. At constant air mass flow rate, = 1 a base a m m & & and Equations 4154.17 match Equation 4.14.3 at the rated condition. Figure 522 shows that the sensible/latent split cannot be accurately predicted without including a range of air flow rates in the data set used to generate the coefficients. This is the expected result based on Figure 5.15 and 5.17. As shown in Figure 5.22 the error in the predicted sensible heat transfer rate is about 27%. This is because the sensible heat transfer is more susceptible to a variation in the wet bulb temperature than the air flow rate as seen in Figure 5.15 and 5.17. The error in the predicted power is small (less than 3%) as shown in Figure 5.23. This result is also expected from Figure 5.15 and 5.17. 5700 6700 7700 5700.0 6700.0 7700.0 Catalog Total cooling capacity(W) Calculated Total cooling capacity(W) Eqn(415) Without correction factors Eqn(415) With correction factors Eqn(41)  Independent of Air Flow rate 6% +6% Figure 5.21. Comparison of catalog v/s calculated cooling capacity with and without the correction factors for variable air flow rates 64 3000 4000 5000 3000.0 4000.0 5000.0 Catalog Sensible cooling capacity(W) Calculated Sensible cooling capacity(W) Eqn(417) Without correction factors Eqn(417) With correction factors Eqn(43)  Independent of Air Flow rate 27% +27% Figure 5.22. Comparison of catalog v/s calculated sensible capacity with and without the correction factor for variable air flow rates 1200 1250 1300 1350 1400 1450 1500 1200.0 1250.0 1300.0 1350.0 1400.0 1450.0 1500.0 Catalog Power(W) Calculated Power(W) Eqn(417) Without correction factors Eqn(417) With correction factors Eqn(43)  Independent of Air Flow rate 3% +3% Figure 5.23. Comparison of catalog v/s calculated power consumption with and without the correction factors for variable air flow rates The results recorded in the above comparison plots indicate this mathematical influence on calculations to determine the stability of the implemented model simulation environment. 65 5.4.3. Application of the model outside the catalog data sets Jin(2002) validated the detailed model using ClimateMaster HS006 performance data. In the model validation, the HS series correction factors were used to expand the original data over a range of air temperatures and flow rates. The RMS errors from Jin’s work are shown for comparison table 5.6. The simplified model has been validated under identical conditions. The nominal capacity of the heat pump is 7400 Btu/h or 2170 W. ClimateMaster HS006 performance data is merged with the HS series correction factors for the air temperatures and flow rate. Since each correction factor is applied to multiple operating points (i.e. different entering water temperature, water flow rate and air flow rate), these data are presumed to have lower accuracy than the regular tabulated data. 2981 points are generated by applying correction factor to all the operating points. The performance is also calculated using the model equations (415) and (4 17). As shown in Figure 5.24 the error in the predicted sensible heat transfer rate is less than 13%.and the error in the predicted total cooling capacity is less than 10% The error in the predicted power is relatively small (less than 8%) as shown in Figure 5.25. The rootmeansquare (RMS) error for the total cooling capacity, sensible cooling capacity and power are compared with the detailed model in Table 5.6. The RMS errors between model prediction and catalog data for the total and sensible capacities are 6.4% and 7.7% respectively. This is slightly larger than the RMS error reported for the detailed model, but still quite reasonable. The RMS error in the predicted power is nearly the same for two models. Overall, the simplified model does a reasonable job of extrapolating beyond the data set. 66 3500 4500 5500 6500 7500 3500.0 4500.0 5500.0 6500.0 7500.0 Catalog Total cooling capacity(W) Calculated Total cooling capacity(W) +15% 15% Figure 5.24. Comparison of catalog v/s calculated cooling capacity for all points with the correction factors(simplified model equation(415)) 3500 4500 5500 6500 7500 8500 9500 3500.0 4500.0 5500.0 6500.0 7500.0 8500.0 9500.0 Catalog Sensible capacity(W) Calculated Sensible capacity(W) +15% 15% Figure 5.25. Comparison of catalog v/s calculated sensible capacity for all points with the correction factors(simplified model equation(417)) 67 300 400 500 600 700 800 900 1000 300.0 400.0 500.0 600.0 700.0 800.0 900.0 1000.0 Catalog Total Power(W) Calculated Total Power(W) +8% 8% Figure 5.26. Comparison of catalog v/s calculated power consumption for all points with the correction factors(simplified model) Table 5.6. Comparison of model RMS errors for ClimateMaster HS006 Performance Variable RMS Error Detailed Model Simple Model Total Capacity 4.72% 6.38% Sensible Capacity 6.33% 7.72% Power Consumption 6.38% 6.61% 68 CHAPTER 6 MODEL APPLICATION 6.1.Using manufacturers’ data As previously stated, manufacturers are required to certify the performance of their watertoair heat pumps using the Air Conditioning and Refrigeration Institute (ARI) Standard 320 and the ISO Standards 132561. The standard developed by ASHRAE Standard 90.1, references both ARI and ISO standards, with the ISO standard designated as the executive standard. The following performance metrics are verified by test for a limited set of conditions, usually 3 or 4 points. • Cooling Capacity, Btu/h [W] • EER, Btu/w.h [W/W] • Heating Capacity, Btu/h [W] • EER, Btu/w.h [W/W] As discussed in chapter 5, manufacturers only measure a few data points. They use the measured data to generate parameters for their own models. These models are then used to generate tables of catalog data. This catalog data is not necessarily accurate and often consists of physically inconsistent data. Most heat pump manufacturers use air temperatures or the mass flow rates as the basis for measuring the power and the capacities. Florida Heat Pump focuses on keeping the water and air flow rates constant while varying the entering water and air temperatures. ClimateMaster and Trane keep the flow rates constant. Section 5.4 used available correction factors to partially evaluate the effect of data sets with constant parameters (air mass flow rates and wet bulb temperatures. This section evaluates the basic model (equation 41 69 through 414) implemented in the EnergyPlus program with coefficients generated from both ClimateMaster and FHP data sets. The results of a 24 hour simulations are compared with results from detailed Jin (2002) model with coefficients generated from the same data sets. 6.1.1. Example 1  Florida Heat pump (FHP) The units follow the ISO 132561 standards. The nominal capacities of the units can be determined simply by looking at the identification number of each unit. For instance, FHP GT018 is designed with a nominal cooling and heating capacities of 18000 Btu/h and a FHP GT054 with 54000 Btu/h. The units are designed to operate with entering fluid temperatures between 50°F (10°C) and 110°F (43.3°C) in cooling and temperatures between 25°F (3.9°C) and 80°F (27°C) in heating. All catalog points are at constant air flow and water flow rates as shown in Figure 6.1. For every entering fluid temperature data point there are five different entering air wet bulb temperature data points in the cooling operation of the unit. Moreover, for every wet bulb temperature data point there are three sensible capacity data points at 75°F, 80°F and 85°F. This means that for the Florida Heat Pump, the total cooling capacity is only a function of the dry bulb temperatures and the sensible cooling capacity is a function of both the dry and the wet bulb temperatures. Qbase and base m& are selected at the peak capacities. For the FHPGT018, the peak cooling capacity 20900 Btu/h occurs at an entering water temperature of 50oF and an entering air temperature of 70oF. The water mass flow rate, mbase, occurs at a constant flow rate of 4 GPM. This will result in the coefficients of the capacity equations that depend only on the load side and source side temperatures since w base w m m & & =1 for all data points. 70 FHP GT018 Data All performance at 550 CFM and 4.0 GPM Entering Ent. Air Total H e a t Sensible Capacity BTUH EER Fluid Wet Bulb Capacity Watts ERnet.j e c t i o n Air Dry Bulb Temp. Temp. Temp. BTUH Input BTUH 75o 80o 85o 50 61 18259 713 20692 13895 16670 18259 23.4 50 64 19144 729 21632 12999 16502 18302 24 50 67 20044 745 22586 11966 15634 18040 24.6 50 70 20959 761 23556 9976 13791 17941 25.2 50 73 21889 777 24541  11754 16110 25.7 60 61 17375 776 20021 13222 15863 17375 19.9 60 64 18217 793 20922 12370 15703 17416 20.4 60 67 19074 810 21838 11387 14878 17167 20.9 60 70 19945 827 22768 9494 13124 17073 21.4 60 73 20830 845 23713  11186 15331 21.9 70 61 16464 852 19372 12529 15032 16464 17.7 70 64 17262 871 20234 11721 14880 16503 18.2 70 67 18074 890 21111 10790 14098 16267 18.6 70 70 18899 909 22001 8996 12436 16178 19.1 70 73 19738 928 22905  10599 14527 19.5 85 61 14701 994 18091 11187 13422 14701 15.3 85 64 15413 1016 18879 10466 13286 14735 15.7 85 67 16138 1038 19680 9634 12588 14524 16.1 85 70 16875 1060 20493 8032 11104 14445 16.5 85 73 17623 1083 21318  9464 12971 16.9 100 61 13318 1125 17156 10135 12159 13318 13.1 100 64 13963 1150 17887 9481 12037 13349 13.4 100 67 14620 1175 18629 8728 11404 13158 13.8 100 70 15287 1200 19383 7277 10059 13086 14.1 100 73 15966 1226 20148  8574 11751 14.4 Figure 6.1. Florida Heat Pump (GT018) cooling catalog data Identification code indicates the Nominal capacity GT018 18000 Btu/h 71 6.1.2 Example 2  ClimateMaster ClimateMaster holds the air temperature and the air mass flow rate constant to generate their data. Unlike the FHP unit in which the water mass flow rate remains constant through out the specification data, the water mass flow rate is varied between 2.2 and 4.5 gpm. Again, the identification code indicates the nominal capacity of the heat pump unit that is GSV018 indicates a nominal capacity of 18000Btu/h. The design conditions for the heat pump unit occur at 85°F and 4.5 gpm as shown in Figure 6.2. The entering air temperature is 80°F dry bulb and 67°F wet bulb for cooling and 70°F dry bulb for heating as per the ISO standards. Again, Qbase and mbase are selected at the peak cooling capacities. For the GSH/GSV018, the peak cooling capacity 22400 Btu/h occurs at an entering water temperature of 30oF, an entering air dry bulb temperature of 80oF and wet bulb temperature of 67oF. The volumetric flow rate is 4.5 GPM occurs at the peak capacity. This will result in coefficients of the capacity equations that depend only on source side mass flow rate and temperature since the load side wet bulb temperature is a constant for all data points. 72 Figure 6.2. ClimateMaster heat pump (GSH/GSV018) catalog data (used with permission) Design Conditions Base Capacity Base flow rate 73 6.1.3 Example 3 – Trane Trane is similar to ClimateMaster in its approach to following the standard rating conditions. The performance data is based on constant air dry bulb and wet bulb temperatures with varying water mass flow rates and entering water temperatures. GEH/V006 indicates a nominal capacity of 6000Btu/h. The entering air temperature is 80.6°F dry bulb and 67°F wet bulb for cooling and 68°F dry bulb for heating as per the ISO standards. For the GEH/V006, the peak cooling capacity or Qbase is 9200 Btu/h and occurs at an entering water temperature of 45oF ,an entering air dry bulb temperature of 80oF and wet bulb temperature of 67oF. The volumetric flow rate is 1.2 GPM at the peak capacity. 74 TRANE GEH006 Data Rated GPM 1.8 Rated CFM 242 Entering Ent. Water Total Heat Fluid Mass flow Capacity Watts COP Absorption Temp. rate BTUH Input BTUH EWT GPM 25 1.2 5.8 0.52 3.3 4 25 1.5 5.8 0.52 3.3 4 25 1.7 5.8 0.53 3.2 4 25 1.8 5.8 0.53 3.2 4 25 1.9 5.9 0.53 3.3 4.1 25 2 5.9 0.53 3.3 4.1 25 2.2 5.9 0.53 3.3 4.1 35 1.2 6.2 0.55 3.3 4.3 35 1.5 6.3 0.55 3.3 4.4 35 1.7 6.4 0.56 3.3 4.5 35 1.8 6.4 0.56 3.4 4.5 35 1.9 6.4 0.56 3.4 4.5 35 2 6.4 0.56 3.4 4.5 35 2.2 6.5 0.56 3.4 4.6 45 1.2 7 0.57 3.6 5.1 45 1.5 7.2 0.57 3.7 5.3 45 1.7 7.3 0.57 3.7 5.3 45 1.8 7.4 0.58 3.7 5.4 45 1.9 7.4 0.58 3.7 5.4 45 2 7.4 0.58 3.7 5.4 45 2.2 7.5 0.58 3.8 5.5 55 1.2 8 0.59 4 6 55 1.5 8.2 0.6 4 6.1 55 1.7 8.3 0.6 4 6.2 55 1.8 8.4 0.6 4.1 6.3 55 1.9 8.4 0.6 4.1 6.4 55 2 8.4 0.6 4.1 6.4 55 2.2 8.5 0.6 4.2 6.5 68 1.2 9.2 0.63 4.3 7.1 68 1.5 9.5 0.63 4.4 7.3 68 1.7 9.6 0.63 4.5 7.4 68 1.8 9.7 0.64 4.5 7.5 68 1.9 9.7 0.64 4.5 7.5 68 2 9.7 0.64 4.5 7.6 68 2.2 9.7 0.64 4.5 7.6 75 1.2 9.9 0.64 4.6 7.8 75 1.5 10.1 0.64 4.6 7.9 75 1.7 10.2 0.64 4.6 8 75 1.8 10.3 0.64 4.7 8.1 75 1.9 10.3 0.64 4.7 8.1 75 2 10.4 0.64 4.7 8.2 75 2.2 10.4 0.65 4.7 8.2 86 1.2 10.8 0.66 4.8 8.5 86 1.5 11 0.66 4.9 8.7 86 1.7 11 0.66 4.9 8.8 86 1.8 11.1 0.66 4.9 8.9 86 1.9 11.2 0.67 4.9 8.9 86 2 11.2 0.67 4.9 8.9 86 2.2 11.3 0.67 4.9 9 .Figure 6.3. TRANE heat pump (GEH/V 006) heating catalog data 75 TRANE GEH006 Data Rated GPM 1.8 Rated CFM 242 Entering Ent. Water Total Sensible Heat Fluid Mass flow Capacity Capacity Watts EER Rejection Temp. rate BTUH BTUH Input BTUH EWT GPM 45 1.2 9.2 6.4 0.49 18.9 10.9 45 1.5 9.2 6.4 0.47 19.6 10.8 45 1.7 9.3 6.4 0.46 20.2 10.9 45 1.8 9.3 6.4 0.45 20.6 10.9 45 1.9 9.3 6.4 0.45 20.6 10.9 45 2 9.3 6.4 0.45 20.6 10.9 45 2.2 9.3 6.4 0.45 20.6 10.9 55 1.2 8.8 6.2 0.49 18 10.4 55 1.5 8.9 6.2 0.47 18.9 10.5 55 1.7 8.9 6.2 0.46 19.2 10.4 55 1.8 8.9 6.2 0.45 19.6 10.4 55 1.9 8.9 6.2 0.45 19.6 10.4 55 2 8.9 6.2 0.45 19.6 10.4 55 2.2 8.9 6.2 0.45 19.6 10.4 68 1.2 8.2 5.9 0.54 15.2 10 68 1.5 8.3 6 0.52 15.8 10 68 1.7 8.3 6 0.51 16.1 10 68 1.8 8.3 6 0.5 16.4 10 68 1.9 8.3 6 0.5 16.7 10 68 2 8.3 6 0.5 16.7 10 68 2.2 8.3 6 0.5 16.7 10 75 1.2 7.8 5.8 0.58 13.4 9.8 75 1.5 7.9 5.8 0.57 14 9.8 75 1.7 7.9 5.8 0.55 14.4 9.8 75 1.8 7.9 5.8 0.54 14.7 9.8 75 1.9 8 5.8 0.54 14.8 9.8 75 2 8 5.9 0.54 14.8 9.8 75 2.2 8 5.9 0.54 14.8 9.8 86 1.2 7.4 5.6 0.66 11.2 9.7 86 1.5 7.4 5.7 0.64 11.5 9.6 86 1.7 7.4 5.7 0.64 11.6 9.6 86 1.8 7.5 5.7 0.63 11.9 9.6 86 1.9 7.5 5.7 0.62 12.1 9.6 86 2 7.5 5.7 0.62 12.1 9.6 86 2.2 7.5 5.7 0.62 12.1 9.6 95 1.2 7 5.4 0.73 9.5 9.5 95 1.5 7 5.5 0.71 9.9 9.5 95 1.7 7 5.5 0.7 10 9.5 95 1.8 7 5.5 0.7 10.1 9.4 95 1.9 7 5.5 0.7 10.1 9.4 95 2 7 5.5 0.69 10.3 9.4 95 2.2 7 5.5 0.69 10.3 9.4 105 1.2 6.5 5.2 0.81 8.1 9.3 105 1.5 6.5 5.2 0.8 8.2 9.3 105 1.7 6.6 5.2 0.78 8.4 9.3 105 1.8 6.6 5.2 0.77 8.5 9.3 105 1.9 6.6 5.2 0.77 8.5 9.3 105 2 6.6 5.2 0.77 8.6 9.2 105 2.2 6.6 5.2 0.77 8.6 9.2 115 1.2 5.9 5 0.88 6.7 8.9 115 1.5 5.9 5 0.87 6.8 8.9 115 1.7 6 5 0.85 7 8.9 115 1.8 6 5 0.84 7.1 8.9 115 1.9 6 5 0.84 7.1 8.9 115 2 6 5 0.84 7.1 8.9 115 2.2 6 5 0.84 7.2 8.9 120 1.2 5.4 4.9 0.9 6 8.5 120 1.5 5.5 4.8 0.9 6.1 8.5 120 1.7 5.5 4.8 0.89 6.2 8.5 120 1.8 5.6 4.8 0.88 6.3 8.6 120 1.9 5.6 4.8 0.87 6.4 8.5 120 2 5.6 4.8 0.87 6.4 8.5 120 2.2 5.6 4.8 0.87 6.4 8.5 .Figure 6.4. TRANE heat pump (GEH/V 006) Cooling catalog data Identification code indicates the Nominal capacity GEH/V 006 6000 Btu/h 76 6.2.Case study The simplified watertoair heat pump model that was implemented in EnergyPlus as described in the previous chapters is evaluated by comparing and analyzing its performance in the simulation environment. The case study was carried out on a typical office building assumed to be located at Chanute AFB, Illinois. An annual building loads simulation along with simulations for the summer design day (21st July) and the winter design day (21st January) were run for this region. Results obtained from the simplified model are compared with detailed model (Jin,2002) results for the same building, system and environmental conditions. 6.2.1. Example building and system description The example building shown in Figure 6.5 has an area of 108 m2. The zones are served by a watertoair heat pump unit that includes a supplemental heating coil. Figure 6.5. Isometric north east view of the building plan A summary of additional assumptions and a brief description of the building and system are shown below. 77 • Typical building with 3 thermal zones. • No ground contact (all floors are adiabatic). • Roofs are exposed to the outdoor environment • There is one large south facing single pane window located in the southwest zone. • The air handling system is modeled as a blow through system. • The lighting loads are 7.6 W/m2. The electric equipment plug load is 12.1W/m2. • The office occupancy is assumed to be one person per 10.7 m2 with a total heat gain of 131.7 Watts/Person of which 30% is assumed to be radiant heat gain. • A single watertoair heat pump serves all zones. • The controlling zone is the east zone. • The heating set point during winter months is 20°C during occupied hours, 15°C setback otherwise. Cooling set point is at 24°C during occupied hours only. • The heat pump is scheduled to be unavailable during unoccupied hours. 78 Zone Water to air cooling mode Water to air heating mode Ground loop heat exchanger Supply fan Cooling coil Heating coil Water to Air Heat Pump Figure 6.6. Schematic of the building and system plan implemented in EnergyPlus 6.2.2. System connections and configuration In the case of a watertoair ground source heat pump as shown in Figure 6.6, the condenser loop is directly connected to the air loop. The condenser loop has a constant speed circulating pump that operates continuously. The heat pumps runs with design flow rates on both the load and the source sides. The design water mass flow rate is set at 1.7 kg/s and the design load side mass flow rate is set at 2 kg/s. The heating coil of the single reversible packaged unit is available for operation during winter and the cooling coil in summer. The supplemental gas heating coil is assumed to have a nominal capacity of 32000W. A simple ONOFF supply fan with a total 79 efficiency of 70% is used. The fan outlet node forms the inlet node to the heating/cooling coils with in the heat pump. The outlet node of the heating coil forms the inlet node to the supplemental coil. The outlet node of the supplemental coil becomes the outlet node of the heat pump to complete the ground loop configuration. 6.2.3 Annual and design day building load profiles Fig 6.7 shows the hourly zone cooling and heating loads for the example building when simulated with Chanute AFB, Illinois weather data. Heating loads are shown as positive loads and cooling as negative loads. The peak heating load is around 15KW and the peak cooling load is around 16KW. The building is well balanced in terms of the heating and cooling loads. In order to verify the correctness of results generated by the simulation, the heat pump was operated in the heating and the cooling mode. For the winter design day, that is Jan 21st, the heating mode of the heat pump is triggered. For the summer design day, that is July 21st, the cooling mode of the heat pump is activated. The schedule is an active day schedule for both design days, which means that the design day is a typical Monday. The load profile for the winter design day is shown in Fig 6.8. Although typically, a winter design day includes no scheduled load, some scheduled loads were included to exercise the model. 80 20000 15000 10000 5000 0 5000 10000 15000 20000 1 730 1459 2188 2917 3646 4375 5104 5833 6562 7291 8020 8749 Number of timesteps Building loads[W] Heating load Cooling load Figure 6.7. Annual hourly loads for the example building in Chanute AFB,IL Figure 6.8 shows the winter design day heating load. From the 1st time step through the 42nd time step, all electric equipment is scheduled OFF and the building is unoccupied. At 7 a.m. (the 43rd time step), the building experiences a high pickup load as the system comes out of setback. Starting at 7a.m. (timestep 43) office occupancy, lighting and all electric equipment schedules begin to ramp up. This along with the solar heat gain largely offset, the heating load which steadily decreases until the activity comes to an end at 5p.m. At 5p.m the building suddenly experiences a major drop in the heating load as the system returns to setback. The load is zero until the sun sets and the thermal mass of the building cooling and which point the heating load steadily increases until the system comes out of setback. 81 Figure 6.8. Building load profile for the winter design day Figure 6.9. Building load profile for the summer design day The load profile for the summer design day is shown in Fig 6.9 and may be analyzed as follows. The peak cooling load is about 17KW. The pick up load again occurs at 7 a.m. as activity and system schedules are initiated. The maximum dry bulb temperature of 32°C and solar radiation contributes to the higher peak load. Once the system stabilizes, the building load steadily increases due to the effect of increasing 82 dry bulb temperature, solar heat gains and scheduled loads until the system returns to setback. 6.2.4 Selection of heat pump A Florida heat pump GT120 having a nominal capacity of 38KW is selected for the typical office building. The heat pump is modeled as a single heat pump which switches the control to the heating or cooling coils depending on the mode in which the heat pump is required to operate. Although the heat pump is modeled to operate throughout the year, a schedule is maintained to handle the demands appropriately. The cooling mode is made available only in summer and the heating mode is available only in winter. The nominal capacity of the heat pump is set at 38KW with a nominal COP of 4.5 in the heating mode and a nominal EER of 20 in the cooling mode. The nominal values are characteristic of the GT120 Florida heat pump used in this study. Using the nominal values above and the catalog data for the GT120 unit, 17 coefficients were generated as shown in Table 6.1 by using the performance coefficient calculator discussed in the next chapter. Table 61. Distribution of coefficients in model equations Performance Variables No. of Coefficients in the Performance Equations Cooling Capacity 3 Heating Capacity 3 COP 3 EER 3 Sensible Cooling Capacity 5 83 6.2.5. Cooling mode operation and analysis Based on the control zone sensible load , represented by the east zone in this case study and the fraction of air flow through the control zone, the total cooling sensible load demanded by all the zones being served is given by equation (6.1). ControlZoneAirFlowFraction ControlZoneCoolingLoad HeatPumpCoolingLoad = ………………….. (6.1) On the condenser demand side all the components are already simulated and controlled by the air loop. The condenser demand side manager is responsible for supplying the specified flow rate through the source side coil. 0 5000 10000 15000 20000 25000 30000 1 12 23 34 45 56 67 78 89 100 111 122 133 144 Number of timesteps Cooling demands[W] Figure 6.10. Demands for the summer design day The load side coil demand, shown in Figure 6.10, peaks late in the afternoon, unlike the building load (Figure 6.9) which reaches a high point as the office building begins its operation at 7a.m.This is due to the effect of outside air which increasingly adds to the coil demand as the outside temperature rises. To meet this demand, the heat pump coils and fans must be configured and scheduled appropriately. The heat pump may be configured for ‘blowthrough’ or ‘drawthrough’ operation with the fan operating in either of two modes: ‘cycling’ or ‘continuous’. 84 0 100 200 300 400 500 600 700 1 12 23 34 45 56 67 78 89 100111122133144 Number of timesteps Fan Electric Power[W] Figure 6.11. Fan electric power consumption (cooling mode) The profile of the fan electric power consumption as shown in Figure 6.11 is identical to the demand that must be met by the heat pump. A simple algorithm switches the fan OFF when the cooling load to be met by the heat pump is zero and switches the fan ON when it is greater than zero. The fan power consumption is directly proportional to the air mass flow rate. The flow rate is resolved as the program iterates through each node and determines what the flow requests and flow limits are. Figure 6.12 demonstrates point to point synchronization between the cooling demand and the demand met. The heat pump is operated at full load and its capacity under full load conditions is determined. The run time fraction in cooling mode is calculated by using a simple control strategy as shown by equation 6.2. BaseCoolingCapacity TotalCoolingdemand RuntimeFractionCool = ……………………… (6.2) 85 0 5000 10000 15000 20000 25000 30000 1 12 23 34 45 56 67 78 89 100 111 122 133 144 Number of timesteps Cooling Demand/Met[W] Cooling Demand[W] Cooling MET[W] Figure 6.12. Cooling demands met by the heat pump Once the runtime fraction is computed, the total capacity under full load conditions is multiplied by the runtime to compute the actual capacity. The other performance variables such as heat pump power consumption and energy added to the air stream by the heat pump are calculated in the same manner as shown in the following equations. ActualCapacity TotalCapacity RuntimeFractionCool fullload = * ………………… (6.3) ActualPower TotalPower RuntimeFractionCool fullload = * ……………………….(6.4) ActualGroundHeatTrans TotalGroundHeatTrans RuntimeFractionCool fullload = * (6.5) The performance variables depend on the requested water mass flow rate. In the case study, the east zone temperatures are controlled within the bounds of the set point at 24°C when the building is occupied and the heat pump is operating. The temperature peaks high for a few time steps when the system is switched off and then gradually drops to the set point at 30°C. The sudden rise and drop in the temperatures, which occur at 7 a.m and 5 p.m occur as the system comes out of setback or goes into setback, are shown in Figure 6.13. 86 0 5 10 15 20 25 30 35 40 45 50 1 12 23 34 45 56 67 78 89 100 111 122 133 144 Number of timesteps East zone Temperature[C] Figure 6.13. Controlled zone temperatures (cooling mode) 6.2.6. Heating mode operation and analysis Analogous to the cooling mode operation, the total heating sensible load demanded by all the zones being served is given by Equation (6.6). ControlZoneAirFlowFraction ControlZoneHeatingLoad HeatPumpHeatingLoad = ………………….. (6.6) The demand picks up at 7a.m as the building begins its operation as shown in Figure 6.14. Once the building stabilizes the daytime heating demand decreases gradually due to the presence of scheduled internal heat gains. Analogous to the cooling mode, the fan switches OFF when the heating load to be met by the heat pump is zero and switches ON when it is greater than zero. The profile of the fan electric power consumption as shown in Figure 6.15 is identical to the demands needed to be met by the heat pump. 87 0 10000 20000 30000 40000 50000 60000 1 14 27 40 53 66 79 92 105 118 131 144 Number of timesteps Heating demand[W] Figure 6.14. Demands for the winter design day 0 100 200 300 400 500 600 700 800 900 1 12 23 34 45 56 67 78 89 100 111 122 133 144 Number of timesteps Fan Electric Power[W] Figure 6.15. Fan electric power consumption (heating mode) Figure 6.16 shows excellent agreement between the heating demand and the demand met. The heat pump is operated at full load and its heating capacity under full load conditions is determined. 88 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 1 12 23 34 45 56 67 78 89 100 111 122 133 144 Number of timesteps Heating Demand/Met[W] Heating demand[W] Heating Demand Met[W] Demand exceeds Capacity Heat pump cannot meet demand Figure 6.16. Heating demand v/s demand met The run time fraction, actual heating capacity and the actual power for the heating load is calculated using equation 6.36.6. In the case study, the east zone temperature is controlled within the bounds of the set point at 20°C when the building is occupied and the heat pump is operating. The temperature peaks high for a few time steps when the system is switched off and then gradually drops to the set point at 15°C as shown in Figure 6.17. 0 5 10 15 20 25 1 12 23 34 45 56 67 78 89 100 111 122 133 144 Number of timesteps East Zone temperatures[C] Figure 6.17. Controlled zone temperatures (heating mode) 89 The sudden rise and drop in the temperatures occur at 7 a.m and 5 p.m occur as the system comes out of setback or goes into setback.. A few stand alone points show up between the 43rd and the 48th time step and then again between the 108th time step and the 111th time step as the building system tries to approach a steady state configuration. Some other important plots which are a significant part of this case study are discussed and compared with the detailed model in the next section. 6.2.7. Comparison with the Detailed Model The building discussed in the preceding sections is simulated again in EnergyPlus using the existing detailed model (Jin, Spitler; 2002). It is a parameter estimation based model which incorporates a multivariable unconstrained optimization algorithm to estimate model parameters. The detailed model is discussed in section 2.2.1. Comparison of the detailed and the simplified model shows that the new model is in good agreement with the detailed model when the heat pump operates at the rated air mass flow rate and wet bulb temperature. The demands, demand met, duty factor and the power consumption calculated using the detailed model match the simplified model at every point as shown by Figures 6.18 and 6.19. 90 0 5000 10000 15000 20000 25000 30000 1 12 23 34 45 56 67 78 89 100 111 122 133 144 Number of timesteps Cooling demand / Met [W] Cooling demand  Detailed Model[W] Cooling demandSimplified Model [W] Demand Met Detailed Model[W] Demand MetSimplified Model[W] Figure 6.18. Comparison of demand v/s met in the cooling mode 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 1 12 23 34 45 56 67 78 89 100 111 122 133 144 Number of timesteps Heating demand / Met [W] Heating demandDetailed [W] Heating demandSimplified [W] Heating demand MetSimplified [W] Heating Demand Met Detailed[W] Demand exceeds capacity Heat pump cannot meet demand Figure 6.19. Comparison of demand v/s met in the heating mode In cooling mode the heat pump operates with the highest duty factor of 0.6 in the 43rd time step due to the pickup load as shown in Figure 6.20. At this point the heat pump has to meet a capacity of about 20000W with a power consumption of about 5000W. In the heating mode the heat pump has to operate at full capacity to meet a demand of 91 about 45000W as shown in Figure 6.21. At this point the duty factor is 1 which means that the heat pump is operating at its peak. The points that do not occur along the smooth curve represent the time steps in which the system attempts to attain a steady state after experiencing a huge pick up load. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 1000 2000 3000 4000 5000 6000 Power consumption[W] Duty factor Detailed Simplified Figure 6.20. Comparison of duty factor v/s power consumption in the cooling mode 92 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 1000 2000 3000 4000 5000 6000 7000 8000 Power Consumption[W] Duty Factor Detailed Simplified Figure 6.21. Comparison of duty factor v/s power consumption in the heating mode 6.2.8. Model performance under off design conditions The design water flow rate at which the heat pump operates is 4.5 GPM. The heat pump offdesign performance is observed by changing the design flow rate to 4 GPM. The profile for both the heating and the cooling modes under off design conditions matches the design conditions as shown in Figures 6.22 and 6.23. This happens because the capacity of the heat pump is directly proportional to the water mass flow rate through it. However at points where the demands exceed capacity, the heat pump fails to meet the demand. 93 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 1 12 23 34 45 56 67 78 89 100 111 122 133 144 Number of timesteps Heating demand / Met [W] Heating demandSimplified [W] Heating demand Met(Off design)Simplified [W] Figure 6.22. Heating mode under off design conditions 0 5000 10000 15000 20000 25000 30000 1 12 23 34 45 56 67 78 89 100 111 122 133 144 Number of timesteps Cooling Demand/Met[W] Cooling Demand[W] Cooling MET[W]Off design Figure 6.23. Cooling mode under off design conditions As shown in section 5.4, the model’s sensitivity to wet bulb temperature is negligibly small for the case when the C1 term in equation 4.1 has been fit for constant wet bulb and variable flow rate (i.e. ClimateMaster data). Based on this analysis, offdesign wet bulb conditions are not shown in this section. 94 6.2.9. Annual Simulation Computation time For the case study input file, the parameter estimation model running in EnergyPlus takes approximately 35 minutes computing time for an annual simulation on a PC with Pentium 4, 2.4GHz CPU. The simple model takes approximately 7 minutes under identical conditions, which is about 80% faster than the parameter estimation model. 95 CHAPTER 7 PERFORMANCE COEFFICIENTS CALCULATOR 7.1. Need for an interface Computation of the model performance coefficients is an important prerequisite to executing accurate simulations. Although a generalized least square method will serve the purpose, calculation of every coefficient for different heat pump units becomes tedious without a graphical user interface. An interface was developed that not only calculates the performance coefficients, but also compares every calculated data point with the corresponding catalog data point in a graphical environment to demonstrate the consistency of the model coefficients with the catalog data. 7.2. Front end architecture The front end is a visual basic graphical user interface (GUI), the backend application is the generalized least square method. The program interacts with the user through a combination of menu driven event handling subfunctions. Event driven programming determines the sequence of operations for an application by the user’s interaction with the application interface (forms, menus, buttons, graphical components etc). Here, the user picks up the process to be performed and the event driven application remains in the background. The advantage of this design is that the application logic that processes events is clearly separated from the user interface logic that generates these events. 96 7.2.1. Workspace The key elements in designing an interface are deciding what the user sees, what data he will enter, what kind of warning boxes the application will use and how the application will handle inputs and outputs. Figure 7.1 shows the application window. The application begins by automatically asking the user to select the mode in which the heat pump is operating. Once the user clicks on the command button to select the mode, the application window interacts with the user again prompting him to click the type of performance variable coefficients to be generated. In the cooling mode, the user may click on the Cooling capacity, EER or Sensible capacity coefficients as shown in Figure 7.2. In the heating mode, the user selects heating capacity or COP coefficients. Once the user makes his selection, the application automatically loads the interface window. The interface window consists of a comprehensive menu that supports and conducts all important operations. Figure 7.3 shows the interface window. Operations such as opening a new file, clearing all the values on the interface window, saving the file and aborting the application can be performed under the FILE menu. The window accepts input parameters for the model equations and reports the values of the coefficients after back end processing. The window also calculates the model outputs and compares them with respective catalog data points to demonstrate the correctness of the coefficients. 97 Figure 7.1. Application window of the performance coefficient calculator 98 Figure 7.2. Window when the user clicks on cooling mode 99 Figure 7.3. The main interface window 100 7.2.2. Input format For each unit at the specified operating conditions, the following catalog and input data is needed • Total cooling capacity (Cooling Mode) • Sensible cooling capacity (Cooling Mode) • Heating capacity (Heating mode) • Energy efficiency ratio(EER) • Coefficient of performance(COP) • Nominal capacity of the heat pump unit • Nominal water mass flow rate • Inlet water mass flow rate • Entering air temperature • Entering water temperature • Nominal coefficient of performance of the heat pump unit • Nominal energy efficiency ratio of the heat pump unit The nominal values, which are determined from the manufacturer’s catalog are passed to the back end processor by using textboxes. Since the catalog data is the heat pump performance data at multiple points, it would be tedious to have the user enter every single operating point into the text box. Hence a grid component has been introduced as shown in Figure 7.3. The grid component gets information from a text file with the extension “.DAT”. The text file contains all the catalog input parameters with their values at the corresponding operating points as shown in Figure 7.4. 101 Figure 7.4. A typical model input file The grid component is also associated with a model definition file that understands the format of the input that is being fed into the grid component as shown in Figure 7.5. In other words, there is a correspondence between the model definition file and the input file. The model definition file has a “.pd” extension. The model definition file has to be loaded into the component before feeding the input information so that the grid component assimilates the type of information it needs to load. 102 Figure 7.5. A typical model definition file Once the definition and the inputs are passed into the component, the inputs are displayed on the interface window. Figure 7.6 shows an example of a pre processed interface window when Florida heat pump GT018 is loaded into it. 103 Figure 7.6. Preprocessed interface window 104 7.2.3. Reporting results After the coefficients are generated by the back end processor, which is the generalized least square algorithm, a validation technique is required to determine if the generated results are reasonable. The interface plots point to point values of the performance variables and then compares every calculated (Model) data point with the catalog data point. This feature triggers au
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Title  Simulation, Modeling and Analysis of a Water to Air Heat Pump 
Date  20041201 
Author  Shenoy, Arun 
Document Type  
Full Text Type  Open Access 
Abstract  The objective of the thesis is to develop a simplified model that can simulate a water to air ground source heat pump system and implement the model in EnergyPlus. The model which includes a ground loop configuration is significantly modified for implementation in the EnergyPlus program. Also, the model is extended to calculate sensible and latent capacity splits for the heat pump while operating in the cooling mode. The performance of the simplified model is compared with the parameter estimation watertoair heat pump model (Jin 1999) to determine the usefulness of implementing a simple model. The simplified and the detailed models are compared in a case study and the results are summarized and analyzed. The performance data generated using the model is compared with the manufacturer's catalog data to check the validity of the simulation. A visual basic graphical user interface that generates performance coefficients from manufacture's data was also developed. The model was successfully implemented in EnergyPlus as a single component model which can switch between heating and cooling modes. The accuracy of model coefficients generated from two different heat pump manufacturers' catalog data was evaluated. This evaluation showed that for all cases, the error in the predicted results was less than 1.5%. The model has been validated for both the heating and the cooling modes. During the validation exercise, the model outputs were compared with the catalog data of the heat pump manufacturer. The comparison show a good agreement between the model predicted data and the calculated data. It was concluded that the model is useful to obtain sensible and latent capacity splits with good accuracy. A case study to check the performance of the heat pump model revealed that the models show the expected trends of the thermal processes correctly. For an annual simulation, the simplified model was 80% faster than the same input file with the detailed model. 
Note  Thesis 
Rights  © Oklahoma Agricultural and Mechanical Board of Regents 
Transcript  SIMULATION, MODELING AND ANALYSIS OF A WATER TO AIR HEAT PUMP By ARUN SHENOY Bachelor of Engineering Bangalore University Bangalore, India 2001 Submitted to the Faculty of the Graduate College of the Oklahoma State University in partial fulfillment of the requirements for the Degree of MASTER of SCIENCE December, 2004 ii SIMULATION, MODELING AND ANALYSIS OF A WATER TO AIR HEAT PUMP Thesis Approved: ________________Dr. D.E.Fisher____________________ Thesis Advisor _________________Dr.J.D.Spitler___________________ __________________ Dr. D.G.Lilley_________________ _________________Dr.Emslie Gordon________________ Dean of the Graduate College iii ACKNOWLEDGEMENTS Many people have been a part of my graduate education, as friends, teachers, and colleagues. Dr. Fisher, first and foremost, has been all of these. The best advisor and teacher I could have wished for, he is actively involved in the work of all his students, and clearly always has their best interest in mind. Many thanks to the members of my thesis committee, Dr. Spitler and Dr. Lilley for helping to supervise me, providing resources and subjects, and offering direction and criticism. I have also benefited from many course discussions with the members. Special thanks to Sankar and Haider; their comments, encouragement and criticism helped me many times. I cannot end without thanking my father Late Shri.H Gourishanker Shenoy my mother Late Smt.H. Geetha Shenoy, and my sister H.Kiran Shenoy on whose constant love and memory I have relied throughout my time at OSU. iv TABLE OF CONTENTS Chapter Page 1. INTRODUCTION……………………………………………………………...1 1.1.Background………………………………………………………………....1 1.2.Thesis objective and scope………………………………………………...2 2. LITERATURE REVIEW…………………….………………………………...3 2.1. Equation fit water to air heat pump and chiller models…….........................3 2.1.1. Lash model……………………………………………..……………3 2.1.2. Allen and Hamilton model...………………………………………...5 2.1.3. DOE2 model………………………………………………………..7 2.1.4. BLAST model……………………………………………………….7 2.1.5. Hamilton and Miller model……………………………………….. ..9 2.1.6. Parent and Larue development of Domanski model………………..10 2.2. Jin and Spitler’s parameter estimation water to air heat pump model.…....11 2.2.1. Overview…………………………………………………………....11 2.2.2. Parameter estimation procedure...……………………...… ….…….14 2.2.3. Model implementation……………………………………………...15 3. OVERVIEW OF ENERGYPLUS……………………………………………..17 3.1. Introduction………………………………………………………………...17 3.2. Simulation environment…………………………………………………...17 3.3. Water source heat pumps in EnergyPlus……………………..…………...20 3.3.1. Water to air heat pump simulation………………..………….……...20 3.3.2. Water to water heat pump simulation…………………………….....21 3.4. Implementing watertoair heat pump models in EnergyPlus……….……..22 v 4. MODEL DEVELOPMENT AND IMPLEMENTATION………………..…....24 4.1. Development of the simplified water to air heat pump model……………..24 4.2. Design Basis………………………………………………………...….......24 4.2.1. Cooling mode equations...……………………………………….….27 4.2.2. Heating mode equations...……………………………………….….31 4.2.3. Accommodating variable mass flow rates.…………..…….……..…32 4.2.4. Determination of performance coefficients……………………….....33 4.2.5. Performance coefficients calculation procedure…………………......34 4.3. Model implementation……………………………………………...…........35 4.3.1. Input configuration…………………………………………………..36 4.3.2. Input specification………………………………...............................37 4.3.3. Flow of control/ implementation algorithm………………………...41 4.3.4. Output arrangement………………………………..............................45 5. MODEL VERIFICATION……………………………………………………..46 5.1. Introduction……………………………………………….……………... ...46 5.2. Cooling mode verification…………………………….……………...........46 5.3. Heating mode verification…………………………….……………...........51 5.4. Verification using correction factors……………………..………...….... ..55 5.4.1. Correction factors..…………………………....…………………......55 5.4.2. Comparison of simulated and measured data using correction factors………………………………………………57 5.4.2.1.Comparison procedure………………………………………….58 5.4.2.2.Results……………….………………………………………….60 5.4.3. Application of the model outside the catalog data sets ….………..…64 6. MODEL APPLICATION………………………………………………………67 6.1. Using Manufacturers’ data…………………………………………….....…67 6.1.1. Example 1 Florida Heat Pump……………………………………...68 6.1.2. Example 2 ClimateMaster……………….……………………….....70 6.1.3. Example 3 TRANE………………………………………...…….....72 vi 6.2. Case Study……………... ………………………………………………….76 6.2.1. Example building and system description……………………..........76 6.2.2. System connections and configuration….…………………………...78 6.2.3. Annual and design day building load profiles …………………….79 6.2.4. Selection of heat pump…………………............................................82 6.2.5. Cooling mode operation and analysis…………………......................83 6.2.6. Heating mode operation and analysis…………………......................86 6.2.7. Model validation by comparison with the detailed model………......89 6.2.8. Model performance under off design conditions...………..…….......92 6.2.9. Annual simulation computation time………………………………94 7. PERFORMANCE COEFFICIENT CALCULATOR………………………….95 7.1. Need for an interface…………………………………….……………... …95 7.2. Front end architecture……………………………….……………........ …..95 7.2.1. Workspace……………………………………………………….......95 7.2.2. Input format……………….………………………………………...100 7.2.3. Reporting results…………………………………….........................104 7.3. Backend architecture……………………………….……………... ……...106 8. CONCLUSIONS AND RECOMMENDATIONS……..……………………..107 8.1. Conclusions………..…………………………………….………………...107 8.2. Recommendations..……………………………….…………….................109 References……………………………………………………………………...111 Appendix A…………………………………………………………………….113 vii LIST OF FIGURES Figure Page 2.1. Heat Pump system nodes (Lash 1990)…………………….. ……………….…4 2.2. Water chiller system schematic………………………… …………………….5 2.3. Schematic of an air conditioning system showing connecting points between components…………………………………………………………..10 2.4. Flow diagram for model implementation computer program ...………………13 2.5. Information flowchart for model implementation (Jin 2002)…………………14 3.1. Overall hierarchy of EnergyPlus………………………………………………18 3.2. EnergyPlus Nodal Connections (EnergyPlus guide for module developers (EnergyPlus2002)………….……..19 3.3. HVAC loop connections for a water to water heat pump simulation ……...….20 3.4. HVAC loop connections for a water to air heat pump simulation ……………21 3.5. Schematic of blowthrough Water to air heat pump …………………………22 4.1. Manufacturer’s catalog for Florida Heat Pump GT030………...……………..25 4.2. Water to air heat pump cycle…………………………...……………………..29 4.3. Informational flowchart for model implementation………………………......34 4.4. Correspondence required between the IDD and the IDF…..………………….37 4.5. Input object for the simple water to air heat pump model defined in the IDD..39 4.6. Input object for the heating and cooling coils as defined in the IDD ……..…40 4.7. Hierarchical flow of simulation……………………………………………….42 4.8. Flow of code for the simple water to air heat pump model…………...………44 5.1. Catalog cooling capacity v/s calculated cooling capacity (heat pump #1) …....47 5.2. Catalog power consumption v/s calculated power consumption viii (heat pump #1)…………………………………………………………………48 5.3. Catalog sensible capacity v/s calculated sensible capacity (heat pump#1) ....48 5.4. Catalog v/s calculated heat transfer rate (heat pump#1)………………….…..49 5.5. Catalog cooling capacity v/s calculated cooling capacity (heat pump #2)……49 5.6. Catalog power consumption v/s calculated power consumption (heat pump #2)…………………………………………………………………50 5.7. Catalog sensible capacity v/s calculated sensible capacity (heat pump#2) ….50 5.8. Catalog v/s calculated heat transfer rate (heat pump#1)……………….……..51 5.9. Catalog heating capacity v/s calculated heating capacity (heat pump #1) …...52 5.10. Catalog power consumption v/s calculated power consumption (heat pump #1)…………………………………………………………….……52 5.11. Catalog v/s calculated heat transfer rate (heat pump#1) ……………………....53 5.12. Catalog heating capacity v/s calculated heating capacity (heat pump #2) ……53 5.13. Catalog power consumption v/s calculated power consumption (heat pump #2)…………………………………………………………….……54 5.14. Catalog v/s calculated heat transfer rate (heat pump#1) ………………….…..54 5.15. Influence of air flow correction factors on performance…………………..…....56 5.16. Influence of wet bulb correction factors on performance…...……………..…....56 5.17. Correction factors for GC ClimateMaster series…………………………...…...59 5.18. Comparison of catalog cooling v/s calculated cooling capacities with and without the correction factors for wet bulb temperatures………….….61 5.19. Comparison of catalog cooling v/s calculated sensible cooling capacities with and without the correction factors for wet bulb temperatures……….….…62 5.20. Comparison of catalog cooling v/s calculated power consumption with and without the correction factors for wet bulb temperatures………….….62 5.21. Comparison of catalog cooling v/s calculated cooling capacities with and without the correction factors for variable air flow rates………….…..63 5.22. Comparison of catalog cooling v/s calculated sensible cooling capacities ix with and without the correction factors for variable air flow rates……….….…64 5.23. Comparison of catalog cooling v/s calculated power consumption with and without the correction factors for variable air flow rates………….….64 5.24. Comparison of catalog v/s calculated cooling capacity for all points with the correction factors(simplified model  equation(415))…………………66 5.25. Comparison of catalog v/s calculated sensible capacity for all points with the correction factors(simplified model equation(417))………………...66 5.26. Comparison of catalog v/s calculated power consumption for all points with the correction factors(simplified model equation(415))……................…67 6.1. Florida Heat Pump (GT018) catalog data……………………………………….70 6.2. ClimateMaster catalog data………………..…………………………………….72 6.3. Trane heating catalog data…………………….......…………………………….74 6.4. Trane cooling catalog data…………………….......…………………………….75 6.5. Isometric north east view of the building plan………………………………….76 6.6. Schematic of the building and system plan implemented in EnergyPlus………..78 6.7. Annual hourly loads for the example building in Chanute,IL……………...……80 6.8. Building load profile for the winter design day…………………………….……81 6.9. Building load profile for the summer design day……………………….….……81 6.10. Demands for the summer design day……………………….……………………83 6.11. Fan electric power consumption (cooling mode)………………………………...84 6.12. Cooling demands met by the heat pump……………………………..…………..85 6.13. Controlled zone temperatures (cooling mode)………………………..………….86 6.14. Demands for the winter design day……………………….…………………..…87 6.15. Fan electric power consumption (heating mode)………………………………...87 6.16. Heating demands met by the heat pump……………………………..…………..88 6.17. Controlled zone temperatures (heating mode)……………………..…………….88 6.18. Comparison of demand v/s met in the cooling mode……………………………90 x 6.19. Comparison of demand v/s met in the heat ing mode……………………………90 6.20. Comparison of duty factor v/s power consumption in the cooling mode……….91 6.21. Comparison of duty factor v/s power consumption in the heating mode……….92 6.22. Heating mode under off design conditions………………………………………93 6.23. Cooling mode under off design conditions………………………………………93 7.1. Application window of the performance coefficient calculator………...……….97 7.2. Window when the user clicks on cooling mode………...………………………..98 7.3. The main interfacing window………………………...…………………………..99 7.4. A typical model Input file…………..……………………………………...……101 7.5. A typical model definition file…………………………………………..……..102 7.6. Preprocessed interface window……………………..…………………………..103 7.7. Output window with the coefficients and comparison plots…………...…...…...105 xi LIST OF TABLES Table Page 4.1 Manufacturer’s catalog for Florida Heat pump GT030 in the cooling mode.…..26 4.2 Manufacturer’s catalog data for cooling mode…………..…………….………30 4.3 Implementation of the simple water to air heat pump model..…………………36 5.1 List of heat pumps in cooling mode for model verification……………….…... 46 5.2 List of heat pumps in heating mode for model verification……………………52 5.3. Correction factors for the air flow rate………..…………………………...……55 5.4. Correction factors for the wet bulb temperatures..………………………...……55 5.5. Range of wet bulb temperatures and air flow rates for GC series………………60 5.6. Comparison of model RMS errors for ClimateMaster HS006…………………..67 6.1. Distribution of coefficients in model equations………………………………...82 xii NOMENCLATURE PLR = Part load ratio() QE = Evaporator cooling load Btu/h (W) QENOM = Nominal evaporator cooling load Btu/h (W) PNOM = Nominal Compressor power Btu/h (W) P = Compressor power Btu/h (W) ANCR = Available to nominal capacity ratio() FFL = Fraction of full load power() FLPR = Full load Power ratio() PFL = Full load compressor power Btu/h (W) E = Energy rate Btu/h (W) f = Functional relationship P = Pressurepsia(Pa) m& = Mass flow ratelbm/h(kg/s) T = TemperatureoF(oC) X = Refrigerant quality() U = Energy flow rate Btu/h (W) R = gas constant(J/K.s) PD = Piston displacementCFM(m3/s) C = Clearance factor() dis P = Discharge pressure psia(Pa) suc P = Suction pressure psia(Pa) = Isentropic exponent() xiii hcoAo = External heat transfer coefficientBtu/ oF(J/K) a m& = Air mass flow ratelbm/h(kg/s) a Cp = Specific heat of airBtu/lbm oF(J/kgK) • s Q = Source side heat transfer rate Btu/h (W) • L Q = Load side heat transfer rate Btu/h (W) •W = Compressor power input Btu/h (W) cat W = Catalog power consumption Btu/h (W) W = Model power consumption Btu/h (W) cat QL = Catalog load side heat transfer Btu/h (W) QL = Model load side heat transfer Btu/h (W) TWiL = Entering water load side temperatureoF(oC) TWiS = Entering water Source side temperatureoF(oC) WiL m • = Entering water load side mass flow rateoF(oC) WiS m • = Entering water Source side mass flow rateoF(oC) S = Thermostatic Signal Tref = Reference Temperature(K or R) TWin = Entering water temperature (K or R) Tdb = Dry bulb air temperatures (Kor R) Twb = Wet bulb air temperatures (Kor R) Qbase = Base capacity of the heat pump unit(W) Qc = Cooling capacity (W) Qh = Heating capacity (W) A = Polynomial regression coefficient() B = Polynomial regression coefficient() xiv C = Polynomial regression coefficient() CT = Polynomial regression coefficient() LT = Polynomial regression coefficient() QT = Polynomial regression coefficient() A2 F2 = Equation fit coefficients for the heating mode() A1 J1 = Equation fit coefficients for the cooling mode() 1 CHAPTER 1 INTRODUCTION 1.1. Background Watertoair ground source heat pump systems have been an ideal choice for design engineers since they provide a promising ecofriendly alternative for heating and cooling of residential and commercial buildings. These units accept energy from or reject energy to a common ground loop depending on whether the zone has a requirement for heating or cooling. The single package reverse cycle watertoair heat pump uses ground as the heat source in the heating mode. In the cooling mode, the ground acts as the heat sink by the application of a refrigerant reversing valve. An expansion device maintains the pressure difference between the high pressure condenser side and the low pressure evaporator sides of the watertoair heat pump refrigeration system. In heating mode, the refrigerant under high pressure is directed through the refrigerant to air heat exchanger and the closed loop heat exchanger absorbs heat from the ground (typically 55F to 70F). Heat transfers to the refrigerant via the water to refrigerant heat exchanger and the condensation of the refrigerant results in heating. In the cooling mode, the high pressure refrigerant is channeled through the water to refrigerant heat exchanger connected to the ground loop and cooling is provided by the evaporation of the refrigerant in the refrigerant to air heat exchanger. With increasing energy costs, many potential heat pump applications require frequent reassessment in terms of design and modeling. Modeling heat pumps for design and simulation is significant since it allows cost effective design solutions and permits the designer to quantitatively compare a variety of design strategies. Also, it 2 facilitates modifications at any stage in the design process before the final documents are produced. 1.2.Thesis objective and scope The objective of the thesis is to develop a simplified model that can simulate a watertoair ground source heat pump and implement the model in EnergyPlus. EnergyPlus (Crawley et al; 1997) is a new energy analysis and thermal simulation engine capable of performing subhourly simulation of the building, fan system and the ground source heat pump system. It is a highly modularized, platform independent engine. It is developer friendly and is based on the best features of BLAST (BSO, 1991) and DOE2 (LBNL, 1980). The steady state behavior and performance characteristics of the water loop heat pump system developed by Lash (1990) for the BLAST program have been investigated. The model which includes a ground loop configuration is significantly modified for implementation in the EnergyPlus program. Also, the model is extended to calculate sensible and latent capacity splits for the heat pump while operating in the cooling mode. The performance of the simplified model is compared with the parameter estimation watertoair heat pump model (Jin 1999) to determine the usefulness of implementing a simple model. The simplified and the detailed models are compared in a case study and the results are summarized and analyzed. The performance data generated using the model is compared with the manufacturer’s catalog data to check the validity of the simulation. A visual basic graphical user interface that generates performance coefficients from manufacture’s data was also developed. 3 CHAPTER 2 LITERATURE REVIEW One of the biggest challenges facing the simulation and design of performance oriented ground source heat pump systems is the degree of complexity involved in modeling the individual components of the system. The challenge also impinges on the successful implementation of the model in a computer program. Several heat pump models have been implemented in the past. However a detailed review of the literature uncovers a few limitations in the existing models. 2.1.Equation fit watertoair heat pump and chiller models 2.1.1. Lash model Lash(1990) developed a model for a water loop heat pump system(WLHPS) in which a network of zoned reversible packaged water source heat pumps operate independently of each other to control individual zone loads. These units are capable of supplying both heating and cooling. Fig 2.1 is an illustration of the simplified loop model used by Lash. The water loop is modeled by breaking up the loop into two sections or nodes where node1 represents the water mass between the central plant outlet and the first heat pump inlet and node2 represents the water mass between the first heat pump inlet to the central plant inlet. The Lash water loop heat pump model as implemented in the BLAST energy analysis program uses four nondimensional performance equations to describe the heat pump as discussed in chapter 4. 4 Figure 2.1. Heat pump system nodes (Lash 1990) The Heat pump performance is based on entering air temperatures, entering water temperatures and the inlet mass flow rate of water. Coefficients for the nondimensional equations are obtained by a least square fit of manufacturer’s data to the form of the equation. The biggest advantage of the model is that the data is readily available. Also, since refrigeration properties are not required the model is very robust, resulting in quick execution time. However the model is not applicable beyond a particular data range and it also fails to account for the latent and sensible capacity splits in the cooling mode. 5 2.1.2. Allen and Hamilton model Allen and Hamilton (1983) developed a steady state reciprocating compressor water chiller model. Fig 2.2 shows a schematic of the water chiller with the basic pieces of equipment and the variables labeled at appropriate points. MC QC QE ME P TC1 TC2 TE1 TE2 TD TS Evaporator Condenser Expansion Compressor Valve QL Figure 2.2. Water chiller system schematic The equation fit model is capable of simulating the energy rate characteristics of the system under full load and part load conditions. The evaporator heat transfer rate and the compressor power are expressed in terms of a polynomial which is a function of the evaporator and condenser exit temperatures. The coefficients are determined from experimental data by polynomial regression. Given a water chiller, an evaporator inlet water temperature, TE1, an evaporator waterside mass flow rate, ME, a condenser inlet water temperature, TC1, and a condenser waterside mass flow rate, Mc, then the five system equations can be given as follows. The evaporator heat transfer rate is given by 6 ( ) 2 1 Q m C TE TE E = & E p ……………………………………………..(2.1) 6 2 5 2 2 1 2 2 2 3 2 2 4 2 QE b T E b T C b T E T C b T E b T C b = + + + + + ………..……. (2.2) And the compressor power is 12 2 11 2 2 7 2 8 2 9 2 2 10 2 P b T E b T C b T E T C b T E b T C b = + + + + + ……….…….(2.3) Also, the condenser heating rejection rate is Q Q P C E = + …………………………………………….………...(2.4) ( ) C c p C2 C1 Q =m& C T T ……………………………………………..(2.5) Where b1b12 = Coefficients fitted by polynomial regression TE1,TE2, = Evaporator water entering and leaving temperature TC1,TC2 = Condenser water entering and leaving temperature Ts, Td = Refrigerant temperature at compression suction and discharge Cp = Specific heat at constant pressure P = Compressor power QE = Evaporator heat transfer rate QC = Condenser heat transfer rate E m& = Evaporator mass flow rate C m& = Condenser mass flow rate Although the model does not include the complexity of modeling individual components it is not a useful model due to the fact that its two main biquadratic equations are based on condenser and evaporator outlet temperatures which are not readily available from catalog data. In addition to this, the model neglects the power losses QL, shown in Figure 2.2, assuming well designed systems available from the major manufacturers. 7 2.1.3. DOE2 Model The water chiller model used in the DOE2 computer simulation program (DOE2 Engineers Manual, 2002) is the simplest model with a minimum number of performance coefficients. The DOE2 model eliminates the dependency of water chiller performance on condenser and evaporator temperatures. Here, the power consumption is fit as a quadratic function of the evaporator heat transfer rate, QE. The part load ratio is given as ENOM E Q Q PLR = ………………………………...……………..(2.6) C L PLR Q PLR2 P P T T T Nom = + + …………................................(2.7) Where PLR = Part load ratio QE = Evaporator heat transfer rate QENOM = Nominal evaporator heat transfer rate PNOM = Nominal Compressor power CT, LT, QT = Polynomial regression coefficients P = Compressor power Although the model exhibits simplicity with only two equations and four constants, it does not account for the effect of temperature variations on performance. This results in a large predictive error. 2.1.4. BLAST Model The BLAST chiller model (BLAST Users Manual, 1991) slightly improves on the DOE2 model. The model was actually developed to counter the demerits of the DOE2 model. This model accounts for variations in performance due to condenser 8 and evaporator temperatures. The model is based on the observation that under full load, the constant evaporator heat transfer rate lines are approximately linear and parallel. It also introduces a variable known as the equivalent temperature difference, T, which is an expression involving the user input condenser and evaporator exit temperatures, TC2 and TE2, and the slope, k, of the constant evaporator heat transfer rate at full load performance. The available capacity to nominal capacity ratio, ANCR, is modeled as a quadratic function of T. The water chiller power PFL is modeled as a function of ANCR. ( ) ( ) 2 2 2 2 E E NOM C C NOM T T k T T T = ……………………………………..(2.8) b1 bb2. TT 3. 2 Q Q ANCR ENOM EFL = = + + …………………………….…….(2.9) (c1 c2.ANCR c3.ANCR2 ) Q P Q PFL FLPR ENOM NOM EFL + + = = …………….……(2.10) EFL E Q Q PLR = ……………………………………………………….……...(2.11) a1 a2.PLR a3.PLR2 PFL P FFL = = + + …………………………….……..(2.12) P = RatedCOP ANCR Q FLPR FFL ENOM * * * ………………………………………...(2.13) Where a1a3, b1b3, c1c3 = Polynomial regression coefficients ANCR = Available capacity to nominal capacity ratio FFL = Fraction of full load power FLPR = Full load Power ratio PFL = Full load compressor power PLR = Part load ratio 9 QEFL = Full load evaporator heat transfer rate PLR = Part load ratio QE = Evaporator heat transfer rate QENOM = Nominal evaporator heat transfer rate PNOM = Nominal Compressor power P = Compressor power 2.1.5. Hamilton and Miller model The Hamilton and Miller model (1990) is a typical detailed equation fit model. The steady state model incorporates functional fits of manufacturers’ catalog performance data of various individual standard components including evaporators, compressors, condensers, capillary tubes and fans. The system model shown in Fig 2.3 is a combination of mathematical models of individual components with mass and energy flow at each component connection and conformity of pressure/temperature values at each component connection. The equations expressing the behavior and operation of each component are based on assumed steady state conditions. The components are connected such that the input to one component is the output of the previous component and the values of the state variables are updated by each component model. Each node is primarily characterized by the pressure and temperature of the refrigerant. Together the equations form a set of nonlinear simultaneous equations depicting the response of the vapor compression system to fan inlet conditions. The computer model is valuable for simulating the response of air conditioning systems for a range of ambient and inside conditions. Although the model provides the flexibility to specify various coils, compressors and capillary tubes, it is important to notice that comprehensive individual component performance data is not readily available. 10 P10 T10db T10wb U10 10 9 P8 T8db T8wb U8 8 P9 T9db T9wb U9 Condenser Condenser fan 1 Compressor qc Wcd Wcp P1 T1 U1 4 Evaporator qe Evaporator fan P7 T7db T7wb U7 7 mw7 P6 T6db T6wb U6 6 mw6 P5 T5db T5wb U5 mw5 X4 mr4 T4 U4 P3 T3 U3 3 mr3 P2 2 T2 U2 mr2 Capillary tube 5 Figure 2.3. Schematic of an air conditioning system showing connecting points between components 2.1.6. Parent and Larue development of the Domanski Model Parent and Larue(1989) developed a steady state watertoair heat pump modeling program called SIMPAC which uses Domanski’s (1986) model of an airtoair heat pump as the starting point. The program involves a driver program that links the independent models of each system component together. Thermodynamic properties of pure refrigerants and zeotropic mixtures are evaluated by the application of Carnahan Starling Desantis (CSD) equation of state. The SIMPAC program requires detailed input for the individual components of the heat pump. The SIMPAC algorithm is computationally intensive and may not converge. 11 2.2. Jin and Spitler’s parameter estimation watertoair heat pump models 2.2.1 Overview Jin and Spitler (2002) proposed a steady state simulation model for watertowater and watertoair heat pumps. The model uses a multivariable unconstrained optimization algorithm to estimate a number of parameters. The aim of the model is to determine the geometric parameters and operation of each component and replicate the performance of the actual unit in operation. The model algorithm and the equations are shown in Fig 2.4. Where TWiL = Entering water Load side temperature TWiS = Entering water Source side temperature WiL m • = Entering water Load side mass flow rate WiS m • = Entering water Source side mass flow rate Tc = Condenser temperature Te = Evaporator temperature S = Thermostatic Signal mwl = Load Mass flow rate mws = Source Mass flow rate QL = Load side Heat transfer rate QS = Source side Heat transfer rate The parameters included in the model are shown in Figure 2.5 where: PD = Piston displacement C = Clearance Factor 12 P = Pressure drop across the suction and discharge valves = Loss factor Tsh = Superheat in C or F loss W = Constant part of the electromechanical losses (UA)S = Source side heat transfer coefficient (UA)L = Load side heat transfer coefficient (hco,Ao) = External heat transfer coefficient[Cooling mode only] 13 Parameters Inputs Enter the initial guesses for 1)Heat transfer rate in condenser 2)Heat transfer rate in evaporator Effectiveness for evaporator and condenser = pw wL L L C m UA 1 exp = pw wS s s C m UA 1 exp Temperatures for evaporator and condenser L pw wL L e wiL C m Q T T • = S pw wS S c wiS C m Q T T • = + Is Evaporator pressure < Low pressure cutoff Condenser pressure > High pressure cutoff End yes no Identify Condenser and evaporator exit points Apply suction and discharge drops P P P suc = e P P P dis c = + Is Suction pressure < Low pressure cutoff Discharge pressure > High pressure cutoff End yes no Find refrigerant mass flow rate 1 (1 ( ) suc dis suc P P C C PD m = + • Find Power consumption Cooling capacity Heat rejection Is End yes Abs QS QS QS err Abs QL QL QL err guess guess guess guess < < ( / ( / NO Figure 2.4. Flow diagram for model implementation computer program 14 Parameter estimation based Watertoair Heat Pump Tdbil Twbil Vil Twis mwiS S Tdbol Twbol Vol Twos mwos W F (UA)s (UA)l PD C P Tsh Wloss Phi Plo (UA)s (UA)l PD C P Tsh Wloss Phi Plo hcoAo Inputs Outputs Heating Mode Parameters Cooling Mode Parameters Figure 2.5. Information flowchart for model implementation (Jin 2002) 2.2.2. Parameter estimation procedure A set of parameters for the cooling mode are defined on the basis of the equations used in the model. An information flowchart indicating the parameters, inputs to the model and the resulting outputs are shown in Fig 2.5. The estimation of parameters is conducted using the catalog data. The parameter estimation procedure incorporates an objective function which computes the difference between the model outputs and the catalog outputs. The objective function is then minimized by using a multivariable, unconstrained, multimodal, NelderMead optimization algorithm. The inputs to the model include the entering water temperatures and mass flow rates on the load side 15 and the source side. The sum of the square of the errors (SSQE) for a given set of parameter values which will be minimized is given by 2 1 2 + = = cat cat i i cat cat i QL QL QL W W W SSQE ………………………..(2.23) Where cat W = Catalog power consumption Btu/h (W) W = Model power consumption Btu/h (W) cat QL = Catalog load side heat transfer Btu/h (W) QL = Model load side heat transfer Btu/h (W) i = Number of points 2.2.3. Model implementation A thermostat signal is used as an input parameter to tell the model which set of parameters (heating mode or cooling mode) should be used. Also, the objective function evaluation takes advantage of the fact that the heat transfer rates are known, using the catalog data as an initial guess, then minimizing the difference between the calculated and catalog heat transfer rates. However, for the model implementation, the heat transfer rates are obtained by simultaneous solution with successive substitution. An information flow chart of the model implementation is presented in Figure 2.5. Extrapolation beyond the catalog data grants the parameter estimation model an upper hand in comparison with the equation fit models. However, the model although advantageous, carries a high overhead cost associated with repeated calls to refrigerant data routines. This makes the process computationally more intensive and time consuming. In addition, refrigerant properties may go out of bounds while the model attempts to converge on the final solution. Running the model successfully in a 16 general simulation environment requires considerable effort to hold the refrigerant properties within bounds for every iteration of the simulation. Finally the convergence on a valid set of parameters is largely dependent on the initial guesses. This places an undue burden on users who typically have little knowledge of appropriate ranges for the model parameters. 17 CHAPTER 3 OVERVIEW OF ENERGYPLUS 3.1.Introduction EnergyPlus (Crawley et al; 1997) is a new energy analysis and thermal simulation engine developed in Fortran 90. It is capable of modeling HVAC systems, central plants, ideal controls and building heat transfer. It is a highly modularized, platform independent engine that was originally based on the best features of BLAST (BSO1991) and DOE2 (LBNL1980). Program features include variable time step, user configurable modular systems and an integrated system / zone simulation. Recently, a number of components have been developed and implemented in the program to support ground source heat pump analysis. Rees(2002) implemented the shallow pond model developed by Chiasson(1999). Murugappan(2002) implemented the vertical ground loop heat exchanger model developed by Yavuzturk and Spitler(1999). Murugappan(2002) also implemented the watertowater heat pump model developed by Jin and Spitler(2002). Most recently, the watertoair heat pump model developed by Jin and Spitler has been implemented in EnergyPlus. 3.2.Simulation environment Figure 3.1 shows the overall hierarchy of EnergyPlus. On the whole, the simulation environment is based on fundamental heat balance principles and is a simultaneous solution of the coupled building, system and the plant simulations. 18 Input Building Description Simulation Manager Heat Balance Manager Surface Heat Balance Manager Air Heat Balance Manager Input Building Use Information Input Building Thermostatic control Information Input HVAC Operation and Central plant schemes HVAC Manager Air loop simulation Plant loop simulation Zone equipment simulation Convergence Condenser Loop simulation HVAC Iteration No Figure 3.1. Overall hierarchy of EnergyPlus 19 Figure 3.2. EnergyPlus nodal connections (EnergyPlus guide for module developers (EnergyPlus2002) The HVAC simulation in EnergyPlus is loop based (Fisher, 1999). Equipment such as boilers, chillers, thermal storage tanks and watertowater heat pumps are simulated on a “Plant loop”. Environmental heat exchangers such as cooling towers, ground loop heat exchangers, pavement heat exchangers and ponds are simulated on a “Condenser loop”. Heating coils, cooling coils as well as unitary equipment such as watertoair heat pumps and airtoair heat pumps are simulated on an “Air loop”. The components are connected to the loops by defining nodes at the connections as shown in Fig 3.2 .The nodes are in turn defined in the FORTRAN program as data structures which hold state variable and control information for the node location on the loop. 20 3.3.Water source heat pumps in EnergyPlus 3.3.1. Watertowater heat pump simulation Fig 3.3 shows the HVAC loop connections for a watertowater heat pump simulation in EnergyPlus. The watertowater heat pump is coupled both to the plant loop and the condenser loop as shown in the Figure. Zone Water to Water Heat Pump Zone/AirLoop Condenser Loop Ground loop Heat exchanger Condenser demand side Condenser supply side Plant Loop Plant demand side Plant supply side Figure 3.3. HVAC loop connections for a watertowater heat pump simulation Plant Loop The plant loop is divided into two sections, so that it can be simulated using a successive substitution solver. The demand side couples coils and other components to the zone/air loop, and the supply side models energy conversion equipment such as 21 boilers, chillers and heat pumps. The plant supply side supplies hot or cold water to meet the demands of the plant demand side and controls the flow rate and the temperature of the loop. 3.3.2. Watertoair heat pump simulation Fig 3.4 shows the HVAC loop connections for a watertoair heat pump simulation in EnergyPlus. The watertoair heat pump is coupled directly to the condenser loop. The system consists of two loops, the Zone/Air loop and the Condenser loop. Zone Water to Air Heat Pump Zone/AirLoop Condenser Loop Ground loop Heat exchanger Condenser demand side Condenser supply side Figure 3.4. HVAC loop connections for a watertoair heat pump simulation Zone/Air loop The watertoair heat pump model is called from the zone air loop manager. The zone thermostat turns the heat pump on or off depending on the sensible demand for that zone. The most recent source side inlet temperature is taken from the condenser loop and the water flow request based on the air loop simulation is passed as a demand to the condenser loop. Condenser Loop 22 The condenser loop is divided into two sections, the demand side where energy is transferred to the air stream by various components in the zone/air loop and the supply side where energy is transferred to the environment by various components. The loop attempts to meet the flow request made by the heat pump. The condenser loop temperature is determined by the ground loop heat exchanger simulation. The overall HVAC simulation manager calls the zone/air loop and the condenser loop successively until convergence is achieved. 3.4.Implementing Watertoair heat pump models in EnergyPlus In EnergyPlus, the unitary watertoair heat pump is a virtual component that consists of a single speed fan, a cooling coil, a heating coil and a gas or electric supplementary heating coil as shown in Figure 3.5. Fan Auxiliary Cooling Coil Heating Coil Heating Coil Figure 3.5. Schematic of Blow thru Watertoair heat pump virtual component The heat pump components are accessed with a single call to the unitary equipment manager from the air loop manager. Air properties are evaluated at nodes between each of the heat pump components. The outlet node of one model forms the inlet node to the next model within the virtual component. The virtual component is connected to the air loop as specified in the user generated input file. This means that the inlet node for the heat pump is also the inlet node for the fan component. In the draw thru configuration, the fan will be located between the coils and the supplemental heating coils. Although it is convenient to cycle the heat pump at the system time step, 23 typically, EnergyPlus time steps will be too long to avoid overheating or overcooling the zone using this control scheme. EnergyPlus, therefore, approximates cycling as a part load by adjusting the system air flow rate to meet the demand of the controlling (thermostat) zone. The fraction of total system volumetric airflow that goes to the controlling zone along with the controlling zone load determines the total load that must be met by the heat pump as: ControlZoneAirFlowFraction ControlZoneHeatingLoad HeatPumpHeatingLoad = …………………….…(3.1) ControlZoneAirFlowFraction ControlZoneCoolingLoad HeatPumpCoolingLoad = …………………..…...(3.2) The “part load” system flow rate is computed on the basis of the heat pump heating or cooling load. The run time fraction of the heat pump in its heating/cooling mode is estimated by the ratio of the sensible heating/cooling demands to the base heating/cooling capacities. The base capacities can be determined from the manufacturers’ catalog data. The equation fit performance model discussed in Chapter 4 is used to determine performance variables such as the heating/cooling capacities, power consumption, energy efficiency ratio and the coefficient of performance. The actual energy extracted or provided is then calculated as the product of the equation fit performance variables for energy extracted or provided and the run time fraction for the respective modes. The power consumption is computed in the same manner. The output data is then moved from the heat pump data structures to the node data structure and are readily available to other computational and reporting modules. 24 CHAPTER 4 MODEL DEVELOPMENT AND IMPLEMENTATION 4.1. Development of the watertoair heat pump model The model implemented in EnergyPlus is based on the water loop heat pump system model developed by Lash (1990). This section describes the development of the model equations and how they were incorporated in the EnergyPlus simulation engine. 4.2. Design basis Water source heat pumps are required to meet the standards of ARI or ISO 132561. The certification program rates water source heat pump performance at specified entering water temperatures. The standard simplifies the use of rating data for heat pump performance modeling in energy analysis calculations and allows for direct rating comparisons across applications. High efficiency water source heat pumps are designed to operate over a range of entering water temperatures in either the heating or the cooling mode. The manufacturer’s catalog provides heat pump performance data within a specific range of entering fluid temperatures. The catalog provides sufficient data to develop a correlation for heat pump performance as a function of entering water temperature, entering air temperatures and inlet water mass flow rates. The catalog data were used to fit the coefficients for the watertoair heat pump described later in this chapter. A typical set of manufacturer’s data for the Florida heat pump in the heating and cooling mode is shown in Figure 4.1. 25 FHP GT030 Data All performance at 1000 CFM and 7.5 GPM Entering Ent. Air Total H e a t Sensible Capacity BTUH EER Fluid Wet Bulb Capacity Watts ERnet.j e c t i o n Air Dry Bulb Temp. Temp. Temp. BTUH Input BTUH 75o 80o 85o 50 61 29514 1209 33639 22460 26946 29514 24.4 50 64 30944 1236 35162 21011 26674 29583 25 50 67 32399 1263 36709 19342 25271 29159 25.7 50 70 33878 1290 38280 16126 22292 29000 26.3 50 73 35381 1318 39877  19000 26041 26.9 60 61 28380 1360 33022 21597 25911 28380 20.9 60 64 29756 1391 34501 20204 25650 28447 21.4 60 67 31155 1421 36003 18599 24301 28039 21.9 60 70 32577 1452 37530 15507 21436 27886 22.4 60 73 34022 1482 39080  18270 25041 23 70 61 27246 1512 32404 20735 24876 27246 18 70 64 28567 1545 33840 19397 24625 27310 18.5 70 67 29910 1579 35298 17857 23330 26919 18.9 70 70 31276 1613 36779 14887 20580 26772 19.4 70 73 32664 1647 38284  17540 24040 19.8 85 61 25546 1739 31478 19441 23324 25546 14.7 85 64 26785 1777 32848 18187 23088 25606 15.1 85 67 28044 1816 34240 16742 21874 25239 15.4 85 70 29324 1855 35654 13958 19295 25101 15.8 85 73 30625 1894 37089  16446 22540 16.2 100 61 23846 1966 30552 18147 21771 23846 12.1 100 64 25002 2009 31857 16976 21552 23902 12.4 100 67 26177 2053 33182 15628 20418 23560 12.8 100 70 27372 2097 34528 13029 18011 23431 13.1 100 73 28587 2142 35894  15351 21040 13.3 Figure 4.1. Manufacturer’s catalog for Florida Heat Pump GT030 Using manufacturer’s catalog data in the model equations requires conversion since the units vary for the input parameters. For example, in Figure 4.1 British IP units are used for all reported parameters except the power input which is in SI units. The catalog data must be converted to the units required by the model equations—SI units in the case of the EnergyPlus model. The converted catalog data for the Florida heat pump GT030 in the cooling mode is shown in Table 4.1. 26 Table 41.Manufacturer’s catalog for Florida Heat Pump GT030 in the cooling mode Since it is extremely cumbersome to obtain data at every single point within the range specified, manufacturers often assume rated flow rates or temperatures for convenience. Different manufacturers assume different input variables as rated constants. For example, the Florida heat pump catalog assumes rated air flow rates and water flow rates, whereas the ClimateMaster heat pump catalog creates the set of catalog data at constant wet bulb temperatures over a range of water flow rates. The heat pump model was developed to accommodate both data formats. In Table 4.1 All performance data is obtained at a constant air volumetric flow rate of 0.471 m3/s and a constant water mass flow rate of 0.47 kg/s. A detailed explanation of the methodical approach in using manufacturer’s data along with a list of different heat pump manufacturers is provided in Chapter 5. Following Lash, Heat pump performance based on entering air temperatures, entering water temperatures and inlet water mass flow rate were developed as follows: 27 4.2.1. Cooling Mode Equations + = + wbase w wb ref ref Win base c m m T T C1 T T A1 B1 Q Q & & ………………………...(41) + = + wbase w wb ref ref Win base m m T T F1 T T D1 E1 EER EER & & ..…..……...……….….(42) + + = + wbase w db ref wb w wbase ref ref Win Sensbase Sens m m T T J1 T m m T I1 T T G1 H1 Q Q & & & & … (43) Ql c Sens Q =Q …………………………………………….……………(44) Where: A1 J1 = Equation fit coefficients for the cooling mode Tref = 283K or 511 R TWin = Entering water temperature (K or R) w m • = Mass flow rate of water through the heat pump w base m • = Base mass flow rate of water through the heat pump Tdb,Twb = Entering Dry bulb and wet bulb air temperatures (K or R) Qbase = Base capacity of the heat pump unit(W) Qc = Cooling capacity (W) Ql = Latent cooling capacity (W) Qsens = Sensible cooling capacity (W) In cooling mode the heat pump rejects heat to the ground loop. The power input and the heat rejected by the heat pumps are functions of the entering water temperature, the water mass flow rate and the entering air temperature. The power input to the heat 28 pump is computed from the energy efficiency ratio(EER), which is defined as the ratio of net cooling capacity to the total input rate of electric energy, as follows: EER Q P = c …………………………………………(49) The amount of heat rejected to the loop is given by Equation(410) EER Q Q Q c Reject c= + ………………………………..(410) Equations 4.1 4.3 take into account critical heat pump performance parameters and are cast in a form that accommodates both variable wet bulb and variable water flow rate data. By defining a reference temperature and a ‘base case’, the equations are cast in nondimensional form. For the FHP cooling mode catalog data shown in Figure 4.1, the sensible capacity is a function of wet bulb temperatures and the dry bulb temperatures while the total capacity is a function of the wet bulb temperature only. On a psychrometric chart, the lines of constant air enthalpy follow almost exactly the lines of constant wet bulb temperature. Therefore wet bulb temperature variation, which can be easily measured, provides a relatively accurate measurement of the enthalpy difference: h WB …………………………………….(4.5) The air (‘load side’) coil heat transfer rate is directly proportional to this change in enthalpy. The inlet air wet bulb temperature is therefore an appropriate scale for the air side heat transfer rate in the total capacity equation (41). For a given heat pump (specified coil geometry, compressor and expansion device), the air and water inlet conditions as shown in Fig 42, completely determine the total operating capacity of the unit. The inlet water temperature is therefore an appropriate scale for the source side heat transfer rate. 29 Twin mW Twout Twbin Tdbin Twbout Tdbout Air (‘Load’) Side Water (‘Source’) Side Figure 4.2. Water to air heat pump cycle (cooling mode) When the load increases, the entering wet bulb temperature increases. If the heat pump has to meet this increase in load, with a constant source side water mass flow rate, the water temperature has to be reduced. This establishes an inverse relationship between the entering water temperature and the entering wet bulb temperature as shown in equation 4.6. wb win T T 1 …………………………………………………….(4.6) The C1 term in equation (41) shows the expected relationship between the water mass flow rate and the entering wet bulb temperature as illustrated in Table 42. As the mass flow rate decreases the total cooling capacity and the sensible cooling capacity also decrease. This establishes a direct relationship between the heat pump capacities and the water mass flow rate as shown by equation 4.7. TC m& w ………………………………….…..(4.7) 30 Table 42.Manufacturer’s catalog data for cooling mode 0.9 GPM 1.1 GPM 1.7 GPM EWB EWT TC SC KW HR TC SC KW HR TC SC KW HR 60 60 6.52 5.42 0.46 9 6.69 5.42 0.45 9.1 6.86 5.52 0.43 9.2 65 60 7.33 4.47 0.47 9.1 7.52 4.47 0.46 9.2 7.71 4.56 0.43 9.3 66.2 60 7.49 4.19 0.47 9.2 7.69 4.19 0.46 9.3 7.89 4.27 0.44 9.4 67 60 7.60 4.00 0.48 9.3 7.80 4.00 0.47 9.4 8.00 5.20 0.44 9.5 70 60 7.97 3.21 0.49 9.4 8.18 3.21 0.48 9.5 8.39 3.28 0.45 9.6 Where: EWB= Entering wet bulb temperature (oF) EWT= Entering water temperature (oF) TC = Total cooling capacity (kBtu/hr) SC = Sensible cooling capacity (kBtu/hr) KW= Kilowatts input HR = Heat rejected to water (kBtu/hr) Table 42 also shows that as the mass flow rate decreases the sensible cooling capacity decreases. This establishes a direct relationship between the heat pump capacities and the water mass flow rates as shown by equation 4.8. SC m& w ……………………………………...(4.8) Since most data sets show either a range of entering wet bulb temperatures or a range of mass flow rates, but not both, the C1 term may introduce significant error in simulation applications where either the water mass flow rate or the entering wet bulb temperature will vary beyond the range of the single point shown in the catalog data. This concern is discussed in detail in Chapter 5. 31 4.2.2. Heating Mode Equations The Heating mode equations are analogous to the cooling mode equations. The dry bulb temperature is used in place of the wet bulb temperature and the COP is used in place of the EER as shown in equation (411) through (413). + = + wbase w db ref ref Win base h m m T T C2 T T A2 B2 Q Q & & ………………………...(411) + = + wbase w db ref ref Win base m m T T F2 T T D2 E2 COP COP & & ..…..……...……….….(412) The amount of heat transferred to the ground loop is given by: COP Q Q Q h absorb= h ……………………………………….(413) The power consumption is given by equation (414) COP Q P = h …………………………………………………(414) Where: A2 F2 = Equation fit coefficients for the heating mode Tref = 283K or 511 R TWin = Entering water temperature (K or R) •m = The mass flow rate of water through the heat pump Tdb,Twb = The dry bulb and wet bulb air temperatures (K or R) Qbase = The base capacity of the heat pump unit(W) Qh = Heating capacity (W) The base capacity is typically selected as the maximum capacity at which the heat pump unit can operate. Hence it can be sorted easily by looking at the peak values of 32 the capacity in the manufacturer’s catalog. The base mass flow rate is the water flow rate at the maximum capacity. Similarly, the base EER and COP are the respective values at the maximum capacity. 4.2.3. Accommodating Variable air flow rates Manufacturers rate the heat pump assuming a “constant volume” fan which means that the fan runs at a constant rpm and delivers a relatively constant volumetric flow rate over a range of air temperatures for a given duct configuration. For rating purposes, the manufacturers assume a zero pressure drop across the fan which is an approximated minimum for an independent water source unit without any ductwork. However, if the heat pump is simulated with rated air flow, predicted performance of the heat pump will be high. With no measured data readily available, air flow correction factors provided by the manufacturer are the only means of estimating the effect of offdesign air flow rates. These correction factors may be used to generate extra catalog points as discussed in the next chapter. In order to investigate the sensitivity of the performance variables at variable air flow rates, equations 4.14.3 are proposed and implemented in the modified form by including the air mass flow rates as follows: Cooling Mode: + = + wbase w wb ref ref Win base c m m T T C1 T T A1 B1 Q Q & & & & a a base m m ………………..(415) + = + wbase w wb ref ref Win base m m T T F1 T T D1 E1 EER EER & & & & a a base m m ..…..……...…. .(416) + + = + wbase w db ref wb w wbase ref ref Win Sensbase Sens m m T T J1 T m m T I1 T T G1 H1 Q Q & & & & & & a a base m m ……...(417) 33 Heating Mode: + = + wbase w db ref ref Win base h m m T T C1 T T A1 B1 Q Q & & & & a a base m m ………………………..(418) + = + wbase w db ref ref Win base m m T T F1 T T D1 E1 COP COP & & & & a a base m m ..…..……...……… .(419) Since only a few data points are available to verify this form of the model, it is proposed for future consideration, but was not implemented in EnergyPlus. 4.2.4. Determining the performance Ccefficients Performance coefficients in the heating and cooling modes for the equation fit model are determined by implementing a generalized least square equation fitting method. The method uses a minimal sum of the deviations squared from a given set of data. The performance coefficients A1J1 and A2F2 in equations (41) (42) (43) (411) and (412) are generated by this method. The coefficients are generated by using the available catalog data for the heating mode and the cooling mode. Since watertoair heat pump systems seldom operate at the catalog specified water temperature, a correlation for the heat pump performance as a function of the entering water temperature, wet bulb and dry bulb air temperature and mass flow rate must be derived from manufacturer’s data as discussed in the previous section. An information flowchart showing all the input parameters is shown in Figure 4.3. 34 Entering Air Temperature (K or R) Entering Water Temperature (K or R) Reference Temperature (K or R) Base Mass flow rate (kg/s) Entering Water Mass flow rate (kg/s) Cooling Mode Heating Mode Base Cooling Capacity (W) Base Heating Capacity (W) A1 B1 C1 D1 E1 F1 Base Energy Efficiency Ratio Base Coefficient of Performance A2 B2 C2 D2 E2 F2 COP Performance Coefficients Capacity Performance Coefficients Simple Water to Air Heat Pump Model Capacity Performance Coefficients EER Performance Coefficients Total Cooling Capacity(W) Total Heating Capacity(W) Power Consumption(W) Exit Water Temperature (K or R) Exit Air Temperature (K or R) Inputs Outputs Figure 4.3. Information flow chart 4.2.5. Performance coefficients calculation procedure For each unit at the specified operating conditions, the following catalog and input data is needed: • Sensible cooling capacity and Latent cooling capacity (Cooling Mode) • Heating capacity (Heating mode) • Energy efficiency ratio • Coefficient of performance • Nominal capacity of the heat pump unit • Nominal water mass flow rate • Inlet water mass flow rate • Entering air wet bulb and dry bulb temperature 35 • Entering water temperature • Nominal coefficient of performance of the heat pump unit • Nominal energy efficiency ratio of the heat pump unit Once the data at each operating point is obtained, they are input into a subroutine performing the LU decomposition of the n data points in the matrix. A procedure for decomposing a N x N matrix formed from n data points into a product of lower triangular matrix L and upper triangular matrix U is called the LU decomposition. The upper triangular matrix results from reducing the original matrix by way of elementary row operations excluding row interchange. The lower triangular matrix is created by storing a representative of each elementary row operation .The standard procedure for solving X for the linear system AX=B is shown in Appendix A. 4.3. Model implementation The model is implemented using the equations (41) and (44) for the cooling mode and (411) and (412) for the heating mode. The input/output reference specified in the EnergyPlus guide for module developers (2002) lists guidelines for new module implementation. The watertoair heat pump model was implemented as a single module with both heating and cooling subroutines. The single module approach with cooling/heating mode switching within the module makes the execution computationally less intensive than the detailed model. The zone sensible demand tells the model which set of coefficients (heating mode or cooling mode) and which input parameters must be used. The state variable information is read by the model from the loop nodes Table 43 shows the modifications made in accordance with the EnergyPlus standards to implement the watertoair heat pump simulation. EnergyPlus psychometric property routines were used to calculate the enthalpy, humidity ratios etc. 36 Table 43 Implementation of the simple watertoair heat pump model Source file/Program Modifications Purpose IDF IDD Added 2 Keywords UNITARYSYSTEM:HEATPUMP:SIMPLE COIL:WATERTOAIRHP:SIMPLE Defines the inputs for the simple watertoair heat pump model. Added 1 Module WATERTOAIRHPSIMPLE Encapsulates the data and algorithms required to manage the watertoair heat pump component 5 Subroutines SimsimpleWatertoAirHP Main calling subroutine at the top of the module hierarchy GetSimpleWatertoAirHPInput Obtains input data for the heat pump and stores it in heat pump data structures InitSimpleWatertoAirHP Initializes the watertoair heat pump components CalcSimpleWatertoAirHP Simulates the cooling and the heating mode of the watertoair heat pump EnergyPlus UpdateSimpleWatertoAirHP Updates the watertoair heat pump outlet nodes 4.3.1. Input configuration Energyplus is characterized by two formats of the input specification file, the input data dictionary file (IDD) and the input data file (IDF). The IDD defines a list of all possible EnergyPlus objects (‘keywords’). The IDD has an ‘.idd’ extension. The IDF provides the user description of a specific building and its underlying HVAC system components. The IDF has an ‘.idf’ extension. 37 4.3.2. Input specification The keywords may appear in the IDF in any order, but the data under each keyword must follow the format specified in the IDD. Each data value in the IDF must go hand in hand with the keyword fields of the IDD. This means that the type of data (Alpha or Numeric) and the order of the data must match the specifications of the IDD. Figure 4.4 provides a pictorial view of the correspondence required between the IDD and the IDF. IDD File Object Keyword Alphabetical Field Definition ……………….. ……………….. Numerical Field Definition Example FAN:SIMPLE:ONOFF Fan Name Available Schedule Fan Total Efficiency Delta Pressure IDF File Object Keyword Alphabetical Value ……………….. ……………….. Numerical Value Example FAN:SIMPLE:ONOFF, Supply Fan 1 FanAndCoilAvailSched 0.7 300.0 One to One correspondence Figure 4.4. Correspondence required between the IDD and the IDF Accordingly, two new objects UNITARYSYSTEM:HEATPUMP:SIMPLE and COIL:WATERTOAIRHP:SIMPLE are created for the model of the watertoair heat pump as shown in Figure 4.5 and 4.6. The first 15 fields in UNITARYSYSTEM:HEATPUMP:SIMPLE (A1A15) are alphabetic strings representing the heat pump name, schedule, air nodes, control zone etc. In Figure 4.6 38 the numeric fields (N1N18) indicate various numeric inputs that the coil requires to meet the zone demands. N7N18 are the performance coefficients of the model equations discussed in Section 4.1. The coefficients are generated using the performance coefficient calculator described in detail in chapter 7. Some of the fields carry optional values in them such as the Fan placement which can be a blowthrough or a drawthrough configuration. The inputs in the Fortran environment are handled by the EnergyPlus Input processor. The Input processor reads the IDD and IDF and supplies the simulation routines with the data contained in the input files. During the course of processing the inputs, the input processor validates keyword formats and node connections. 39 UNITARYSYSTEM:HEATPUMP:SIMPLE, A1, \field Name of Water to air heat pump A2, \field Availability schedule A3, \field Air Side Inlet Node A4, \field Air Side Outlet Node A5, \field Controlling zone or thermostat location A6, \field Supply air fan type A7, \field Supply air fan name A8, \field Fan placement A9, \field Simple Heating or Cooling coil type A10, \field Simple Heating or Cooling coil name A11, \field Supplemental heating coil type A12, \field Supplemental heating coil name A13, \field Fan Side Inlet Node A14, \field Fan Side Outlet Node A15, \field Heat pump operating mode N1, \field Fraction of the total volume flow that goes through controlling zone N2, \field Air Volumetric Flow Rate N3, \field Supplemental heating coil capacity N4, \field Maximum supply air temperature from heat pump supplemental heater N5; \field Maximum outdoor drybulb temperature for supplemental heater operation Figure 4.5. Input object for the simple watertoair heat pump model defined in the IDD 40 COIL:WATERTOAIRHP:SIMPLE, A1, \field Name of Coil A2, \field Water Side Inlet Node A3, \field Water Side Outlet Node A4, \field Air Side Inlet Node A5, \field Air Side Outlet Node N1, \field Design Water mass flow rate N2, \field Base water Mass Flow Rate N3, \field Base EER N4, \field Base COP N5, \field Base Capacity for the Heating mode N6, \field Base Capacity for the Cooling mode N7, \field Capacity Coefficient Heating 1 N8, \field Capacity Coefficient Heating 2 N9, \field Capacity Coefficient Heating 3 N10, \field COP Coefficient 1 N11, \field COP Coefficient 2 N12, \field COP Coefficient 3 N13, \field Capacity Coefficient cooling 1 N14, \field Capacity Coefficient cooling 2 N15, \field Capacity Coefficient cooling 3 N16, \field EER Coefficient 1 N17, \field EER Coefficient 2 N18; \field EER Coefficient 3 Figure 4.6. Input object for the heating and cooling coils as defined in the IDD 41 4.3.3. Flow of control / implementation algorithm The watertoair heat pump simulation is located in the module SIMPLEWATERTOAIRHP.. Once the heating/cooling latent and sensible demands are determined, the watertoair heat pump simulation routine is called. SIMSIMPLEWATERTOAIRHP is the main driver routine for the watertoair heat pump. The sensible and latent coil demands are passed as arguments to the driver routine. The driver routine, as shown in Figure 4.7, calls specific service subroutines to execute various tasks discussed below. • GetSimpleWatertoAirHPInput This subroutine obtains input data for the watertoair heat pump and stores it in the data structures. The routine also reads the data, checks whether they conform to IDD and IDF specifications, allocates arrays, initializes data structures and sets up report variables for the heat pump. • InitSimpleWatertoAirHP This subroutine initializes the watertoair heat pump components at the beginning of each environment, day, hour or time step. It uses status flags to trigger appropriate initializations. It also updates local simulation variables with the latest node data. • CalcSimpleWatertoAirHP This subroutine simulates the cooling and the heating mode of the watertoair heat pump. It simulates the model for the inlet conditions using the calculated performance coefficients and predicts the outlet conditions. The main subroutine employs 3 routines called HEAT, COOL and OFF which simulate the three operating modes of the heat pump. The check for the type of mode depends on the sign associated with the coil 42 SimFurnace simulates different Unitary euipment types Air to Air Heat pump Water to Air Heat pump Furnaces ……………… ……………… SimSimplewatertoairHP GetSimpleWatertoAirHPInput InitSimpleWatertoAirHP CalcSimpleWatertoAirHP UpdateSimpleWatertoAirHP Simulate Air Loop ……………… ……………… Simple Water to air Heat pump model Simulate Fan CalcCoolingcoil OFF CalcHeatingcoil Figure 4.7. Hierarchical flow of simulation demand, which has been discussed earlier as the thermostatic signal. The duty factor is calculated as the ratio of the zone load to the nominal capacity of the heat pump. Once all the outlet variables are computed, the control is transferred to the update routines. 43 • UpdateSimpleWatertoAirHP This subroutine updates the watertoair heat pump outlet nodes. Data is moved from the heat pump data structure to the heat pump outlet nodes. Output variables like the heating and cooling capacities, power consumption and the load side and the source side temperatures are computed. A detailed flowchart of the computational algorithm is shown in Figure 4.8.The algorithm uses the building hourly loads and the base heat pump capacity to compute the runtime fraction. The building hourly loads are equated to the heat pump hourly loads. Depending on the hourly load (Cooling/Heating), the algorithm switches between the operating modes. Then the model equations and the generated performance coefficients are used to calculate the performance variables such as capacities, COP and EER. The actual power, is the product of the calculated capacity and the runtime fraction. If the heat pump capacity exceeds the demand, the demand is met and the heat pump is switched off. However, if the demand exceeds the capacity, the duty factor of the heat pump equals 1, and and the heat pump runs at its maximum capacity. 44 Obtain Building Hourly loads and equate them to Heat pump hourly loads Runtime Fraction=HeatPumpLoad/BaseCapacity Calculate steady state heat transfer Qss=Basecap*(A1+B1*(Tin/Tref)+C1(mdot/mbase)(Tref/Tdb) Basecap is the Nominal capacity of the Heat pump A1,B1,C1 are coefficients obtained from performance trends Mbase is the rated mass flow rate multiplied by the base capacity Calculate COP COP=BaseCOP*(D1+E1*(Tin/Tref)+F1(mdot/mbase)(Tref/Tdb) BaseCOP is the Nominal COP of the Heat pump D1,E1,F1 are coefficients obtained from performance coefficient calculator Compute Heat Pump Power consumption Power=Qss/COP(or EER) HeatPumpload>0.0001 HeatPumpload<0.0001 & >0.0001 HeatPumpload<0.0001 Heating Cycle Cooling Cycle Switch HeatPump off Calculate EER EER=BaseEER*(D2+E2*(Tin/Tref)+F2(mdot/mbase)(Tref/Twb) BaseEER is the Nominal EER of the Heat pump D1,E1,F1 are coefficients obtained from performance coefficient calculator TotalCapacity>=HeatPumpLoad Switch HeatPump off Yes Compressor is cyclicfancyclic Yes Get the enthalpy, Humidity ratio from the nodes No Get the enthalpy, Humidity ratio from the nodes. Use Runtime fraction in calculations Update water to air Heat pump outlet nodes Actual Power = Calculated power*Runtime fraction Actual energy added/extracted= Calculated energy added/extracted*Runtime fraction Calculate steady state heat transfer Qss=Basecap*(A2+B2*(Tin/Tref)+C2(mdot/mbase)(Tref/Twb) Basecap is the Nominal capacity of the Heat pump A1,B1,C1 are coefficients obtained from performance trends Mbase is the rated mass flow rate multiplied by the base capacity Figure 4.8. Flow of code for the simple watertoair heat pump model 45 4.3.4. Output arrangement EnergyPlus has an efficient way of handling outputs. The output formats of the files are simple and text based. The output variables of the watertoair heat pump can be reported at every HVAC time step. EnergyPlus automatically saves all node data for each timestep. This information is available upon user request in the IDF. In addition, any simulation variable can be specified as a report variable in the computational module and included in the output reports. Output reporting is enhanced by the READVARS.EXE application which converts the text format of the output file to a readable excel table format. 46 CHAPTER 5 MODEL VERIFICATION 5.1. Introduction The simple watertoair heat pump is verified by using catalog data from two different heat pump manufacturers. The model has been verified for both heating and cooling modes. The verification exercise is intended to check both the form of the model and the generation of the performance coefficients. The performance coefficients for the model have been generated using the performance coefficient calculator, a graphical user interface written in visual basic and described in detail in Chapter 7. 5.2. Cooling mode verification Table 5.1 summarizes the results of the cooling mode verification exercise. The RMS error was less than 1.3% for the cooling capacity. The RMS error for the model prediction of power consumption in comparison with the catalog data ranged from 0.2% to 1.2%. The comparison shows good agreement between the data predicted by the model and the catalog data. The model also predicts sensible and latent capacity splits with good accuracy. The RMS error is higher when the nominal capacity of the heat pump increases drastically. This may be due to small inconsistencies in the catalog data. Table 5.1. List of heat pumps in cooling mode for model verification No Manufacturer Nominal Cooling Capacity RMS Error for Capacity W Btu/h RMS Error for Power RMS Error for Energy Added to ground 1 FHP024 7000 24000 0.42% 0.21% 0.33% 2 ClimateMasterGSH024 7000 24000 1.28% 1.15% 1.23% 47 Also, in the case of heat pump#1(Florida Heat Pump), with all data reported for a single water flow rate, all the calculated points lie on the diagonal to match the catalog data points as shown in Figures 5.15.4. However, in the case of heat pump#2 (ClimateMaster) as shown by Figures 5.55.8, some of the points group and cluster away from the diagonal. This error is small and may be a result of either the computer model used to generate the tables from a few measured points or of the testing and rating methods used by the manufacturer. Water source heat pumps are subjected to the rating requirements of ARI standard 320 which allows a tolerance of ±5% from the catalog data. Also, the ARI rated condition forms one data point among the various data points in the catalog. As previously noted, many of the catalog points are generated by computer models. This creates a built in uncertainty in the catalog data. 6 6.5 7 7.5 8 8.5 9 6.0 6.5 7.0 7.5 8.0 8.5 9.0 Catalog Total cooling capacity(kW) Calculated Total cooling capacity(kW) 20.5 22.5 24.5 26.5 28.5 30.5 20.5 22.5 24.5 26.5 28.5 30.5 Catalog Total cooling capacity(kBtu/Hr) Calculated Total cooling capacity(kBtu/hr) 10% +10% Figure 5.1. Catalog cooling capacity v/s calculated cooling capacity (heat pump #1) 48 0.3 0.4 0.5 0.6 0.7 0.3 0.4 0.5 0.6 0.7 Catalog Power Consumption(kW) Calculated Power Consumption(kW) 1 1.5 2 2.5 1 1.5 2 2.5 Catalog Power Consumption(kBtu/Hr) Calculated Power Consumption(kBtu/hr) 10% +10% Figure 5.2. Catalog power consumption v/s calculated power consumption (heat pump #1) 2 3 4 5 6 7 8 9 10 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 Catalog Total Sensible cooling capacity(kW) Calculated Total Sensible cooling capacity(kW) 10 12 14 16 18 20 22 24 10 12 14 16 18 20 22 24 Catalog Total Sensible cooling capacity(kBtu/hr) Calculated Total Sensible cooling capacity(kBtu/hr) 10% +10% Figure 5.3. Catalog sensible capacity v/s calculated sensible capacity (heat pump #1) 49 6 6.5 7 7.5 8 8.5 9 6.0 6.5 7.0 7.5 8.0 8.5 9.0 Catalog Heat transfer rate(kW) Calculated Heat transfer rate(kW) 20.5 22.5 24.5 26.5 28.5 30.5 20.5 22.5 24.5 26.5 28.5 30.5 Catalog Heat transfer rate(kBtu/Hr) Calculated Heat transfer rate(kBtu/hr) 10% +10% Figure 5.4. Catalog v/s calculated heat transfer rate (heat pump #1) 4500 5500 6500 7500 4500.0 5500.0 6500.0 7500.0 Catalog Total cooling capacity(W) Calculated Total cooling capacity(W) 15500 16500 17500 18500 19500 20500 21500 22500 23500 15500 16500 17500 18500 19500 20500 21500 22500 23500 Catalog Total cooling capacity(Btu/Hr) Calculated Total cooling capacity(Btu/hr) 10% +10% Figure 5.5. Catalog cooling capacity v/s calculated cooling capacity (heat pump #2) 50 200 250 300 350 400 450 500 200.0 250.0 300.0 350.0 400.0 450.0 500.0 Catalog Power Consumption(W) Calculated Power Consumption(W) 900 1200 1500 1800 2100 2400 2700 3000 900 1200 1500 1800 2100 2400 2700 3000 Catalog Power Consumption(Btu/Hr) Calculated Power Consumption(Btu/hr) 10% +10% Figure 5.6. Catalog power consumption v/s calculated power consumption (heat pump #2) 2500 3500 4500 5500 6500 7500 2500.0 3500.0 4500.0 5500.0 6500.0 7500.0 Catalog Total Sensible cooling capacity(W) Calculated Total Sensible cooling capacity(W) 13500 14500 15500 16500 17500 18500 19500 20500 21500 13500 14500 15500 16500 17500 18500 19500 20500 21500 Catalog Total Sensible cooling capacity(Btu/Hr) Calculated Total Sensible cooling capacity(Btu/hr) 10% +10% Figure 5.7. Catalog sensible capacity v/s calculated sensible capacity (heat pump #2) 51 4500 5500 6500 7500 4500.0 5500.0 6500.0 7500.0 Catalog Total Heat transfer rate(kW) Calculated Heat transfer rate(kW) 15500 16500 17500 18500 19500 20500 21500 22500 23500 24500 15500 16500 17500 18500 19500 20500 21500 22500 23500 24500 Catalog Heat transfer rate(kBtu/Hr) Calculated Heat transfer rate(kBtu/hr) 10% +10% Figure 5.8. Catalog v/s calculated heat transfer rate (heat pump #2) 5.3. Heating mode verification Table 5.2 summarizes the results of the heating mode verification exercise. There is a good agreement between the model predicted data and the catalog data for the heating mode of the simple model. The RMS error was less than 0.8% for the heating capacity. The RMS error for the model prediction of power consumption in comparison with the catalog data ranged from 0.2% to 0.65%. The comparison shows good agreement between the model predicted data and the catalog data. The heating model as shown by the Figures 5.95.14 performs better than the cooling model with most of the points lying on the diagonal. The error is small and probably occurs due to the uncertainty in the catalog data as discussed in the previous section. 52 Table 52. List of heat pumps in heating mode for model verification No Manufacturer Nominal Heating Capacity RMS Error for Capacity W Btu/h RMS Error for Power RMS Error for Energy extracted from ground 1 FHP024 7000 24000 0.41% 0.26% 0.31% 2 ClimateMasterGSH024 7000 24000 0.73% 0.62% 0.71% 1 3 5 7 9 11 1.0 3.0 5.0 7.0 9.0 11.0 Catalog Total Heating capacity(kW) Calculated Total Heating capacity(kW) 10 15 20 25 30 35 40 45 10 15 20 25 30 35 40 45 Catalog Total Heating capacity(kBtu/Hr) Calculated Total Heating capacity(kBtu/hr) 10% +10% Figure 5.9.Catalog heating capacity v/s calculated heating capacity (heat pump #1) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Catalog Power Consumption(kW) Calculated Power Consumption(kW) 1 1.5 2 2.5 1 1.5 2 2.5 Catalog Power Consumption(kBtu/Hr) Calculated Power Consumption(kBtu/hr) 10% +10% Figure 5.10. Catalog power consumption v/s calculated power consumption (heat pump #1) 53 1 3 5 7 9 11 1.0 3.0 5.0 7.0 9.0 11.0 Catalog Heat transfer rate(kW) Calculated Heat transfer rate 10 15 20 25 30 35 40 45 10 15 20 25 30 35 40 45 Catalog Heat transfer rate(kBtu/Hr) Calculated Heat transfer rate(kBtu/hr) 10% +10% Figure 5.11.Catalog v/s calculated heat transfer rate (heat pump #1) 2000 3000 4000 5000 6000 2000.0 3000.0 4000.0 5000.0 6000.0 Catalog Total Heating capacity(W) Calculated Total Heating capacity(W) 11500 13500 15500 17500 19500 21500 23500 25500 11500 13500 15500 17500 19500 21500 23500 25500 Catalog Total Heating capacity(Btu/Hr) Calculated Total Heating capacity(Btu/hr) 10% +10% Figure 5.12. Catalog heating capacity v/s calculated heating capacity (heat pump #2) 54 800 1300 1800 800.0 1300.0 1800.0 Catalog Power Consumption(W) Calculated Power Consumption(W) 2500 3100 3700 4300 4900 5500 2500 3100 3700 4300 4900 5500 Catalog Power Consumption(Btu/Hr) Calculated Power Consumption(Btu/hr) 10% +10% Figure 5.13. Catalog power consumption v/s calculated power consumption (heat pump #2) 2000 3000 4000 5000 6000 7000 8000 2000.0 3000.0 4000.0 5000.0 6000.0 7000.0 8000.0 Catalog Heat transfer rate(kW) Calculated Heat transfer rate(kW) 7000 12000 17000 22000 7000.0 12000.0 17000.0 22000.0 Catalog Heat transfer rate(kBtu/Hr) Calculated Heat transfer rate(kBtu/hr) 10% +10% Figure 5.14.Catalog v/s calculated heat transfer rate (heat pump #2) 55 5.4. Verification using Correction Factors 5.4.1. Correction Factors As noted in Section 4.3, some manufacturers, such as ClimateMaster, provide correction factors to account for the effect of parameters that are held constant in their data sets. These parameters include the air flow rates and the wet bulb temperatures. If the units are operating at conditions different than the rated conditions, the heat pump capacities can be approximated using these correction factors. Table 5.3 and 5.4 show air flow rate(CFM) and wet bulb temperature correction factors for total capacity (TC), sensible capacity (SC), power and heat rejection rate (HR) to the ground for cooling operation of the ClimateMaster GS series units. Table 5.3. Correction factors for the air flow rate CFM TC SC Power HR 350 0.957 0.946 0.994 0.964 375 0.979 0.969 0.997 0.982 400 1 1 1 1 425 1.021 1.029 1.003 1.018 450 1.043 1.058 1.006 1.036 Table 5.4. Correction factors for the wet bulb temperatures WB TC SC (80DB) Power HR 60 0.899 1.192 0.984 0.899 65 0.94 1.106 0.991 0.949 66.2 0.976 1.043 0.997 0.98 67 1 1 1 1 70 1.012 0.933 1.002 1.01 75 1.024 0.8 1.005 1.019 The influence of the correction factors on the performance variables is observed by plotting the correction factors against the air flow rates and the wet bulb temperatures as shown by Figure 5.15 and 5.16. 56 0.9 1 1.1 300 325 350 375 400 425 450 475 Air flow rate(CFM) Correction Factors TC SC Power HR Rated Rated Air Flow rate Figure 5.15. Influence of air flow correction factors on performance 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 55 61 67 73 79 Wet bulb Temperatures (F) Correction Factors TC SC (80DB) Power HR Rated Figure 5.16.Influence of wet bulb correction factors on performance Figure 5.15 shows that for a variation of 50 CFM (12.5%) in the air flow rate, the performance of the GS series heat pump changes by less than 10%. For the same change in air flow rate, the change in power is negligible. Since the sensible cooling capacity is highly dependent on the wet bulb temperature, it shows a significant variation (20%) over a wet bulb range of 15o F (6075oF) as shown in Figure 5.16. For low wet bulb depression (high relative humidity) a change in the wet bulb affects Rated Wet bulb 57 the latent/sensible split, but not the total capacity. At low relative humidity, Fig. 516 shows that a change in wet bulb temperature affects both the total capacity and the latent/sensible split. The plots suggest that the standard manufacturers’ data sets should be extended to include a range of air flow rates and inlet wet bulb temperatures (in addition to water mass flow rates, water inlet temperatures and inlet dry bulb temperatures). Since in the simulation environment, neither the wet bulb temperature nor the air flow rate are expected to always be at the rated condition, using the correction factors to extend the data set is necessary to ensure that the model is not applied outside the range of the catalog data. 5.4.2. Comparison of simulated and measured data using correction factors In this section, the effect of applying the model outside the range of the manufacturer’s standard data set is investigated. Since experimental data is not available, data points generated using correction factors are used.. The procedure is as follows: • Generate model coefficients using the standard data set (no correction factor data) • Compare predicted outputs with calculated outputs at nonrated (correction factor) conditions. • Generate model coefficients using extended data set (standard + correction factor data). • Compare predicted outputs with calculated outputs at nonrated (correction factor) conditions. 58 5.4.2.1. Comparison Procedure ClimateMaster GC series performance and correction data was used as shown in Fig 5.17. Nine data points were generated using the correction factors for the total cooling capacity at different air flow rates. Similarly, thirty one data points were generated using the correction factors for the sensible cooling capacity over the range of wet bulb temperatures. The data points were merged with the catalog data set characterized by constant air flow rate and wet bulb. Then the entire data set was used to generate a new set of coefficients for equations 41 through 43 and equations 415 through 417 by applying the least square technique. A set of coefficients based on the regular catalog data set (without the correction factors) and a set of coefficients (with the correction factors) were then used in the model equations to calculate heat pump capacities and power use at variable air mass flow rates and wet bulb temperatures. 59 Figure 5.17. Correction factors for GC ClimateMaster series (used with permission) The ClimateMaster GC018 data set was used in the verification process. The total and sensible capacities were calculated over a range of wet bulb temperatures and air mass flow rates as shown in Table 5.5. The 3rd data point was introduced by the 60 manufacturers to meet the European standards of 19oC(66.2oF) wet bulb and 27oC(80.6oF) dry bulb. Table 5.5. Range of wet bulb temperatures and air flow rates for GC series 5.4.2.2. Results Figure 5.185.23. summarizes the complete set of coefficients generated for the study. Two data sets were used to generate coefficients for six model equations: total capacity, sensible capacity and power for both forms of the model presented in section 4.3..By merging the extra data points generated using correction factors with the existing data set, coefficients for the total cooling capacity, sensible capacity and the power consumption equations are calculated. The coefficients generated using constant wet bulb temperature of 67 oF,(rated condition) diverge from the catalog data as shown in Figure 5.185.20. The one point that lies on the diagonal is the rated condition. With the inclusion of the corrected wet bulb temperature points, the total cooling capacity, sensible capacity and the power consumption were in good agreement with the catalog data. As expected for all three figures, the two models (equations 41 – 43 and equations 415417) show identical results for coefficients generated with and without correction factor data. As shown in figure 518, the total capacity is accurately predicted within 10% by the water mass flow rate alone. That is, generating model coefficients at the rated wet 61 bulb temperature then applying the model beyond the data set (over a 15 oF range of wet bulb temperatures) results in less than 1% error in the predicted total capacity. 5000 6000 7000 8000 5000.0 6000.0 7000.0 8000.0 Catalog Total cooling capacity(W) Calculated Total cooling capacity(W) Eqn(41) With correction factors Eqn(41) Without correction factors Eqn(415) With correction factors Eqn(415) Without correction factors +10% 10% Figure 5.18. Comparison of catalog v/s calculated cooling capacity with and without the correction factors for wet bulb temperatures Figure 519 shows that the sensible/latent split cannot be accurately predicted without including a range of wet bulb temperatures in the data set used to generate the coefficients. This is the expected result based on Figure 5.16 and 5.17. As shown in Figure 5.19 the error in the predicted sensible heat transfer rate is in the range of 30% to 40%. The error in the predicted power on the other hand is small (less than 2%) as shown in Figure 5.20. This result is also expected from Figure 5.16 and 5.17. 62 1000 3000 5000 7000 9000 1000.0 3000.0 5000.0 7000.0 9000.0 Catalog Sensible cooling capacity(W) Calculated Sensible cooling capacity(W) Eqn(43) With correction factors Eqn(43) Without correction factors Eqn(417) With correction factors Eqn(417) Without correction factors +40% 40% Figure 5.19. Comparison of catalog v/s calculated sensible cooling capacity with and without the correction factors for wet bulb temperatures 1300 1325 1350 1375 1400 1300.0 1325.0 1350.0 1375.0 1400.0 Catalog Power(W) Calculated Power(W) Eqn(41) With correction factors Eqn(41) Without correction factors Eqn(415) With correction factors Eqn(415) Without correction factors +2% 2% Figure 5.20. Comparison of catalog v/s calculated power consumption with and without the correction factors for wet bulb temperatures 63 Equations 4.14.3 are independent of the air mass flow rate and hence will not respond to any load side flow variation. However, at rated conditions (600 CFM), the calculated point coincides with the catalog data point as shown by Figures 5.215.23. Equation (4.15)(4.17) developed in section 4.3 accounts for air flow rate variation and the correction factors make possible a few data points to verify this form of the model. At constant air mass flow rate, = 1 a base a m m & & and Equations 4154.17 match Equation 4.14.3 at the rated condition. Figure 522 shows that the sensible/latent split cannot be accurately predicted without including a range of air flow rates in the data set used to generate the coefficients. This is the expected result based on Figure 5.15 and 5.17. As shown in Figure 5.22 the error in the predicted sensible heat transfer rate is about 27%. This is because the sensible heat transfer is more susceptible to a variation in the wet bulb temperature than the air flow rate as seen in Figure 5.15 and 5.17. The error in the predicted power is small (less than 3%) as shown in Figure 5.23. This result is also expected from Figure 5.15 and 5.17. 5700 6700 7700 5700.0 6700.0 7700.0 Catalog Total cooling capacity(W) Calculated Total cooling capacity(W) Eqn(415) Without correction factors Eqn(415) With correction factors Eqn(41)  Independent of Air Flow rate 6% +6% Figure 5.21. Comparison of catalog v/s calculated cooling capacity with and without the correction factors for variable air flow rates 64 3000 4000 5000 3000.0 4000.0 5000.0 Catalog Sensible cooling capacity(W) Calculated Sensible cooling capacity(W) Eqn(417) Without correction factors Eqn(417) With correction factors Eqn(43)  Independent of Air Flow rate 27% +27% Figure 5.22. Comparison of catalog v/s calculated sensible capacity with and without the correction factor for variable air flow rates 1200 1250 1300 1350 1400 1450 1500 1200.0 1250.0 1300.0 1350.0 1400.0 1450.0 1500.0 Catalog Power(W) Calculated Power(W) Eqn(417) Without correction factors Eqn(417) With correction factors Eqn(43)  Independent of Air Flow rate 3% +3% Figure 5.23. Comparison of catalog v/s calculated power consumption with and without the correction factors for variable air flow rates The results recorded in the above comparison plots indicate this mathematical influence on calculations to determine the stability of the implemented model simulation environment. 65 5.4.3. Application of the model outside the catalog data sets Jin(2002) validated the detailed model using ClimateMaster HS006 performance data. In the model validation, the HS series correction factors were used to expand the original data over a range of air temperatures and flow rates. The RMS errors from Jin’s work are shown for comparison table 5.6. The simplified model has been validated under identical conditions. The nominal capacity of the heat pump is 7400 Btu/h or 2170 W. ClimateMaster HS006 performance data is merged with the HS series correction factors for the air temperatures and flow rate. Since each correction factor is applied to multiple operating points (i.e. different entering water temperature, water flow rate and air flow rate), these data are presumed to have lower accuracy than the regular tabulated data. 2981 points are generated by applying correction factor to all the operating points. The performance is also calculated using the model equations (415) and (4 17). As shown in Figure 5.24 the error in the predicted sensible heat transfer rate is less than 13%.and the error in the predicted total cooling capacity is less than 10% The error in the predicted power is relatively small (less than 8%) as shown in Figure 5.25. The rootmeansquare (RMS) error for the total cooling capacity, sensible cooling capacity and power are compared with the detailed model in Table 5.6. The RMS errors between model prediction and catalog data for the total and sensible capacities are 6.4% and 7.7% respectively. This is slightly larger than the RMS error reported for the detailed model, but still quite reasonable. The RMS error in the predicted power is nearly the same for two models. Overall, the simplified model does a reasonable job of extrapolating beyond the data set. 66 3500 4500 5500 6500 7500 3500.0 4500.0 5500.0 6500.0 7500.0 Catalog Total cooling capacity(W) Calculated Total cooling capacity(W) +15% 15% Figure 5.24. Comparison of catalog v/s calculated cooling capacity for all points with the correction factors(simplified model equation(415)) 3500 4500 5500 6500 7500 8500 9500 3500.0 4500.0 5500.0 6500.0 7500.0 8500.0 9500.0 Catalog Sensible capacity(W) Calculated Sensible capacity(W) +15% 15% Figure 5.25. Comparison of catalog v/s calculated sensible capacity for all points with the correction factors(simplified model equation(417)) 67 300 400 500 600 700 800 900 1000 300.0 400.0 500.0 600.0 700.0 800.0 900.0 1000.0 Catalog Total Power(W) Calculated Total Power(W) +8% 8% Figure 5.26. Comparison of catalog v/s calculated power consumption for all points with the correction factors(simplified model) Table 5.6. Comparison of model RMS errors for ClimateMaster HS006 Performance Variable RMS Error Detailed Model Simple Model Total Capacity 4.72% 6.38% Sensible Capacity 6.33% 7.72% Power Consumption 6.38% 6.61% 68 CHAPTER 6 MODEL APPLICATION 6.1.Using manufacturers’ data As previously stated, manufacturers are required to certify the performance of their watertoair heat pumps using the Air Conditioning and Refrigeration Institute (ARI) Standard 320 and the ISO Standards 132561. The standard developed by ASHRAE Standard 90.1, references both ARI and ISO standards, with the ISO standard designated as the executive standard. The following performance metrics are verified by test for a limited set of conditions, usually 3 or 4 points. • Cooling Capacity, Btu/h [W] • EER, Btu/w.h [W/W] • Heating Capacity, Btu/h [W] • EER, Btu/w.h [W/W] As discussed in chapter 5, manufacturers only measure a few data points. They use the measured data to generate parameters for their own models. These models are then used to generate tables of catalog data. This catalog data is not necessarily accurate and often consists of physically inconsistent data. Most heat pump manufacturers use air temperatures or the mass flow rates as the basis for measuring the power and the capacities. Florida Heat Pump focuses on keeping the water and air flow rates constant while varying the entering water and air temperatures. ClimateMaster and Trane keep the flow rates constant. Section 5.4 used available correction factors to partially evaluate the effect of data sets with constant parameters (air mass flow rates and wet bulb temperatures. This section evaluates the basic model (equation 41 69 through 414) implemented in the EnergyPlus program with coefficients generated from both ClimateMaster and FHP data sets. The results of a 24 hour simulations are compared with results from detailed Jin (2002) model with coefficients generated from the same data sets. 6.1.1. Example 1  Florida Heat pump (FHP) The units follow the ISO 132561 standards. The nominal capacities of the units can be determined simply by looking at the identification number of each unit. For instance, FHP GT018 is designed with a nominal cooling and heating capacities of 18000 Btu/h and a FHP GT054 with 54000 Btu/h. The units are designed to operate with entering fluid temperatures between 50°F (10°C) and 110°F (43.3°C) in cooling and temperatures between 25°F (3.9°C) and 80°F (27°C) in heating. All catalog points are at constant air flow and water flow rates as shown in Figure 6.1. For every entering fluid temperature data point there are five different entering air wet bulb temperature data points in the cooling operation of the unit. Moreover, for every wet bulb temperature data point there are three sensible capacity data points at 75°F, 80°F and 85°F. This means that for the Florida Heat Pump, the total cooling capacity is only a function of the dry bulb temperatures and the sensible cooling capacity is a function of both the dry and the wet bulb temperatures. Qbase and base m& are selected at the peak capacities. For the FHPGT018, the peak cooling capacity 20900 Btu/h occurs at an entering water temperature of 50oF and an entering air temperature of 70oF. The water mass flow rate, mbase, occurs at a constant flow rate of 4 GPM. This will result in the coefficients of the capacity equations that depend only on the load side and source side temperatures since w base w m m & & =1 for all data points. 70 FHP GT018 Data All performance at 550 CFM and 4.0 GPM Entering Ent. Air Total H e a t Sensible Capacity BTUH EER Fluid Wet Bulb Capacity Watts ERnet.j e c t i o n Air Dry Bulb Temp. Temp. Temp. BTUH Input BTUH 75o 80o 85o 50 61 18259 713 20692 13895 16670 18259 23.4 50 64 19144 729 21632 12999 16502 18302 24 50 67 20044 745 22586 11966 15634 18040 24.6 50 70 20959 761 23556 9976 13791 17941 25.2 50 73 21889 777 24541  11754 16110 25.7 60 61 17375 776 20021 13222 15863 17375 19.9 60 64 18217 793 20922 12370 15703 17416 20.4 60 67 19074 810 21838 11387 14878 17167 20.9 60 70 19945 827 22768 9494 13124 17073 21.4 60 73 20830 845 23713  11186 15331 21.9 70 61 16464 852 19372 12529 15032 16464 17.7 70 64 17262 871 20234 11721 14880 16503 18.2 70 67 18074 890 21111 10790 14098 16267 18.6 70 70 18899 909 22001 8996 12436 16178 19.1 70 73 19738 928 22905  10599 14527 19.5 85 61 14701 994 18091 11187 13422 14701 15.3 85 64 15413 1016 18879 10466 13286 14735 15.7 85 67 16138 1038 19680 9634 12588 14524 16.1 85 70 16875 1060 20493 8032 11104 14445 16.5 85 73 17623 1083 21318  9464 12971 16.9 100 61 13318 1125 17156 10135 12159 13318 13.1 100 64 13963 1150 17887 9481 12037 13349 13.4 100 67 14620 1175 18629 8728 11404 13158 13.8 100 70 15287 1200 19383 7277 10059 13086 14.1 100 73 15966 1226 20148  8574 11751 14.4 Figure 6.1. Florida Heat Pump (GT018) cooling catalog data Identification code indicates the Nominal capacity GT018 18000 Btu/h 71 6.1.2 Example 2  ClimateMaster ClimateMaster holds the air temperature and the air mass flow rate constant to generate their data. Unlike the FHP unit in which the water mass flow rate remains constant through out the specification data, the water mass flow rate is varied between 2.2 and 4.5 gpm. Again, the identification code indicates the nominal capacity of the heat pump unit that is GSV018 indicates a nominal capacity of 18000Btu/h. The design conditions for the heat pump unit occur at 85°F and 4.5 gpm as shown in Figure 6.2. The entering air temperature is 80°F dry bulb and 67°F wet bulb for cooling and 70°F dry bulb for heating as per the ISO standards. Again, Qbase and mbase are selected at the peak cooling capacities. For the GSH/GSV018, the peak cooling capacity 22400 Btu/h occurs at an entering water temperature of 30oF, an entering air dry bulb temperature of 80oF and wet bulb temperature of 67oF. The volumetric flow rate is 4.5 GPM occurs at the peak capacity. This will result in coefficients of the capacity equations that depend only on source side mass flow rate and temperature since the load side wet bulb temperature is a constant for all data points. 72 Figure 6.2. ClimateMaster heat pump (GSH/GSV018) catalog data (used with permission) Design Conditions Base Capacity Base flow rate 73 6.1.3 Example 3 – Trane Trane is similar to ClimateMaster in its approach to following the standard rating conditions. The performance data is based on constant air dry bulb and wet bulb temperatures with varying water mass flow rates and entering water temperatures. GEH/V006 indicates a nominal capacity of 6000Btu/h. The entering air temperature is 80.6°F dry bulb and 67°F wet bulb for cooling and 68°F dry bulb for heating as per the ISO standards. For the GEH/V006, the peak cooling capacity or Qbase is 9200 Btu/h and occurs at an entering water temperature of 45oF ,an entering air dry bulb temperature of 80oF and wet bulb temperature of 67oF. The volumetric flow rate is 1.2 GPM at the peak capacity. 74 TRANE GEH006 Data Rated GPM 1.8 Rated CFM 242 Entering Ent. Water Total Heat Fluid Mass flow Capacity Watts COP Absorption Temp. rate BTUH Input BTUH EWT GPM 25 1.2 5.8 0.52 3.3 4 25 1.5 5.8 0.52 3.3 4 25 1.7 5.8 0.53 3.2 4 25 1.8 5.8 0.53 3.2 4 25 1.9 5.9 0.53 3.3 4.1 25 2 5.9 0.53 3.3 4.1 25 2.2 5.9 0.53 3.3 4.1 35 1.2 6.2 0.55 3.3 4.3 35 1.5 6.3 0.55 3.3 4.4 35 1.7 6.4 0.56 3.3 4.5 35 1.8 6.4 0.56 3.4 4.5 35 1.9 6.4 0.56 3.4 4.5 35 2 6.4 0.56 3.4 4.5 35 2.2 6.5 0.56 3.4 4.6 45 1.2 7 0.57 3.6 5.1 45 1.5 7.2 0.57 3.7 5.3 45 1.7 7.3 0.57 3.7 5.3 45 1.8 7.4 0.58 3.7 5.4 45 1.9 7.4 0.58 3.7 5.4 45 2 7.4 0.58 3.7 5.4 45 2.2 7.5 0.58 3.8 5.5 55 1.2 8 0.59 4 6 55 1.5 8.2 0.6 4 6.1 55 1.7 8.3 0.6 4 6.2 55 1.8 8.4 0.6 4.1 6.3 55 1.9 8.4 0.6 4.1 6.4 55 2 8.4 0.6 4.1 6.4 55 2.2 8.5 0.6 4.2 6.5 68 1.2 9.2 0.63 4.3 7.1 68 1.5 9.5 0.63 4.4 7.3 68 1.7 9.6 0.63 4.5 7.4 68 1.8 9.7 0.64 4.5 7.5 68 1.9 9.7 0.64 4.5 7.5 68 2 9.7 0.64 4.5 7.6 68 2.2 9.7 0.64 4.5 7.6 75 1.2 9.9 0.64 4.6 7.8 75 1.5 10.1 0.64 4.6 7.9 75 1.7 10.2 0.64 4.6 8 75 1.8 10.3 0.64 4.7 8.1 75 1.9 10.3 0.64 4.7 8.1 75 2 10.4 0.64 4.7 8.2 75 2.2 10.4 0.65 4.7 8.2 86 1.2 10.8 0.66 4.8 8.5 86 1.5 11 0.66 4.9 8.7 86 1.7 11 0.66 4.9 8.8 86 1.8 11.1 0.66 4.9 8.9 86 1.9 11.2 0.67 4.9 8.9 86 2 11.2 0.67 4.9 8.9 86 2.2 11.3 0.67 4.9 9 .Figure 6.3. TRANE heat pump (GEH/V 006) heating catalog data 75 TRANE GEH006 Data Rated GPM 1.8 Rated CFM 242 Entering Ent. Water Total Sensible Heat Fluid Mass flow Capacity Capacity Watts EER Rejection Temp. rate BTUH BTUH Input BTUH EWT GPM 45 1.2 9.2 6.4 0.49 18.9 10.9 45 1.5 9.2 6.4 0.47 19.6 10.8 45 1.7 9.3 6.4 0.46 20.2 10.9 45 1.8 9.3 6.4 0.45 20.6 10.9 45 1.9 9.3 6.4 0.45 20.6 10.9 45 2 9.3 6.4 0.45 20.6 10.9 45 2.2 9.3 6.4 0.45 20.6 10.9 55 1.2 8.8 6.2 0.49 18 10.4 55 1.5 8.9 6.2 0.47 18.9 10.5 55 1.7 8.9 6.2 0.46 19.2 10.4 55 1.8 8.9 6.2 0.45 19.6 10.4 55 1.9 8.9 6.2 0.45 19.6 10.4 55 2 8.9 6.2 0.45 19.6 10.4 55 2.2 8.9 6.2 0.45 19.6 10.4 68 1.2 8.2 5.9 0.54 15.2 10 68 1.5 8.3 6 0.52 15.8 10 68 1.7 8.3 6 0.51 16.1 10 68 1.8 8.3 6 0.5 16.4 10 68 1.9 8.3 6 0.5 16.7 10 68 2 8.3 6 0.5 16.7 10 68 2.2 8.3 6 0.5 16.7 10 75 1.2 7.8 5.8 0.58 13.4 9.8 75 1.5 7.9 5.8 0.57 14 9.8 75 1.7 7.9 5.8 0.55 14.4 9.8 75 1.8 7.9 5.8 0.54 14.7 9.8 75 1.9 8 5.8 0.54 14.8 9.8 75 2 8 5.9 0.54 14.8 9.8 75 2.2 8 5.9 0.54 14.8 9.8 86 1.2 7.4 5.6 0.66 11.2 9.7 86 1.5 7.4 5.7 0.64 11.5 9.6 86 1.7 7.4 5.7 0.64 11.6 9.6 86 1.8 7.5 5.7 0.63 11.9 9.6 86 1.9 7.5 5.7 0.62 12.1 9.6 86 2 7.5 5.7 0.62 12.1 9.6 86 2.2 7.5 5.7 0.62 12.1 9.6 95 1.2 7 5.4 0.73 9.5 9.5 95 1.5 7 5.5 0.71 9.9 9.5 95 1.7 7 5.5 0.7 10 9.5 95 1.8 7 5.5 0.7 10.1 9.4 95 1.9 7 5.5 0.7 10.1 9.4 95 2 7 5.5 0.69 10.3 9.4 95 2.2 7 5.5 0.69 10.3 9.4 105 1.2 6.5 5.2 0.81 8.1 9.3 105 1.5 6.5 5.2 0.8 8.2 9.3 105 1.7 6.6 5.2 0.78 8.4 9.3 105 1.8 6.6 5.2 0.77 8.5 9.3 105 1.9 6.6 5.2 0.77 8.5 9.3 105 2 6.6 5.2 0.77 8.6 9.2 105 2.2 6.6 5.2 0.77 8.6 9.2 115 1.2 5.9 5 0.88 6.7 8.9 115 1.5 5.9 5 0.87 6.8 8.9 115 1.7 6 5 0.85 7 8.9 115 1.8 6 5 0.84 7.1 8.9 115 1.9 6 5 0.84 7.1 8.9 115 2 6 5 0.84 7.1 8.9 115 2.2 6 5 0.84 7.2 8.9 120 1.2 5.4 4.9 0.9 6 8.5 120 1.5 5.5 4.8 0.9 6.1 8.5 120 1.7 5.5 4.8 0.89 6.2 8.5 120 1.8 5.6 4.8 0.88 6.3 8.6 120 1.9 5.6 4.8 0.87 6.4 8.5 120 2 5.6 4.8 0.87 6.4 8.5 120 2.2 5.6 4.8 0.87 6.4 8.5 .Figure 6.4. TRANE heat pump (GEH/V 006) Cooling catalog data Identification code indicates the Nominal capacity GEH/V 006 6000 Btu/h 76 6.2.Case study The simplified watertoair heat pump model that was implemented in EnergyPlus as described in the previous chapters is evaluated by comparing and analyzing its performance in the simulation environment. The case study was carried out on a typical office building assumed to be located at Chanute AFB, Illinois. An annual building loads simulation along with simulations for the summer design day (21st July) and the winter design day (21st January) were run for this region. Results obtained from the simplified model are compared with detailed model (Jin,2002) results for the same building, system and environmental conditions. 6.2.1. Example building and system description The example building shown in Figure 6.5 has an area of 108 m2. The zones are served by a watertoair heat pump unit that includes a supplemental heating coil. Figure 6.5. Isometric north east view of the building plan A summary of additional assumptions and a brief description of the building and system are shown below. 77 • Typical building with 3 thermal zones. • No ground contact (all floors are adiabatic). • Roofs are exposed to the outdoor environment • There is one large south facing single pane window located in the southwest zone. • The air handling system is modeled as a blow through system. • The lighting loads are 7.6 W/m2. The electric equipment plug load is 12.1W/m2. • The office occupancy is assumed to be one person per 10.7 m2 with a total heat gain of 131.7 Watts/Person of which 30% is assumed to be radiant heat gain. • A single watertoair heat pump serves all zones. • The controlling zone is the east zone. • The heating set point during winter months is 20°C during occupied hours, 15°C setback otherwise. Cooling set point is at 24°C during occupied hours only. • The heat pump is scheduled to be unavailable during unoccupied hours. 78 Zone Water to air cooling mode Water to air heating mode Ground loop heat exchanger Supply fan Cooling coil Heating coil Water to Air Heat Pump Figure 6.6. Schematic of the building and system plan implemented in EnergyPlus 6.2.2. System connections and configuration In the case of a watertoair ground source heat pump as shown in Figure 6.6, the condenser loop is directly connected to the air loop. The condenser loop has a constant speed circulating pump that operates continuously. The heat pumps runs with design flow rates on both the load and the source sides. The design water mass flow rate is set at 1.7 kg/s and the design load side mass flow rate is set at 2 kg/s. The heating coil of the single reversible packaged unit is available for operation during winter and the cooling coil in summer. The supplemental gas heating coil is assumed to have a nominal capacity of 32000W. A simple ONOFF supply fan with a total 79 efficiency of 70% is used. The fan outlet node forms the inlet node to the heating/cooling coils with in the heat pump. The outlet node of the heating coil forms the inlet node to the supplemental coil. The outlet node of the supplemental coil becomes the outlet node of the heat pump to complete the ground loop configuration. 6.2.3 Annual and design day building load profiles Fig 6.7 shows the hourly zone cooling and heating loads for the example building when simulated with Chanute AFB, Illinois weather data. Heating loads are shown as positive loads and cooling as negative loads. The peak heating load is around 15KW and the peak cooling load is around 16KW. The building is well balanced in terms of the heating and cooling loads. In order to verify the correctness of results generated by the simulation, the heat pump was operated in the heating and the cooling mode. For the winter design day, that is Jan 21st, the heating mode of the heat pump is triggered. For the summer design day, that is July 21st, the cooling mode of the heat pump is activated. The schedule is an active day schedule for both design days, which means that the design day is a typical Monday. The load profile for the winter design day is shown in Fig 6.8. Although typically, a winter design day includes no scheduled load, some scheduled loads were included to exercise the model. 80 20000 15000 10000 5000 0 5000 10000 15000 20000 1 730 1459 2188 2917 3646 4375 5104 5833 6562 7291 8020 8749 Number of timesteps Building loads[W] Heating load Cooling load Figure 6.7. Annual hourly loads for the example building in Chanute AFB,IL Figure 6.8 shows the winter design day heating load. From the 1st time step through the 42nd time step, all electric equipment is scheduled OFF and the building is unoccupied. At 7 a.m. (the 43rd time step), the building experiences a high pickup load as the system comes out of setback. Starting at 7a.m. (timestep 43) office occupancy, lighting and all electric equipment schedules begin to ramp up. This along with the solar heat gain largely offset, the heating load which steadily decreases until the activity comes to an end at 5p.m. At 5p.m the building suddenly experiences a major drop in the heating load as the system returns to setback. The load is zero until the sun sets and the thermal mass of the building cooling and which point the heating load steadily increases until the system comes out of setback. 81 Figure 6.8. Building load profile for the winter design day Figure 6.9. Building load profile for the summer design day The load profile for the summer design day is shown in Fig 6.9 and may be analyzed as follows. The peak cooling load is about 17KW. The pick up load again occurs at 7 a.m. as activity and system schedules are initiated. The maximum dry bulb temperature of 32°C and solar radiation contributes to the higher peak load. Once the system stabilizes, the building load steadily increases due to the effect of increasing 82 dry bulb temperature, solar heat gains and scheduled loads until the system returns to setback. 6.2.4 Selection of heat pump A Florida heat pump GT120 having a nominal capacity of 38KW is selected for the typical office building. The heat pump is modeled as a single heat pump which switches the control to the heating or cooling coils depending on the mode in which the heat pump is required to operate. Although the heat pump is modeled to operate throughout the year, a schedule is maintained to handle the demands appropriately. The cooling mode is made available only in summer and the heating mode is available only in winter. The nominal capacity of the heat pump is set at 38KW with a nominal COP of 4.5 in the heating mode and a nominal EER of 20 in the cooling mode. The nominal values are characteristic of the GT120 Florida heat pump used in this study. Using the nominal values above and the catalog data for the GT120 unit, 17 coefficients were generated as shown in Table 6.1 by using the performance coefficient calculator discussed in the next chapter. Table 61. Distribution of coefficients in model equations Performance Variables No. of Coefficients in the Performance Equations Cooling Capacity 3 Heating Capacity 3 COP 3 EER 3 Sensible Cooling Capacity 5 83 6.2.5. Cooling mode operation and analysis Based on the control zone sensible load , represented by the east zone in this case study and the fraction of air flow through the control zone, the total cooling sensible load demanded by all the zones being served is given by equation (6.1). ControlZoneAirFlowFraction ControlZoneCoolingLoad HeatPumpCoolingLoad = ………………….. (6.1) On the condenser demand side all the components are already simulated and controlled by the air loop. The condenser demand side manager is responsible for supplying the specified flow rate through the source side coil. 0 5000 10000 15000 20000 25000 30000 1 12 23 34 45 56 67 78 89 100 111 122 133 144 Number of timesteps Cooling demands[W] Figure 6.10. Demands for the summer design day The load side coil demand, shown in Figure 6.10, peaks late in the afternoon, unlike the building load (Figure 6.9) which reaches a high point as the office building begins its operation at 7a.m.This is due to the effect of outside air which increasingly adds to the coil demand as the outside temperature rises. To meet this demand, the heat pump coils and fans must be configured and scheduled appropriately. The heat pump may be configured for ‘blowthrough’ or ‘drawthrough’ operation with the fan operating in either of two modes: ‘cycling’ or ‘continuous’. 84 0 100 200 300 400 500 600 700 1 12 23 34 45 56 67 78 89 100111122133144 Number of timesteps Fan Electric Power[W] Figure 6.11. Fan electric power consumption (cooling mode) The profile of the fan electric power consumption as shown in Figure 6.11 is identical to the demand that must be met by the heat pump. A simple algorithm switches the fan OFF when the cooling load to be met by the heat pump is zero and switches the fan ON when it is greater than zero. The fan power consumption is directly proportional to the air mass flow rate. The flow rate is resolved as the program iterates through each node and determines what the flow requests and flow limits are. Figure 6.12 demonstrates point to point synchronization between the cooling demand and the demand met. The heat pump is operated at full load and its capacity under full load conditions is determined. The run time fraction in cooling mode is calculated by using a simple control strategy as shown by equation 6.2. BaseCoolingCapacity TotalCoolingdemand RuntimeFractionCool = ……………………… (6.2) 85 0 5000 10000 15000 20000 25000 30000 1 12 23 34 45 56 67 78 89 100 111 122 133 144 Number of timesteps Cooling Demand/Met[W] Cooling Demand[W] Cooling MET[W] Figure 6.12. Cooling demands met by the heat pump Once the runtime fraction is computed, the total capacity under full load conditions is multiplied by the runtime to compute the actual capacity. The other performance variables such as heat pump power consumption and energy added to the air stream by the heat pump are calculated in the same manner as shown in the following equations. ActualCapacity TotalCapacity RuntimeFractionCool fullload = * ………………… (6.3) ActualPower TotalPower RuntimeFractionCool fullload = * ……………………….(6.4) ActualGroundHeatTrans TotalGroundHeatTrans RuntimeFractionCool fullload = * (6.5) The performance variables depend on the requested water mass flow rate. In the case study, the east zone temperatures are controlled within the bounds of the set point at 24°C when the building is occupied and the heat pump is operating. The temperature peaks high for a few time steps when the system is switched off and then gradually drops to the set point at 30°C. The sudden rise and drop in the temperatures, which occur at 7 a.m and 5 p.m occur as the system comes out of setback or goes into setback, are shown in Figure 6.13. 86 0 5 10 15 20 25 30 35 40 45 50 1 12 23 34 45 56 67 78 89 100 111 122 133 144 Number of timesteps East zone Temperature[C] Figure 6.13. Controlled zone temperatures (cooling mode) 6.2.6. Heating mode operation and analysis Analogous to the cooling mode operation, the total heating sensible load demanded by all the zones being served is given by Equation (6.6). ControlZoneAirFlowFraction ControlZoneHeatingLoad HeatPumpHeatingLoad = ………………….. (6.6) The demand picks up at 7a.m as the building begins its operation as shown in Figure 6.14. Once the building stabilizes the daytime heating demand decreases gradually due to the presence of scheduled internal heat gains. Analogous to the cooling mode, the fan switches OFF when the heating load to be met by the heat pump is zero and switches ON when it is greater than zero. The profile of the fan electric power consumption as shown in Figure 6.15 is identical to the demands needed to be met by the heat pump. 87 0 10000 20000 30000 40000 50000 60000 1 14 27 40 53 66 79 92 105 118 131 144 Number of timesteps Heating demand[W] Figure 6.14. Demands for the winter design day 0 100 200 300 400 500 600 700 800 900 1 12 23 34 45 56 67 78 89 100 111 122 133 144 Number of timesteps Fan Electric Power[W] Figure 6.15. Fan electric power consumption (heating mode) Figure 6.16 shows excellent agreement between the heating demand and the demand met. The heat pump is operated at full load and its heating capacity under full load conditions is determined. 88 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 1 12 23 34 45 56 67 78 89 100 111 122 133 144 Number of timesteps Heating Demand/Met[W] Heating demand[W] Heating Demand Met[W] Demand exceeds Capacity Heat pump cannot meet demand Figure 6.16. Heating demand v/s demand met The run time fraction, actual heating capacity and the actual power for the heating load is calculated using equation 6.36.6. In the case study, the east zone temperature is controlled within the bounds of the set point at 20°C when the building is occupied and the heat pump is operating. The temperature peaks high for a few time steps when the system is switched off and then gradually drops to the set point at 15°C as shown in Figure 6.17. 0 5 10 15 20 25 1 12 23 34 45 56 67 78 89 100 111 122 133 144 Number of timesteps East Zone temperatures[C] Figure 6.17. Controlled zone temperatures (heating mode) 89 The sudden rise and drop in the temperatures occur at 7 a.m and 5 p.m occur as the system comes out of setback or goes into setback.. A few stand alone points show up between the 43rd and the 48th time step and then again between the 108th time step and the 111th time step as the building system tries to approach a steady state configuration. Some other important plots which are a significant part of this case study are discussed and compared with the detailed model in the next section. 6.2.7. Comparison with the Detailed Model The building discussed in the preceding sections is simulated again in EnergyPlus using the existing detailed model (Jin, Spitler; 2002). It is a parameter estimation based model which incorporates a multivariable unconstrained optimization algorithm to estimate model parameters. The detailed model is discussed in section 2.2.1. Comparison of the detailed and the simplified model shows that the new model is in good agreement with the detailed model when the heat pump operates at the rated air mass flow rate and wet bulb temperature. The demands, demand met, duty factor and the power consumption calculated using the detailed model match the simplified model at every point as shown by Figures 6.18 and 6.19. 90 0 5000 10000 15000 20000 25000 30000 1 12 23 34 45 56 67 78 89 100 111 122 133 144 Number of timesteps Cooling demand / Met [W] Cooling demand  Detailed Model[W] Cooling demandSimplified Model [W] Demand Met Detailed Model[W] Demand MetSimplified Model[W] Figure 6.18. Comparison of demand v/s met in the cooling mode 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 1 12 23 34 45 56 67 78 89 100 111 122 133 144 Number of timesteps Heating demand / Met [W] Heating demandDetailed [W] Heating demandSimplified [W] Heating demand MetSimplified [W] Heating Demand Met Detailed[W] Demand exceeds capacity Heat pump cannot meet demand Figure 6.19. Comparison of demand v/s met in the heating mode In cooling mode the heat pump operates with the highest duty factor of 0.6 in the 43rd time step due to the pickup load as shown in Figure 6.20. At this point the heat pump has to meet a capacity of about 20000W with a power consumption of about 5000W. In the heating mode the heat pump has to operate at full capacity to meet a demand of 91 about 45000W as shown in Figure 6.21. At this point the duty factor is 1 which means that the heat pump is operating at its peak. The points that do not occur along the smooth curve represent the time steps in which the system attempts to attain a steady state after experiencing a huge pick up load. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 1000 2000 3000 4000 5000 6000 Power consumption[W] Duty factor Detailed Simplified Figure 6.20. Comparison of duty factor v/s power consumption in the cooling mode 92 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 1000 2000 3000 4000 5000 6000 7000 8000 Power Consumption[W] Duty Factor Detailed Simplified Figure 6.21. Comparison of duty factor v/s power consumption in the heating mode 6.2.8. Model performance under off design conditions The design water flow rate at which the heat pump operates is 4.5 GPM. The heat pump offdesign performance is observed by changing the design flow rate to 4 GPM. The profile for both the heating and the cooling modes under off design conditions matches the design conditions as shown in Figures 6.22 and 6.23. This happens because the capacity of the heat pump is directly proportional to the water mass flow rate through it. However at points where the demands exceed capacity, the heat pump fails to meet the demand. 93 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 1 12 23 34 45 56 67 78 89 100 111 122 133 144 Number of timesteps Heating demand / Met [W] Heating demandSimplified [W] Heating demand Met(Off design)Simplified [W] Figure 6.22. Heating mode under off design conditions 0 5000 10000 15000 20000 25000 30000 1 12 23 34 45 56 67 78 89 100 111 122 133 144 Number of timesteps Cooling Demand/Met[W] Cooling Demand[W] Cooling MET[W]Off design Figure 6.23. Cooling mode under off design conditions As shown in section 5.4, the model’s sensitivity to wet bulb temperature is negligibly small for the case when the C1 term in equation 4.1 has been fit for constant wet bulb and variable flow rate (i.e. ClimateMaster data). Based on this analysis, offdesign wet bulb conditions are not shown in this section. 94 6.2.9. Annual Simulation Computation time For the case study input file, the parameter estimation model running in EnergyPlus takes approximately 35 minutes computing time for an annual simulation on a PC with Pentium 4, 2.4GHz CPU. The simple model takes approximately 7 minutes under identical conditions, which is about 80% faster than the parameter estimation model. 95 CHAPTER 7 PERFORMANCE COEFFICIENTS CALCULATOR 7.1. Need for an interface Computation of the model performance coefficients is an important prerequisite to executing accurate simulations. Although a generalized least square method will serve the purpose, calculation of every coefficient for different heat pump units becomes tedious without a graphical user interface. An interface was developed that not only calculates the performance coefficients, but also compares every calculated data point with the corresponding catalog data point in a graphical environment to demonstrate the consistency of the model coefficients with the catalog data. 7.2. Front end architecture The front end is a visual basic graphical user interface (GUI), the backend application is the generalized least square method. The program interacts with the user through a combination of menu driven event handling subfunctions. Event driven programming determines the sequence of operations for an application by the user’s interaction with the application interface (forms, menus, buttons, graphical components etc). Here, the user picks up the process to be performed and the event driven application remains in the background. The advantage of this design is that the application logic that processes events is clearly separated from the user interface logic that generates these events. 96 7.2.1. Workspace The key elements in designing an interface are deciding what the user sees, what data he will enter, what kind of warning boxes the application will use and how the application will handle inputs and outputs. Figure 7.1 shows the application window. The application begins by automatically asking the user to select the mode in which the heat pump is operating. Once the user clicks on the command button to select the mode, the application window interacts with the user again prompting him to click the type of performance variable coefficients to be generated. In the cooling mode, the user may click on the Cooling capacity, EER or Sensible capacity coefficients as shown in Figure 7.2. In the heating mode, the user selects heating capacity or COP coefficients. Once the user makes his selection, the application automatically loads the interface window. The interface window consists of a comprehensive menu that supports and conducts all important operations. Figure 7.3 shows the interface window. Operations such as opening a new file, clearing all the values on the interface window, saving the file and aborting the application can be performed under the FILE menu. The window accepts input parameters for the model equations and reports the values of the coefficients after back end processing. The window also calculates the model outputs and compares them with respective catalog data points to demonstrate the correctness of the coefficients. 97 Figure 7.1. Application window of the performance coefficient calculator 98 Figure 7.2. Window when the user clicks on cooling mode 99 Figure 7.3. The main interface window 100 7.2.2. Input format For each unit at the specified operating conditions, the following catalog and input data is needed • Total cooling capacity (Cooling Mode) • Sensible cooling capacity (Cooling Mode) • Heating capacity (Heating mode) • Energy efficiency ratio(EER) • Coefficient of performance(COP) • Nominal capacity of the heat pump unit • Nominal water mass flow rate • Inlet water mass flow rate • Entering air temperature • Entering water temperature • Nominal coefficient of performance of the heat pump unit • Nominal energy efficiency ratio of the heat pump unit The nominal values, which are determined from the manufacturer’s catalog are passed to the back end processor by using textboxes. Since the catalog data is the heat pump performance data at multiple points, it would be tedious to have the user enter every single operating point into the text box. Hence a grid component has been introduced as shown in Figure 7.3. The grid component gets information from a text file with the extension “.DAT”. The text file contains all the catalog input parameters with their values at the corresponding operating points as shown in Figure 7.4. 101 Figure 7.4. A typical model input file The grid component is also associated with a model definition file that understands the format of the input that is being fed into the grid component as shown in Figure 7.5. In other words, there is a correspondence between the model definition file and the input file. The model definition file has a “.pd” extension. The model definition file has to be loaded into the component before feeding the input information so that the grid component assimilates the type of information it needs to load. 102 Figure 7.5. A typical model definition file Once the definition and the inputs are passed into the component, the inputs are displayed on the interface window. Figure 7.6 shows an example of a pre processed interface window when Florida heat pump GT018 is loaded into it. 103 Figure 7.6. Preprocessed interface window 104 7.2.3. Reporting results After the coefficients are generated by the back end processor, which is the generalized least square algorithm, a validation technique is required to determine if the generated results are reasonable. The interface plots point to point values of the performance variables and then compares every calculated (Model) data point with the catalog data point. This feature triggers au 



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