saif abdulameer thesis prepared for the degree of master .../67531/metadc...field validation of zero...
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FIELD VALIDATION OF ZERO ENERGY LAB WATER-TO-WATER GROUND
COUPLED HEAT PUMP MODEL
Saif Abdulameer
Thesis Prepared for the Degree of
MASTER OF SCIENCE
UNIVERSITY OF NORTH TEXAS
May 2016
APPROVED:
Young Tao, Major Professor and Chair of the Department of Mechanical & Energy Engineering.
Kyle Horne, Committee Member Weihuan Zhao, Committee Member Costas Tsatsoulis, Dean of the College of
Engineering Mark Wardell, Dean of the Toulouse
Graduate School Assistant Professor, MEEN Department
Abdulameer, Saif. Field Validation of Zero Energy Lab Water-to-Water Ground
Coupled Heat Pump Model. Master of Science (Mechanical and Energy), May 2016, 59
pp., 8 tables, 29 figures, references, 34 titles.
Heat pumps are a vital part of each building for their role in keeping the space
conditioned for the occupant. This study focuses on developing a model for the ground-
source heat pump at the Zero Energy lab at the University of North Texas, and finding
the minimum data required for generating the model. The literature includes many
models with different approaches to determine the performance of the heat pump. Each
method has its pros and cons. In this research the equation-fit method was used to
generate a model based on the data collected from the field. Two experiments were
conducted for the cooling mode: the first one at the beginning of the season and the
second one at the peak of the season to cover all the operation conditions. The same
procedure was followed for the heating mode. The models generated based on the
collected data were validated against the experiment data. The error of the models was
within Β±10%. The study showed that the error could be reduced by 20% to 42% when
using the field data to generate the model instead of the manufacturerβs catalog data.
Also it was found that the minimum period to generate the cooling mode model was two
days and two hours from each experiment, while for the heating mode it was four days
and two hours from each experiment.
ii
Copyright 2016
by
Saif Abdulameer
iii
ACKNOWLEDGEMENT
I want to thank God for guidance through my life and study, and for providing me
this opportunity and granting me the capability to proceed successfully.
I would like to express my deepest appreciation and thanks to my advisor Dr. Yong
Tao, for his generous and continues support through this work. Thanks for your patient
and motivation during my dark hours. This project would not be possible without his
immense knowledge, wisdom and leadership.
I would like to extend my gratitude to Dr. Jungyon Mun for helping me
understanding the modeling and programing of EnergyPlus, and for keeping his door
open whenever I needed help.
Finally I wish to thank my fellow lab mates and friends Suraj, Pooya, Caleb and
Rodolfo for the stimulating discussions, for their support and for all the good time we had
together
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TABLE OF CONTENTS
ACKNOWLEDGEMENT .................................................................................................. iii
LIST OF TABLES ............................................................................................................vi
LIST OF FIGURS ........................................................................................................... vii
NOMENCLATURE ..........................................................................................................ix
CHAPTER 1 INTRODUCTION ........................................................................................ 1
1.1 Background ............................................................................................................ 1
1.2 Objective ................................................................................................................ 2
1.3 Impact of Research ................................................................................................ 3
CHAPTER 2 LITERATURE REVIEW .............................................................................. 4
2.1 Allen and Hamilton Model ...................................................................................... 4
2.2 Scarpa et al. Model ................................................................................................ 6
2.3 Stefanuk et al. Model ........................................................................................... 10
2.4 Jin and Spitler Model............................................................................................ 11
2.5 Lash Model .......................................................................................................... 14
2.6 Shenoy Model ...................................................................................................... 16
2.7 Tang Model .......................................................................................................... 17
CHAPTER 3 METHODOLOGY ..................................................................................... 20
3.1 Parameters Selection ........................................................................................... 20
3.2 Water-to-Water Heat Pump EnergyPlus Model Modification ................................ 23
3.3 Zero Energy Lab Testing Facility ......................................................................... 25
3.4 Experiment Setup ................................................................................................ 26
3.5 Experiments ......................................................................................................... 29
3.6 Data Collection ..................................................................................................... 30
3.7 Linear Regression Analysis .................................................................................. 32
3.8 Coefficients Generation........................................................................................ 33
3.9 EnergyPlus ........................................................................................................... 33
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3.10 Water-to-Water Curve Fit Model Simulation in EnergyPlus ................................ 35
CHAPTER 4 MODEL VERIFICATION .......................................................................... 37
4.1 Cooling Mode Verification .................................................................................... 37
4.2 Heating Mode Verification .................................................................................... 40
4.3 EnergyPlus Simulation ......................................................................................... 43
4.3.1 EnergyPlus Simulation Using Field Data Model ............................................. 44
4.3.2 EnergyPlus Simulation Using Catalog Data Model ........................................ 47
4.5 Results ................................................................................................................. 49
4.6 Error Analysis ....................................................................................................... 50
CHAPTER 5 CONCLUSION AND RECOMMENDATION ............................................. 53
5.1 Conclusions ......................................................................................................... 53
5.2 Future Work ......................................................................................................... 54
REFERENCES .............................................................................................................. 56
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LIST OF TABLES
Table 3.1: Heat pump models in EnergyPlus ................................................................ 23
Table 4.1: Cooling capacity Error for different models................................................... 40
Table 4.2: Cooling power Error for different models ...................................................... 40
Table 4.3: Heating capacity Error for different models .................................................. 43
Table 4.4: Heating power Error for different models ...................................................... 43
Table 4.5: EnergyPlus field coefficients ......................................................................... 43
Table 4.6: EnergyPlus field coefficients ......................................................................... 44
Table 4.7: Model error analysis ..................................................................................... 52
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LIST OF FIGURES
Figure 2.1: Water chiller system schematic ..................................................................... 6
Figure 2.2: scheme of model input/output. ...................................................................... 9
Figure 2.3: Water to air heat pump schematics ............................................................. 15
Figure 3.1: Water Furnance heat pump catalog data .................................................... 21
Figure 3.3: Heat pump capacity VS load side temp....................................................... 22
Figure 3.2: Heat pump capacity VS load side temp....................................................... 22
Figure 3.5: Heat pump power VS source side temp ...................................................... 22
Figure 3.4: Heat pump power VS load side temp .......................................................... 22
Figure 3.6: Zero Energy lab HVAC system drawing ...................................................... 25
Figure 3.8: Fluxis F601 flow meter installation .............................................................. 28
Figure 3.9: WATTNODE PULSE energy meter installation ........................................... 29
Figure 3.10: Loop reaction time for cooling mode ......................................................... 31
Figure 3.12: Multiple linear regression. ......................................................................... 32
Figure 3.13: EnergyPlus structure ................................................................................. 34
Figure 3.14: Water-to water heat pumps simulation lay out .......................................... 35
Figure 4.3: Validating 4 days 2 hours cooling capacity model ....................................... 39
Figure 4.5: Validating 10 days 10 hours heating capacity model .................................. 41
Figure 4.6: Validating 10 days 10 hours heating power model ...................................... 41
Figure 4.7: Validating 8 days 2 hours heating capacity model ...................................... 42
Figure 4.8: Validating 8 days 2 hours heating power model .......................................... 42
Figure 4.9: Field data model validation for cooling capacity simulation ......................... 45
Figure 4.11: Field data model validation for heating capacity simulation....................... 46
Figure 4.12: Field data model validation for heating power simulation .......................... 46
viii
Figure 4.13: Catalog data model validation for cooling capacity simulation .................. 47
Figure 4.14: Catalog data model validation for cooling power simulation ...................... 48
Figure 4.15: Catalog data model validation for heating capacity simulation .................. 48
Figure 4.16: Catalog data model validation for heating power simulation ..................... 49
Figure 4.17: Single day cooling capacity error analysis............................................... 511
Figure 4.18: Single day heating capacity error analysis .............................................. 511
ix
NOMENCLATURE
Cp Water specific heat
m Mass flow rate
Powerc Cooling power
Powerc,ref Cooling reference letter
Qc Cooling capacity
Qc,ref Reference cooling capacity
Qh heating capacity
Qh,ref Reference heating capacity
QL Load heat transfer
Qsource,c Cooling source heat transfer
Qsource,c,ref Cooling reference source heat transfer
Qsource,h Heating source heat transfer
Qsource,h,ref Heating reference source heat transfer
TL in Inlet load temperature
Tref 283 K
TS in Inlet Source temperature
VL Load side flowrate
VS Source side flowrate
CHAPTER 1
INTRODUCTION
The rapid increase in energy costs in the last decades draws more attention to find
alternative solutions for the conventional space condition units. Both building simulation
and actual utility bills show a great impact of the space heating and cooling on energy
usage. The ground source heat pumps use the ground as a source or sink for heat
because its temperature is more stable than the air temperature, leading to more energy
saving. While integrating the heat pump into simulation programs such as Energyplus is
based on the catalog data that is collected from laboratories to determine the unit
performance in partial and full load. This work offers a protocol to extract the data from
the field and generate a model for the heat pump based on the data.
1.1 Background
Heat pumps are a vital part of each building for their role in keeping the space
conditioned for the occupant. Heat pumps have a great impact on the utility bill, so
modeling them is the point of interest for many researchers. These models fall in a wide
spectrum ranging between curve fit models and deterministic models. Curve fit models
deal with the heat pump as a black box, and a model could be generated depending on
the catalog data. These models are simple and do not need a long time for the simulation.
On the other hand, there are the complicated deterministic models that are based on the
thermodynamic law and need data for each component of the heat pump, which may not
be available. Applying these models provides more accurate results but consumes more
time for the simulation. In addition, there are models that fall in between and get the
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advantage of both methods. Furthermore, there are numerical models that employ
iterative solvers and catalog data to determine the heat pump performance.
1.2 Objective
The focus of this research is to generate an equation fit model (which will be
referred as field data model) for a ground source water-to-water heat pump based on data
collected from the field and find the minimum time required for data collection. The model
will predict the performance of the heat pump capacity and power in both heating and
cooling modes. This study would provide a good tool for simulating aged heat pumps
when no catalog data is available or performance is degraded. Also it will determine the
actual performance of the equipment to help engineers to make the decision of
replacement or maintenance.
There are many approaches to simulate unitary heat pumps. Therefore, existing
models in EnergyPlus along with other models developed by other researchers are
discussed in Chapter 2. The data collection from the field and model development are
discussed in Chapter 3. The model was modified based on previous researchersβ models
so the coefficients could be generated based on constant flow rate. Two experiments
were conducted for each mode, one at the beginning of the season and the second one
at the peak of the season. The results of the models were described in Chapter 4 in
addition to the simulation results in EnergyPlus using the coefficients that were generated
based on field data and compared to the EnergyPlus simulation results of coefficients
generated from manufacturer catalog data. Finally, the results of the models were
discussed in Chapter 5 with recommendation for future work.
2
1.3 Impact of Research
The field model presented in this study will help in improving the results of energy
simulation process especially for the installed heat pumps in existing buildings. For
example this method could be used in performing the base line energy simulation for an
existing building to acquire a LEED Green Building certification that proves the
sustainability of the structure. By utilizing this model the base line energy simulation
results would be closer to the actual performance. Most of the current methods perform
the heat pumps simulation based on the catalog data which is recorded in a laboratory
environment and often over estimating the performance leading to inaccurate results.
Also the field model could help the mechanical, electrical and plumping engineers (MEP)
to evaluate the efficiency of their designs based on the actual operational conditions.
Furthermore the heat pump manufacturers could use this study to evaluate the long-term
performance of heat pumps beyond the laboratory conditions.
3
CHAPTER 2
LITERATURE REVIEW
The heat pump performance accuracy is a challenge for the HVAC designers and
energy auditors because it depends on the boundary conditions. This problem led to the
development of many models, each one with a different approach and requirements. The
energy simulation programs always prefer the simple models that do not demand many
inputs for the ease of use and less simulation time, but that is in the favor of results
accuracy. Implementing complex models to perform the simulation faces the obstacle of
data availability as the heat pump manufacturers do not provide comprehensive data in
the catalog. These models provide more accurate results but require longer time to
simulate. A literature review reveals the pros and cons of the existing models.
2.1 Allen and Hamilton Model
This model was developed by Allen and Hamilton (1983) to model a steady state
reciprocation water chiller in full and part load performance. They generated this model
by using catalog data only, treated the unit as one component, and did not use any
internal pressures or temperatures. Figure 2.1 shows the components of the water chiller
system and the parameters used to generate the model. Basic heat transfer laws could
have been used to find the heat transfer through the evaporator and the condenser.
However, the author used a six-term polynomial to find these values as follow.
Evaporator heat transfer
ππΈ = π1ππΈ2 + π2ππΆ2 + π3ππΈ2ππΆ2 + π4ππΈ22 + π5ππΆ2
2 + π6 (2.1)
Compressor polynomial
ππΈ = π1ππΈ2 + π2ππΆ2 + π3ππΈ2ππΆ2 + π4ππΈ22 + π5ππΆ2
2 + π6 (2.2)
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The energy balance for the system is described by the following equations.
ππΈ = ππΈ β πΆπ β (ππΈ1 β ππΈ2) (2.3)
ππΆ = ππΆ β πΆπ β (ππΆ1 β ππΆ2) (2.4)
ππΆ = ππΈ + π (2.5)
Where
b1-b12 Coefficients fitted by polynomial regression
TE1,TE2 Evaporator water entering and leaving temperature
TC1,TC2 Condenser water entering and leaving temperature
Cp Specific heat at constant pressure
P Compressor Power
QE Evaporator heat transfer rate
QC Condenser heat transfer rate
mE Evaporator mass flow rate
mC Condenser mass flow rate
The polynomial constant coefficients B1-B12 are determined by regression
approach. The performance of the water chiller represented by energy rates QC and QC
and leaving water temperature TC2 and TE2 is determined by solving the above five
equations and providing the inlet evaporator temperature TE1 with the mass flow rate ME
and condenser inlet temperature TC1 with the mass flow rate MC.
5
Figure 2.1: Water chiller system schematic
2.2 Scarpa et al. Model
Scarpa et al. (2012) proposed a numerical model to simulate a chiller driven by
volumetric compressor or a vapor compression-based heat pump. Employing an iterative
solver and boundary conditions, the model could predict the thermodynamic inverse
cycle. However, the model is derived from the catalog data, and it is able to determine
the behavior of the heat pump under boundary conditions far from the ones it is derived
from by utilizing the user side and ambient side heat flow rates and electrical power
consumption, under boundary conditions. The model runs in two calculation steps: the
first one is determination of the basic parameters of the specific heat pump from catalog
data as shown in Figure 2.2, and the output would be the parameter that is used in the
second step as shown in Figure 2.2. The equations used in this phase are as follow:
Compressor power
Condenser
Expansion valve
TE2
Compressor
TC1
TE1
Evaporator
TC2 MC
ME
P
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οΏ½οΏ½πΆπππππππ (
οΏ½οΏ½πΌππ‘πππ
πΆπππππ β οΏ½οΏ½π΄π’π₯πππ) . ππππ‘ππ (2.6)
Computing the fluids flow rate at the source side and the sink side from the catalog
data.
οΏ½οΏ½ππΉ:πΌππ‘πππ =
ππΌππ‘πππ
πΆπ,ππΉ:πΌππ‘.|ΖππΉ:πΌππ‘,ππ’π‘πππ βΖππΉ:πΌππ‘,πΌπ
πππ |(2.7)
οΏ½οΏ½ππΉ:πΈπ₯π‘πππ =
ππΈπ₯π‘πππ
πΆπ,ππΉ:πΈπ₯π‘ .|ΖππΉ:πΌππ‘,ππ’π‘πππ βΖππΉ:πΈπ₯π‘,πΌπ
πππ |(2.8)
The effectiveness index of the heat exchanger is the only user assumption, and it is
selected from a scale of 0 to 10. Then the thermal effectiveness is calculated for the
evaporator and condenser
ΤπΌππ‘ =|ΖππΉ:πΌππ‘,πΌπ
πππ βΖππΉ:πΌππ‘,ππ’π‘πππ |
|ΖππΉ:πΌππ‘,πΌππππ βΖπ πΉ:πΌππ‘
πππ |(2.9)
ΤπΌππ‘ =|ΖππΉ:πΈπ₯π‘,πΌπ
πππ βΖππΉ:πΈπ₯π‘,ππ’π‘πππ |
|ΖππΉ:πΈπ₯π‘,πΌππππ βΖπ πΉ:πΈπ₯π‘
πππ |(2.10)
Calculate flow rate for the refrigerant
οΏ½οΏ½ = οΏ½οΏ½π πΉπππ . π2
πππ =οΏ½οΏ½ππΉ:πΈπ£ππ
πππ
β2πππ . π2
πππ (2.11)
Calculate enthalpy at the compressor outlet
β3πππ = β2
πππ +οΏ½οΏ½πΆππππ
πππ
οΏ½οΏ½π πΉπππ (2.12)
Calculation of compressor efficiency using catalog data
ππΌπ πππ =
ππ πΉπππ .(β3π
πππββ2πππ)
οΏ½οΏ½πΆπππππππ (2.13)
Step two of the simulation starts using the above parameters to determine the full load
performance of the heat pump at the desired actual boundary conditions. To start the
process the following inputs are required.
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Where:
COP coefficient of performance
Cp specific heat of fluid (J/(kg
K))
f factor
h specific enthalpy (J/kg)
I index
m mass flow rate (kg/s)
Q power or heat flow (W)
T temperature (K)
v specific volume (m3/kg)
ΛV volumetric flow rate (m3/s)
Ξ΅ effectiveness
π efficiency
Ζ temperature (β¦C)
Aux auxiliary devices
Compr compressor
Cond condenser
Cool cooling
El electric
Evap evaporator
Ex exergetic
Ext external side
In inlet
Int internal side
Is isentropic
Motor motor
Out outlet
RF refrigerant fluid
SF secondary fluid
Tot total
X part load
Nom nominal
8
Figure 2.2: scheme of model input/output.
Secondary fluids
Efficiency of electric motor
Refrigerant fluid
Effectiveness index
COP
Thermal capacity
Auxiliary power
Condenser fluid temp
Evaporator Fluid temp
Secondary fluids
Thermal effectiveness of condenser
Thermal effectiveness of evaporator
Refrigerant flow rate
Isentropic compressor efficiency
Mass flow rates of secondary fluids
Phase one
Condenser flow rate Evaporator flow rate
Condenser inlet Temp Evaporator inlet Temp
Running Mode
Hourly output:
Maximum thermalCapacity
COP
Outlet temperature ofsecondary fluids
Phase Two
9
Mass flow rate and inlet temperature of the external side (ambient side) secondary
fluid
Mass flow rate and inlet temperature of the internal side (user side) secondary fluid
Running model (cooling or heating);
Electrical power consumed by the auxiliary components
The nominal capacities for the evaporator and condenser assumed to be the start
values for the heat flow, considering constant effectiveness of the heat exchanger, the
software calculates the evaporating and condensing temperature of the refrigerant from
the previous calculation and step one outputs the reverse cycle could be determined.
After that the program compares the calculated heat capacity against the assumed. If the
error is within the acceptable tolerance limits, the process stops. If not, the new iteration
starts and assumes the calculated heat flow rates as starting points. This model is easy
to integrate into a simulation program, and according to the author the error would be
within Β±10%, but it is not possible to determine the heat pump performance in partial load
conditions and this is a critical value for the energy simulation.
2.3 Stefanuk et al. Model
The Stefanuk et al. (1992) model presents a steady state model for a water-to-
water heat pump. The model is based on the equations of states, basic conservation laws
of mass, energy, and momentum in addition to the correlation of heat transfer. According
to the author the model could predict the performance of the heat pump over the full
operation range because it is derived from the basic laws. The heat pump was divided
into four main components: compressor, evaporator, condenser, and expansion device.
Each part has its own model and algorithm. The evaporator model accounts for both heat
10
transfer modes: the forced convective boiling of the two-phase refrigerant and forced
convection through a superheated refrigerant vapor. A simulation algorithm is used to
calculate the unknown heat transfer variables (the mass flow rate of refrigerant in the
evaporator and the required surface area for heat transfer) using the inlet source fluid
temperature and mass flowrate, the refrigerant mass flow rate, the refrigerant evaporation
temperature, and pressure. The condenser model and algorithm are similar to the
evaporator, but they account for the three different phases of the refrigerant: forced
convection in the inlet superheated refrigerant vapor, forced-convection condensation of
the two-phase refrigerant, and forced convection in the outlet subcooled at the outlet of
the condenser. The compressor model accounts for the pressure drop in the inlet and
outlet valves, and it predicts the mass flow rate, electrical power, and thermodynamics of
refrigerant compression fit data. The expansion device model assumed the device is
adiabatic and did not account for any thermal or electrical control for it. The model
assumed no pressure or temperature drop in the connecting pipes. Though the model
shows a good error percentage of Β±10%, it is considered a deterministic model, which
demands many inputs and at some point it uses correlation to variables.
2.4 Jin and Spitler Model
Jin and Spitler (2002) developed a parameter estimation model for both water-to-
water heat pump and water-to-air heat pump. The author used deterministic model to
describe each part of the heat pump. Although this selection requires many parameters
that may not be available by the manufacturer catalogs, the model utilizes a multivariable
unconstrained optimization algorithm to estimate these parameters. The heat pump is
modeled as four main components: compressor, evaporator, condenser, and expansion
11
device. Other parts were neglected due to minor effect on the thermodynamic properties
of the system. The compressor model assumes an isentropic pressure for the
compression, no effects on the refrigerant by the oil, and that the pressure drops in suction
and discharge valves are isentropic. The estimation of compressor parameters leads to
estimating the refrigerant mass flow rate. Evaporator and condenser models assume a
negligible pressure drop in the heat exchangers. The model was derived from the
fundamental analysis of the counter-flow heat exchangers. The expansion device does
not model directly. Instead of that the amount of super heat is held constant, and the
refrigerant mass flow rate is determined by the compressor model. This approach works
for heat pumps with thermostatic expansion valve but may not work if the devise is a
capillary tube. The estimation procedure starts by inputting catalog data and applying
routines to adjust the parameter values to minimize the error between the model results
and the actual values. The flow diagram of the parameter estimation model shown in
Figure 2.3.
12
Figure 2.3: Flow diagram for model implementation computer program
Inputs
The values of the parameters for both cooling & heating mode Load & Source
water mass flow rates & entering temps Thermostat signal
Initial guess for condenser and evaporator heat flow rate
Evaporator and condenser effectiveness
Refrigerant condensing and evaporating temp
Determine evaporator and condesor Exit points
Apply pressure drop for suction and discharge
Calculate mass flow rate for refrigerant
From compressor model find: Power consumption W
Cooling Capacity οΏ½οΏ½πΏ = οΏ½οΏ½π Β· (βπ΄ β βπ΅)
Heat rejection οΏ½οΏ½π = π + οΏ½οΏ½πΏ
Output: Cooling capacity Power Consumption
if
Suction pressure < low pressure cut off
Discharge pressure > High pressure cut off
If
π΄π΅π(οΏ½οΏ½πΏππ’ππ π β οΏ½οΏ½πΏ)/οΏ½οΏ½πΏππ’π π < πππ
π΄π΅π(οΏ½οΏ½πππ’ππ π β οΏ½οΏ½π)/οΏ½οΏ½πππ’π π < πππ
END
if
Evaporating pressure < low pressure cut off
Condenser pressure > High pressure cut off
END
END
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2.5 Lash Model
This model has been developed to simulate the heat pump performance in a water-
loop heat pump that consists of other three components including a heat rejection unit, a
supplementary heating unit, and water circulation pump. A model has been developed for
each one of these parts and then combined and implemented into BLAST energy analysis
program. The model intended to simulate a water-to-air packaged unit with a reversible
cycle as shown in Figure 2.3. Lash proposed that the heat pump performance is a function
of the loop temperature, ambient air temperature, and mass flow rate because the heat
pump always works at a specific inlet source temperature. The proposed parameters are
always available as a requirement of the ARI certification program for the water source
heat pump. The equations that govern the heat pump performance are as follow:
Cooling mode:
πΆππππππ‘π¦
π΅ππ ππππ= π΄1 + π΅1 [
ππΏπππ
ππ ππ] + πΆ1 [
ππ ππ
οΏ½οΏ½π΅ππ π] [
οΏ½οΏ½
ππ€π] (2.14)
πΈπΈπ
π΅ππ ππΈπΈπ = π·1 + πΈ1 [
ππΏπππ
ππ ππ] + πΉ1 [
ππ ππ
οΏ½οΏ½π΅ππ π] [
οΏ½οΏ½
ππ€π] (2.15)
Heating mode:
πΆππππππ‘π¦
π΅ππ ππππ= π΄2 + π΅2 [
ππΏπππ
ππ ππ] + πΆ2 [
ππ ππ
οΏ½οΏ½π΅ππ π] [
οΏ½οΏ½
ππ€π] (2.16)
πΆππ
π΅ππ ππΆππ= π·2 + πΈ2 [
ππΏπππ
ππ ππ] + πΉ2 [
ππ ππ
οΏ½οΏ½π΅ππ π] [
οΏ½οΏ½
ππ€π] (2.17)
Tref 283 K or 511 Β°R
TLoop The loop temperature (Β°R or K)
mBase The rated mass flow per unit of capacity multiplied by the base capacity
14
mBase ππ΅ππ π [οΏ½οΏ½
π]
π ππ
m The mass flow rate of water through the pump.
Tdb, Twb The dry bulb and wet bulb air temperatures. (Β°R or K)
Qbase The base capacity of the heat pump unit. (kW)
[οΏ½οΏ½
π]
π ππThe ratio of design mass flow rate to design capacity. (kg/kJ)
Figure 2.3: Water to air heat pump schematics
The model did not account for the transient start up behavior because the transient
effect diminishes after 3 minutes of running time as proposed by equation (2.18), which
correlates the transient performance of simple on-off control scheme heat pumps.
οΏ½οΏ½
ππ π = 1 β π΄π(βπ‘/π) (2.18)
Compressor
Fan
Air
ref
rige
ran
t
hea
t ex
chan
ger
Reversing valve
Expansion valve Water refrigerant
heat exchanger
15
A, r are constants. (Experimental values are .5 and 1 respectively)
Q is the transient heat transfer of the coil.
Qss is the steady state heat transfer rate of the coil.
t is the time that the heat pump operates.
This model is considered to be insensitive to the change of air flow rates from the
base value because it did not account for the change of capacities with different air flow
rates.
2.6 Shenoy Model
Shenoy (2004) presented an equation fit model by developing Lash (1990) model
to be used in the Energyplus model. The model also accounted for the sensible and latent
heat capacities for heat pump while working in the cooling mode. Based on the
psychometric chart and the manufacturer data the model variables relationships were
presented as below:
β β β β ππ΅ (2.19)
ππ€ππ β 1
ππ€π(2.20)
ππΆ β οΏ½οΏ½π€ (2.21)
ππΆ β οΏ½οΏ½π€
The final equations for the developed model are listed below:
Cooling Mode:
ππ
ππππ π= π΄1 + π΅1 [
ππππ
ππππ
οΏ½οΏ½πβπππ π
οΏ½οΏ½π] + πΆ1 [
ππππ
ππ€π] [
οΏ½οΏ½π€
οΏ½οΏ½π€βπππ π] (2.22)
16
πΈπΈπ
πΈπΈπ πππ π= π·1 + πΈ1 [
ππππ
ππππ
οΏ½οΏ½πβπππ π
οΏ½οΏ½π] + πΉ1 [
ππππ
ππ€π] [
οΏ½οΏ½π€
οΏ½οΏ½π€βπππ π] (2.23)
πππππ
ππ πππ βπππ π= πΊ1 + π»1 [
ππππ
ππππ
οΏ½οΏ½πβπππ π
οΏ½οΏ½π] + πΌ1 [
ππππ
ππ€π] [
οΏ½οΏ½π€
οΏ½οΏ½π€βπππ π] + π½1 [
ππππ
πππ] [
οΏ½οΏ½π€
οΏ½οΏ½π€βπππ π] (2.24)
Heating Mode:
πβ
ππππ π= π΄1 + π΅1 [
ππππ
ππππ
οΏ½οΏ½πβπππ π
οΏ½οΏ½π] + πΆ1 [
ππππ
ππ€π] [
οΏ½οΏ½π€
οΏ½οΏ½π€βπππ π] (2.25)
πΆππ
πΆπππππ π= π·1 + πΈ1 [
ππππ
ππππ
οΏ½οΏ½πβπππ π
οΏ½οΏ½π] + πΉ1 [
ππππ
ππ€π] [
οΏ½οΏ½π€
οΏ½οΏ½π€βπππ π] (2.26)
The model was applied on two different manufacturer heat pumps with the same
capacities. Shenoy (2004) also investigated the data out of the catalog range by
expanding the data points using the correction factors. The error reached 13% for the
sensible cooling capacity.
2.7 Tang Model
Tang (2005) proposed two models: one for the water-to-air heat pump and the
other for the water-to-water heat pump to be implemented in Energyplus. The water-to-
air heat pump model is a modification for Lash (1992) and Shenoy (2004) in order to
reduce the error from the last two models. The model was derived from the catalog data
after expanding the data points by using the catalog correction factors. The modification
to Shenoy (2004) is to add other terms to the equation to separate the inlet temperature,
air flow rate, and water flow rate because the heating capacity and heat absorption are a
strong function of the water inlet temperature and a weak function of the air flow rate. The
final forms of the equations are:
Cooling Mode:
17
ππ‘ππ‘ππ
ππ‘ππ‘ππ,πππ= π΄1 + π΄2 [
ππ€π
ππππ] + π΄3 [
ππ€,ππ
ππππ] + π΄4 [
οΏ½οΏ½πππ
οΏ½οΏ½πππ,πππ] + π΄5 [
οΏ½οΏ½π€
οΏ½οΏ½π€,πππ] (2.27)
ππ πππ
ππ πππ ,πππ= π΅1 + π΅2 [
πππ
ππππ] + π΅3 [
ππ€π
ππππ] + π΅4 [
ππ€,ππ
ππππ] + π΅5 [
οΏ½οΏ½πππ
οΏ½οΏ½πππ,πππ] + π΅6 [
οΏ½οΏ½π€
οΏ½οΏ½π€,πππ] (2.28)
πππ€πππ
πππ€πππ,πππ= πΆ1 + πΆ2 [
ππ€π
ππππ] + πΆ3 [
ππ€,ππ
ππππ] + πΆ4 [
οΏ½οΏ½πππ
οΏ½οΏ½πππ,πππ] + πΆ5 [
οΏ½οΏ½π€
οΏ½οΏ½π€,πππ] (2.29)
ππ ππ’πππ,π
ππ ππ’πππ,π,πππ= π·1 + π·2 [
ππ€π
ππππ] + π·3 [
ππ€,ππ
ππππ] + π·4 [
οΏ½οΏ½πππ
οΏ½οΏ½πππ,πππ] + π·5 [
οΏ½οΏ½π€
οΏ½οΏ½π€,πππ] (2.30)
Heating mode:
πβ
πβ,πππ= πΈ1 + πΈ2 [
πππ
ππππ] + πΈ3 [
ππ€,ππ
ππππ] + πΈ4 [
οΏ½οΏ½πππ
οΏ½οΏ½πππ,πππ] + πΈ5 [
οΏ½οΏ½π€
οΏ½οΏ½π€,πππ] (2.31)
πππ€ππβ
πππ€ππβ,πππ= πΉ1 + πΉ2 [
πππ
ππππ] + πΉ3 [
ππ€,ππ
ππππ] + πΉ4 [
οΏ½οΏ½πππ
οΏ½οΏ½πππ,πππ] + πΉ5 [
οΏ½οΏ½π€
οΏ½οΏ½π€,πππ] (2.32)
ππ ππ’πππ,β
ππ ππ’πππ,β,πππ= πΊ1 + πΊ2 [
πππ
ππππ] + πΊ3 [
ππ€,ππ
ππππ] + πΊ4 [
οΏ½οΏ½πππ
οΏ½οΏ½πππ,πππ] + πΊ5 [
οΏ½οΏ½π€
οΏ½οΏ½π€,πππ] (2.33)
After applying these modifications the error was around 6%. The results were verified by
using Jinβs (1999) parameter estimation model. The water-to-water heat pump model
developed using the water-to-air heat pump methodology. To find the coefficient the
generalized least square method is implemented on the catalog data at indicated
reference conditions. The heating and cooling models equations are as below:
Cooling Mode:
ππ‘ππ‘ππ
ππ‘ππ‘ππ,πππ= π΄1 + π΄2 [
ππΏ,ππ
ππππ] + π΄3 [
ππ,ππ
ππππ] + π΄4 [
οΏ½οΏ½πΏ
οΏ½οΏ½πΏ,πππ] + π΄5 [
οΏ½οΏ½π
οΏ½οΏ½π,πππ] (2.34)
πππ€πππ
πππ€πππ,πππ= π΅1 + π΅2 [
ππΏ,ππ
ππππ] + π΅3 [
ππ,ππ
ππππ] + π΅4 [
οΏ½οΏ½πΏ
οΏ½οΏ½πΏ,πππ] + π΅5 [
οΏ½οΏ½π
οΏ½οΏ½π,πππ] (2.35)
ππ ππ’πππ,π
ππ ππ’πππ,π,πππ= πΆ1 + πΆ2 [
ππΏ,ππ
ππππ] + πΆ3 [
ππ,ππ
ππππ] + πΆ4 [
οΏ½οΏ½πΏ
οΏ½οΏ½πΏ,πππ] + πΆ5 [
οΏ½οΏ½π
οΏ½οΏ½π,πππ] (2.36)
Heating mode:
18
πβ
πβ,πππ= π·1 + π·2 [
ππΏ,ππ
ππππ] + π·3 [
ππ,ππ
ππππ] + π·4 [
οΏ½οΏ½πΏ
οΏ½οΏ½πΏ,πππ] + π·5 [
οΏ½οΏ½π
οΏ½οΏ½π,πππ] (2.37)
πππ€ππβ
πππ€ππβ,πππ= πΈ1 + πΈ2 [
ππΏ,ππ
ππππ] + πΈ3 [
ππ,ππ
ππππ] + πΈ4 [
οΏ½οΏ½πΏ
οΏ½οΏ½πΏ,πππ] + πΈ5 [
οΏ½οΏ½π
οΏ½οΏ½π,πππ] (2.38)
ππ ππ’πππ,β
ππ ππ’πππ,β,πππ= πΉ1 + πΉ2 [
ππΏ,ππ
ππππ] + πΉ3 [
ππ,ππ
ππππ] + πΉ4 [
οΏ½οΏ½πΏ
οΏ½οΏ½πΏ,πππ] + πΉ5 [
οΏ½οΏ½π
οΏ½οΏ½π,πππ] (2.39)
Where:
TL in Inlet load temperature
TS in Inlet Source temperature
Tref 283 K
VS Source side flowrate
VL Load side flowrate
Q total Cooling capacity
Q total,ref Reference cooling capacity
Powerc Cooling power
Powerc,ref Cooling reference letter
The reference conditions are critical for this model. The same references used to
generate the model should be used when applying the model. Tangβs (2005) model could
not be used to generate model from fixed inlet conditions.
19
CHAPTER 3
METHODOLOGY
This chapter will discuss the development of one of Energyplus models in addition
to its bases in order to meet the purpose of this research to simulate the actual
performance of ground-coupled heat pump by using field data instead of the catalog data.
Basically there are two models for the water-to-water heat pump available in Energyplus:
the curve fit water-to-water model, which was developed by Tang (2005) and the
parameter estimation, which was implemented by Jin (2002).
3.1 Parameters Selection
In order for a heat pump manufacturer to sell its product, the equipment should
comply with ARI or ISO 13256-1. The rating program provides a good picture of the
factors that impact the rated performance of ground source heat pump. These standards
require the manufacturer to provide the performance of the heat pump under different
interring water temperatures to demonstrate the efficiency of the heat pump. Most of
ground-source heat pump manufacturer provide that in addition to the performance under
different load temperature and mass flow rates. An example for a manufacturerβs catalog
data for water furnace heat pump in cooling mode is shown in Figure 3.1.
20
Figure 3.1: Water Furnance heat pump catalog data
21
The advantage of implementing these parameters to generate a model from the
field data to compare its results to the results generated form catalog data model. Also
these physical properties do not need invasive measurement tools. In addition field
measurements also showed a correlation between these parameters and the heat pump
performance as shown in Figures 3.2 -3.5.
30
35
40
45
50
55
60
65
70
17000 19000 21000 23000
Load
Tem
pra
ture
F
Cooling Capacity (Btu/h)
TL in
65
67
69
71
73
75
77
15000 17000 19000 21000 23000Su
rce
Tem
pra
ture
F
Capacity (Btu/h)
TS in
40
45
50
55
60
1 1.2 1.4 1.6Load
Sid
e Te
mp
erat
ure
F
Cooling Power kW
TL in
72
74
76
78
80
82
84
86
88
1 1.2 1.4 1.6
Sou
rce
Tem
pra
ture
F
Cooling Power KW
TS in
Figure 3.2: Heat pump capacity VS
load side temp
Figure 3.3: Heat pump capacity VS
load side temp
Figure 3.5: Heat pump power VS
source side temp
Figure 3.4: Heat pump Power VS
load side temp
22
3.2 Water-to-Water Heat Pump EnergyPlus Model Modification
There are several models for the heat pumps in EnergyPlus depending on the
equipment type, and the method used to make the model whether it is a curve-fit of
parameter estimation. Table 3.1 shows a summary of heat pump models in EnergyPlus.
Heat pump type Implemented to E+ by Developer
Cu
rve
-fit mod
el
Air-to-Air Buhl & Shirey DOE 2
Water-to-Air Shenoy (2004) & Tang
(2005) Lash (1992)
Water-to-Water Tang (2005) Tang (2005)
Pa
ram
ete
r
Estim
atio
n
mo
de
l
Water-to-Air Fisher and Tang Jin (2002)
Water-to-Water Murugappan (2002) Jin (2002)
Table 3.1: Heat pump models in EnergyPlus
The current equation-fit model in EnergyPlus was implemented by Tang (2005). It
is based on the manufacturer catalog data to generate a set of performance coefficients
by using the generalized least square method according to the following equations:
Cooling mode:
ππ
ππ,πππ= π΄1 + π΄2 [
ππΏ,ππ
ππππ] + π΄3 [
ππ,ππ
ππππ] + π΄4 [
οΏ½οΏ½πΏ
οΏ½οΏ½πΏ,πππ] + π΄5 [
οΏ½οΏ½π
οΏ½οΏ½π,πππ] (3.1)
πππ€πππ
πππ€πππ,πππ= π΅1 + π΅2 [
ππΏ,ππ
ππππ] + π΅3 [
ππ,ππ
ππππ] + π΅4 [
οΏ½οΏ½πΏ
οΏ½οΏ½πΏ,πππ] + π΅5 [
οΏ½οΏ½π
οΏ½οΏ½π,πππ] (3.2)
ππ ππ’πππ,π
ππ ππ’πππ,π,πππ= πΆ1 + πΆ2 [
ππΏ,ππ
ππππ] + πΆ3 [
ππ,ππ
ππππ] + πΆ4 [
οΏ½οΏ½πΏ
οΏ½οΏ½πΏ,πππ] + πΆ5 [
οΏ½οΏ½π
οΏ½οΏ½π,πππ] (3.3)
Heating mode:
πβ
πβ,πππ= π·1 + π·2 [
ππΏ,ππ
ππππ] + π·3 [
ππ,ππ
ππππ] + π·4 [
οΏ½οΏ½πΏ
οΏ½οΏ½πΏ,πππ] + π·5 [
οΏ½οΏ½π
οΏ½οΏ½π,πππ] (3.4)
23
πππ€ππβ
πππ€ππβ,πππ= πΈ1 + πΈ2 [
ππΏ,ππ
ππππ] + πΈ3 [
ππ,ππ
ππππ] + πΈ4 [
οΏ½οΏ½πΏ
οΏ½οΏ½πΏ,πππ] + πΈ5 [
οΏ½οΏ½π
οΏ½οΏ½π,πππ] (3.5)
ππ ππ’πππ,β
ππ ππ’πππ,β,πππ= πΉ1 + πΉ2 [
ππΏ,ππ
ππππ] + πΉ3 [
ππ,ππ
ππππ] + πΉ4 [
οΏ½οΏ½πΏ
οΏ½οΏ½πΏ,πππ] + πΉ5 [
οΏ½οΏ½π
οΏ½οΏ½π,πππ] (3.6)
This model is not capable of generating the coefficients for constant flow rate conditions,
but the water-to-water heat pump in the Zero Energy Lab has a constant flow for the load
side and source side. In order to overcome this obstacle, a modification is proposed to
the original equations by removing the flow coefficients because they will not affect the
results as long as the flow is constant during the heat pump operation. The simple linear
regression is used to find the performance coefficients. The equations after modification
to generate a field data model become as follow:
Cooling mode:
ππ
ππ,πππ= π΄1 + π΄2 [
ππΏ,ππ
ππππ] + π΄3 [
ππ,ππ
ππππ] (3.7)
πππ€πππ
πππ€πππ,πππ= π΅1 + π΅2 [
ππΏ,ππ
ππππ] + π΅3 [
ππ,ππ
ππππ] (3.8)
ππ ππ’πππ,π
ππ ππ’πππ,π,πππ= πΆ1 + πΆ2 [
ππΏ,ππ
ππππ] + πΆ3 [
ππ,ππ
ππππ] (3.9)
Heating mode:
πβ
πβ,πππ= π·1 + π·2 [
ππΏ,ππ
ππππ] + π·3 [
ππ,ππ
ππππ] (3.10)
πππ€ππβ
πππ€ππβ,πππ= πΈ1 + πΈ2 [
ππΏ,ππ
ππππ] + πΈ3 [
ππ,ππ
ππππ] (3.11)
ππ ππ’πππ,β
ππ ππ’πππ,β,πππ= πΉ1 + πΉ2 [
ππΏ,ππ
ππππ] + πΉ3 [
ππ,ππ
ππππ] (3.12)
24
3.3 Zero Energy Lab Testing Facility
The experiment has been conducted at the University of North Texas in the Zero
Energy Lab, which is a 1,200 square foot facility that is designed to imitate a small
residence. The occupants of the building are students practicing normal activities. The
building is conditioned by two ground-source heat pumps: the first is a water-to-water
heat pump that supplies hot or cold water to a radiant slab and the second unit is a water-
to-air heat pump. The heat pumps are programmed so the WAHP works when the WWHP
can not meet the load. Figure 3.6 shows a schematic of the system. Both heat pumps are
connected to a six parallel vertical ground heat exchanger and each bore is 67 meter in
depth.
The space is monitored and controlled by using a building management software TAC
Vista and 160 sensors that measures different values.
Figure 3.6: Zero Energy lab HVAC system drawing
25
3.4 Experiment Setup
The subject of the experiment is a Florida water-to-water heat pump that is shown
in Figure 3.7. The capacity of the unit is 7 kW in cooling and 9 kW in heating. The load
side is connected to a radiant concrete floor with embedded 573 m Polyethylene pipes of
0.0127 m inside diameter while the source (or skink) side is connected to a ground source
heat exchanger of 0.0635m radiuses. U-bend pipes manufactured from Polyethylene
material are inserted into the boreholes. The boreholes are then grouted with thermal
conductivity of 0.6926 W/m-k. In order to collect the data for generating the model for the
ground-source heat pump, different types of sensors were used to measure each physical
variable of the experiment parameters. Also measuring tools were used to find the
necessary values to do the simulation in EnergyPlus.
Figure 3.7: Water-to-water Florida heat pump
26
Measuring the temperatures was done using four ET series sensors shown in
Figure 3.8. The ET sensor is an immersion temperature transmitter that is installed in the
inlet and the outlet of both the condenser and the evaporator. The transmitter converts
the measured temperature into an electronic current signal. The sensor was installed in
a pocket that was immersed in the fluid. The sensor accuracy is Β±0.1 % C of range. For
measuring the flow rates Fluxis F601 flow meter, which is shown in Figure 3.9, was used,
and it is an ultra-sonic flow meter that has clamp-on transducers for non-intrusive
measured installation and measurement. This device requires a laminar flow to give
accurate results, but because of the limitation of free pipe from fittings, the sensors were
installed on a point close to the fitting. The error of this installation, according to the
manufacturing companyβs technical department, is Β±5%.
Figure 3.8: ET temperature sensors
Temperature sensors
27
The energy and power are a critical part of the experiment for measuring these
parameters. The WATTNODE PULSE is used, which is an accurate AC watt-hour
transducer with pulse output (solid state relay closure) proportional to kWh consumed or
produced. The WATTNODE meter provides real energy measurement for sub-metering,
energy management and performance applications. The heat pump is a single phase two
wire without neutral connection with 208 to 240 Vac. The two conductors have AC
waveforms 120Β° or 180Β° out of phase. For this configuration, the meter is powered from
the ΓA and ΓB (phase A and phase B) terminals as shown in Figure 3.9.
Figure 3.8: Fluxis F601 flow meter installation
Transducers
Flow meter
28
Figure 3.9: WATTNODE PULSE energy meter installation
3.5 Experiments
The experiments were conducted to collect the necessary data for generating the
model. Two experiments were conducted to generate the cooling model: the first one at
the beginning of the cooling season to provide the performance of the heat pump under
mild conditions. The experiment continued for ten days. The set point for the space was
72 F while the water-to-air heat pump was off during the experiment time so it had no
effect on the experiment. The radiant floor loop had a mixing valve to control the slab
temperature. Since the slab temperature was not the subject of the experiment, the valve
was bypassed to eliminate water temperature changing by mixing. The second
experiment was conducted on the peak of the season to collect data when the heat pump
29
worked in its highest performance. The experiment continued for 30 days to collect more
data. The set point was 66 F for the Zero Energy Building to ensure that the heat pump
would work for longer hours to collect more data. The temperature sensors were installed
on the outlet and inlet of the heat pumpβs load side and source side. The power meter
was connected to the unit electric input. Since the heat pump had no fan, the recorded
values represent the compressor power only. Temperatures and power values were
stored and recorded every 15 minutes in TAC Vista. The same methodology was followed
for conducting the heating mode experiment but with set points that were more compatible
with the heating mode. The set point for the first experiment was 74 F and for the second
one was 85 F.
3.6 Data Collection
After finishing the experiments the data was collected and divided into groups of
10 hours of continuous operation periods. In order to get accurate data points, two types
of the recorded points were excluded from each group: First the data related to the power
measured at the moment when the heat pump turned off because the value was not
accurate due to current dissipation. The second type of data that was excluded was the
first hour of heat pump operation to eliminate the effect of the loop reaction time. This was
determined by calculating the mass of the water in the radiant floor loop based on the
length and cross section of the embedded pipe and water density. The total mass of the
water was 72 kg. According to Lash (1990) the loop reaction time for this loop should
have been 25 minutes as its total mass was 72 and it had a 3 GPM/Ton flow rate as
shown in Figures 3.10 and 3.11. For more conservative approach 60 minutes were
selected to be the loop reaction time.
30
Figure 3.10: Loop reaction time for cooling mode
Figure 3.11: Loop reaction time for heating mode
The measured heat transfer for the load side was calculated based on heat transfer law
by using the interning and leaving water temperatures for the coil and the mass flow rate
as follows:
ππΏ = οΏ½οΏ½ β ππ β (ππΏππ β ππΏππ’π‘) (3.13)
0
5
10
15
20
25
30
-10 10 30 50 70 90
Loo
p c
oo
ld d
ow
n t
ime
(min
)
Loop mass per ton installed capacity (kg)
Coolign mode
2 GPM/ Ton
3 GPM/Ton
5 GPM/Ton
0
10
20
30
40
50
60
0 20 40 60 80 100 120 140
Loo
p H
eat
up
tim
e (m
in)
Loop mass per ton installed capacity (kg)
Heating mode
2 GPM/ Ton
3 GPM/Ton
5 GPM/Ton
31
3.7 Linear Regression Analysis
Several methods were used to model and investigate the relationship between
variables. Regression analysis is one of the best statistical techniques for representing
these relations. In this paper the linear regression method will be used to develop the
heat pump model. This method assumes a linear relationship to predict the response from
regressors or predictor variables as shown in Equation 3.14. Nonlinear regression models
are also available, but they were not selected to keep the model simple as much as
possible, so generating the model and applying it in the simulation software will not
consume a lot of time.
π¦ = π½0 + π½1π₯1 + π½2π₯2 + π (3.14)
The number of predictor variables investigated in this research is two, so the model would
be a multiple linear regression and described as a plain in the three-dimensional space
as shown in Figure 3.12.
Figure 3.12: Multiple linear regression.
32
The regression coefficients Ξ²0, Ξ²1, and Ξ²2 were estimated by the method of least squares
so that the sum of the squares of the differences between the observations and the plain
is a minimum.
3.8 Coefficients Generation
One of the goals of this study is to find the minimum hours and days for generating
the heat pump performance model. Several attempts were made on the data to find the
minimum days and hours required to generate the model without losing the accuracy of
predicting the values. The first model started with ten days (5 days from each experiment)
and ten hours for each day and taking the average of the hours of each day, then applying
the linear regression to generate the model coefficients for the heat pump capacity and
power consumption. The model results were compared to the measured data of the unit
cooling capacity and power consumption. The same procedure was followed to generate
the coefficients when reducing the number of days and hours to reach the minimum time
for determining the modelsβ coefficients. Microsoft Office Excel was used to generate the
coefficients for each approach.
3.9 EnergyPlus
The simulation software that is used in this research is EnergyPlus, which is an
energy analysis and thermal load simulation program. The program was developed by
the Department of Energy based on both Building Loads Analysis and System
Thermodynamics (BLAST) and DOE-2 programs in such a way to combine the best
features of the two programs and offer a tool for design engineers or architects to size
appropriate HVAC equipment, optimize energy performance, develop retrofit studies for
life cycling cost analyses, etc. Energyplus was designed to be a part of a group of
33
programs that provide CAD interface to enable the user to draw the building, though it
can run standalone without such programs. After providing the necessary inputs, such as
HVAC systems, building dimensions, etc, the program will determine the heating and
cooling loads for maintaining the set points in addition to other simulations to ensure that
the building is running as the actual conditions. To increase the number of developers
with less investment resources, EnergyPlus was designed to easily connect to other
programs. Figure 3.13 shows how other programs have already linked to the program
and how the simulation flows.
Figure 3.13: EnergyPlus Structure
34
3.10 Water-to-Water Curve Fit Model Simulation in EnergyPlus
Simulating an HVAC system in EnergyPlus is done systematically by dividing the
system hierarchically to a set of objects starting with loops (air loop, condenser loop, etc.),
supply and demand sides, branches, components (water-to-water heat pump) and finally
nodes that store components inlet and outlet conditions. Figure 3.14 shows the water-to-
water heat pump lay out in EnergyPlus and its connection to the ground heat exchanger
and the radiant floor.
Figure 3.14: Water-to water heat pumps simulation lay out
In order to meet the demand and temperatures on each loop, a successive substitution
solver was used for the simulation. The condenser loop is divided into two parts: the supply
side solves for the ground heat exchanger, while the demand side models the energy
35
conversion from the water-to-water heat pump. The same procedure is followed for the
plant loop: the demand side simulates the radiant floor; however, the heat transfer from
the equipment is solved on the supply side.
36
CHAPTER 4
MODEL VERIFICATION
The equation-fit model for the water-to-water heat pump is verified by using actual
measured data from the experiment. The model for unit capacity and power were verified
for both heating and cooling modes. The EnergyPlus simulation results of the field data
model and catalog data model were also verified.
4.1 Cooling Mode Verification
Twenty-four models were generated based on both experiments by using different
number of days and hours to determine the minimum number of days and hours, and
each one of the models were verified against the measured data. First model was based
on 10 days and 10 hours. Figure 4.1 shows the cooling capacity model. The generated
model was applied to the 1225 data points of the two experiments.
Figure 4.1: Validating 10 days 10 hours cooling capacity model
The error percentage of the model was within Β±10%, and the maximum error was 5.12
%, while the minimum error was -4.54%. The root mean square error percentage (RMS)
4.39 4.89 5.39 5.89 6.39 6.89
4.39
4.89
5.39
5.89
6.39
6.89
15000
16000
17000
18000
19000
20000
21000
22000
23000
24000
25000
15000 17000 19000 21000 23000 25000
Cooling capacity measured (kW)
Co
olin
g ca
pac
ity
mo
del
(kW
)
Co
olin
g ca
pac
ity
mo
del
(Btu
/h)
Cooling capacity measured(Btu/h)
+10%
-10%
37
for the model was 1.56 %. Figure 4.2 shows the power model for cooling mode that was
generated based on 10 days and 10 hours. The model predicted the power consumption
of the heat pump within Β±10 % error. The points seem to be grouped and clustered at
different distances. That is because the heat pump has a constant power for a range of
inlet load and source temperatures. The minimum days and hours to generate the cooling
performance model were 4 days 2 hours. Figures 5.3 and 5.4 show the validation of the
water-to-water heat pump capacity and power consumption. The error is still within the
range of Β±10 %. Tables 4.1 and 4.2 list the error percentage of each model. The error
increment of the 4 days 2 hours model compared to the 10 days 10 hours model in the
cooling capacity is negligible, also the same observation can be noticed for the power
model.
.
Figure 4.2: Validating 10 days 10 hours power model
1
1.1
1.2
1.3
1.4
1.5
1.6
1 1.1 1.2 1.3 1.4 1.5 1.6
Po
wer
mo
del
(kW
)
Power measured (kW)
+10 %
-10 %
38
Figure 4.3: Validating 4 days 2 hours cooling capacity model
Figure 4.4: Validating 4 days 2 hours cooling power model
4.98 5.18 5.38 5.58 5.78 5.98 6.18 6.38 6.58
2.928
3.928
4.928
5.928
6.928
7.928
8.928
9.928
17000
18000
19000
20000
21000
22000
23000
17000 18000 19000 20000 21000 22000 23000
Cooling capacity measured (kW)
Co
olin
g ca
pac
ity
mo
del
(kW
)
Co
olin
g ca
pac
ity
mo
del
(BTU
H)
Cooling capacity measured(BTUH)
+10%
-10%
1.1
1.15
1.2
1.25
1.3
1.35
1.4
1.45
1.5
1.55
1.6
1.1 1.2 1.3 1.4 1.5 1.6
Po
wer
mo
del
(kW
)
Power measured (kW)
39
No. Model Max Capacity Error Min Capacity Error RMS error Capacity
1 10 days 10 hours 5.12 % - 4.5 % 1.56 %
2 4 days 2 hours 5.38 % - 4.48 1.63 %
Table 4.1: Cooling capacity Error for different models
No. Model Max Capacity Error Min Capacity Error RMS error Capacity
1 10 days 10 hours 6.85 % - 4.09 % 1.95 %
2 4 days 2 hours 7.39 % - 3.45 % 2.08 %
Table 4.2: Cooling power Error for different models
4.2 Heating Mode Verification
The same procedure was followed to generate the models for the heating mode,
starting from 10 days 10 hours model reaching to 4 days 2 hours, keeping in mind the
first hour is neglected. Figure 4.5 shows the 10 days 10 hours heating capacity model.
The prediction of the model showed a good agreement with the measured data, and the
error was within Β± 10 %. The power model for the same number of days and hours is
shown in Figure 4.6, just like in the cooling mode points clustering noticed for the power
model. The minimum period to generate the model with accurate results was 8 days and
2 hours. Using less data caused scattering for the model results. Figures 4.7 and 4.8
show the verification of the heating capacity and power models for the minimum period
of time. Tables 4.3 and 4.4 summarize the error for both models
40
Figure 4.5: Validating 10 days 10 hours heating capacity model
Figure 4.6: Validating 10 days 10 hours heating power model
2.928 3.928 4.928 5.928 6.928 7.928 8.928 9.928
2.928
3.928
4.928
5.928
6.928
7.928
8.928
9.928
20000
21000
22000
23000
24000
25000
26000
27000
28000
29000
30000
24000 25000 26000 27000 28000 29000 30000
Cooling capacity measured (Kw)
Co
olin
g ca
pac
ity
mo
del
(Kw
)
Co
olin
g ca
pac
ity
mo
del
(BTU
H)
Cooling capacity measured(BTUH)
+10%
-10%
1.3
1.4
1.5
1.6
1.7
1.8
1.9
1.3 1.4 1.5 1.6 1.7 1.8 1.9
Po
wer
mo
del
(kW
)
Power measured (kW)
41
Figure 4.7: Validating 8 days 2 hours heating capacity model
Figure 4.8: Validating 8 days 2 hours heating power model
2.928 3.928 4.928 5.928 6.928 7.928 8.928 9.928
2.928
3.928
4.928
5.928
6.928
7.928
8.928
9.928
20000
21000
22000
23000
24000
25000
26000
27000
28000
29000
30000
24000 25000 26000 27000 28000 29000 30000
Cooling capacity measured (kW)
Co
olin
g ca
pac
ity
mo
del
(kW
)
Co
olin
g ca
pac
ity
mo
del
(BTU
H)
Cooling capacity measured(BTUH)
+10%
-10%
1.4
1.45
1.5
1.55
1.6
1.65
1.7
1.75
1.8
1.85
1.9
1.4 1.5 1.6 1.7 1.8 1.9
Po
wer
mo
del
(kW
)
Power measured (kW)
42
No. Model Max Capacity Error Min Capacity Error RMS error Capacity
1 10 days 10 hours 4.1 % - 2.86 % 1.42 %
2 8 days 2 hours 3.63 % - 3.1 1.27 %
Table 4.3: Heating capacity Error for different models
No. Model Max Capacity Error Min Capacity Error RMS error Capacity
1 10 days 10 hours 4.6 % - 4.76 % 1.27 %
2 8 days 2 hours 4.53 % - 5.49 1.79 %
Table 4.4: Heating power Error for different models
4.3 EnergyPlus Simulation
The coefficients generated based on the data collected from the field were
implemented in EnergyPlus to perform the simulation for Zero Energy Lab. Table 4.5 lists
the cooling and heating mode coefficient, which were generated based on the measured
data. The simulation was also performed for the building using the coefficients generated
from the catalog data using the existing method in EnergyPlus. Since the heat pump
manufacturer catalog provides operating conditions based on constant flow rate, another
manufacturer catalog with similar capacity was used to generate the model. The
coefficients are listed in Table 4.6
Mode Model Coefficient
1
Coefficient
2
Coefficient
3
Cooling Capacity -1.73911 5.721991 -2.94572
Power -5.73419 -1.09828 7.427062
Heating
Capacity -2.91077 -1.74665 5.534539
Power -3.87588 4.944718 -0.49151
Table 4.5: EnergyPlus field coefficients
43
Table 4.6: EnergyPlus Catalog coefficients
4.3.1 EnergyPlus Simulation Using Field Data Model
To cover the two experiment periods of the cooling mode, two simulation runs were
performed. The results then were validated against the measured data. Figure 4.9 shows
the cooling capacity validation of simulation results. The results show the error still within
Β±10%, but the scattering increased. That is because each model of the simulation
components has an error percentage that may affect the water-to-water heat pump
model. The validation of the simulation results for the cooling power model is shown in
Figure 4.10. The results show the power of the unit was within Β± 10 % of the measured
data. The simulation was also performed for the heating mode. Two runs were executed
to include both experiments. The resultsβ validation for both heating capacity and power
are shown in Figures 4.11 and 4.12, and the results show good agreement between the
measured data and simulation results the error was within Β±10 %.
Mode Model Coefficient
1
Coefficient
2
Coefficient
3
Coefficient
4
Coefficient
5
Cooling
Capacity -1.34975 2.20872 -0.28712 0.07445 -0.03697
Power -5.02849 -0.01616 6.04059 0.00250 -0.17484
Heating
Capacity -2.4666 -0.7030 3.7706 -0.000863 0.077808
Power -7.21472 7.71538 0.38778 -0.09405 0.00683
44
Figure 4.9: Field data model validation for cooling capacity simulation
Figure 4.10: Field data model validation for cooling power simulation
4.98 5.18 5.38 5.58 5.78 5.98 6.18 6.38 6.58
4.98
5.18
5.38
5.58
5.78
5.98
6.18
6.38
6.58
5000
5200
5400
5600
5800
6000
6200
6400
6600
6800
7000
5000 5500 6000 6500 7000
Cooling capacity measured (kW)
Co
olin
g ca
pac
ity
mo
del
(kW
)
Co
olin
g ca
pac
ity
mo
del
(BTU
H)
Cooling capacity measured(BTUH)
+10%
-10%
1100
1150
1200
1250
1300
1350
1400
1450
1500
1550
1600
1100 1200 1300 1400 1500 1600
Po
wer
mo
del
(W)
Power measured (W)
-10%
+10%
45
Figure 4.11: Field data model validation for heating capacity simulation
Figure 4.12: Field data model validation for heating power simulation
4.98 5.18 5.38 5.58 5.78 5.98 6.18 6.38 6.58
4.98
5.18
5.38
5.58
5.78
5.98
6.18
6.38
6.58
23000
24000
25000
26000
27000
28000
29000
30000
23000 24000 25000 26000 27000 28000 29000 30000
Cooling capacity measured (kW)
Co
olin
g ca
pac
ity
mo
del
(kW
)
Co
olin
g ca
pac
ity
mo
del
(BTU
H)
Cooling capacity measured(BTUH)
+10%
-10%
1400
1450
1500
1550
1600
1650
1700
1750
1800
1400 1450 1500 1550 1600 1650 1700 1750 1800
Po
wer
mo
del
(W)
Power measured (W)
-10%
+10%
46
4.3.2 EnergyPlus Simulation Using Catalog Data Model
The validation of the cooling capacity of the water-to-water heat pump is shown in
Figure 4.13. The simulation included two runs to cover the experiment on the beginning
of the cooling season and the experiment at the peak of the season. The error of the
model reached 30 % of the actual value. The simulation results of the two runs of cooling
power model are shown in Figure 4.14. The error of the model exceeded 10% and
reached 40%. The overprediction of the cooling capacity and cooling power resulting from
using a different manufacturer catalog, and the actual flow rate is not available in the
catalog. The validation of the heating catalog models also showed overprediction for both
the heating capacity and the power for heating as shown in Figures 4.15 and 4.16. The
error of the heating capacity reached 38%, while the compressor power reached 52%.
Figure 4.13: Catalog data model validation for cooling capacity simulation
4.98 5.18 5.38 5.58 5.78 5.98 6.18 6.38 6.58
4.98
5.18
5.38
5.58
5.78
5.98
6.18
6.38
6.58
17000
18000
19000
20000
21000
22000
23000
24000
25000
17000 18000 19000 20000 21000 22000 23000 24000 25000
Cooling capacity measured (kW)
Co
olin
g ca
pac
ity
mo
del
(kW
)
Co
olin
g ca
pac
ity
mo
del
(BTU
H)
Cooling capacity measured(BTUH)
-10%
+30%
47
Figure 4.14: Catalog data model validation for cooling power simulation
Figure 4.15: Catalog data model validation for heating capacity simulation
900
1100
1300
1500
1700
1900
900 1100 1300 1500 1700 1900
Po
wer
mo
del
(W)
Power measured (W)
Cooling power
-10%
+40%
4.98 5.18 5.38 5.58 5.78 5.98 6.18 6.38 6.58
4.98
5.18
5.38
5.58
5.78
5.98
6.18
6.38
6.58
24000
26000
28000
30000
32000
34000
36000
38000
24000 26000 28000 30000 32000 34000 36000 38000
Cooling capacity measured (Kw)
Co
olin
g ca
pac
ity
mo
del
(Kw
)
Co
olin
g ca
pac
ity
mo
del
(BTU
H)
Cooling capacity measured(BTUH)
+38%
-10%
48
Figure 4.16: Catalog data model validation for heating power simulation
4.5 Results
The field data model assumed a linear relationship between the heat pump
capacity and the temperatures of the load and source sides. The validation of the direct
results of the model and the simulation results in EnergyPlus showed an error within Β±
10%. The cooling mode model was generated based on two days from each experiment
and two continues operation hours. The data was recorded every fifteen minute. The
generalized square method was used to generate the coefficients from the collected data
(Excel or Matlab could be used). The following equations were applied in EnergyPlus to
find the performance of the heat pump.
ππ
ππ,πππ= β1.73911 + 5.721991 [
ππΏ,ππ
ππππ] β 2.94572 [
ππ,ππ
ππππ] (4.1)
πππ€πππ
πππ€πππ,πππ= β5.73419 β 1.09828 [
ππΏ,ππ
ππππ] + 7.427062 [
ππ,ππ
ππππ] (4.2)
900
1100
1300
1500
1700
1900
2100
2300
2500
900 1100 1300 1500 1700 1900 2100 2300
Po
wer
mo
del
(W)
Power measured (W)
-10%
+52%
49
The same procedure was followed to predict the performance of the heat pump in heating
mode except the period of time was four days of each experiment and two hours of
continues operation. The EnergyPlus equations are:
πβ
πβ,πππ= β2.91077 β 1.74665 [
ππΏ,ππ
ππππ] + 5.534539 [
ππ,ππ
ππππ] (4.3)
πππ€ππβ
πππ€ππβ,πππ= β3.87588 + 4.944718 [
ππΏ,ππ
ππππ] β 0.49151 [
ππ,ππ
ππππ] (4.4)
4.6 Error Analysis
It is important to determine the quality of the measurement of physical quantity
because it would enable those who use it to assess its reliability, and giving a quantitative
value of the quality of the result would allow comparing the measurements among
themselves and with reference values. The transform of error equation 4.1 was used to
determine the uncertainty of the measured quantities and the results of the models.
βπ¦ = |ππ
ππ₯1βπ₯1| + |
ππ
ππ₯2βπ₯2| β¦ β¦ β¦ . + |
ππ
ππ₯π βπ₯π| (4.5)
The error analysis of measured cooling capacity was investigated. The results of
single day is shown in Figure 4.17. The maximum error of the measurements is 0.86 %.
The same procedure was followed for the heating capacity measurement. The results of
single day is shown in Figure 4.18. The maximum error of the measurements is 0.85 %.
The error of the models was also investigated, and the results are listed in Table 4.7
50
Figure 4.17: Single day cooling capacity error analysis
Figure 4.18: Single day heating capacity error analysis
17000
17500
18000
18500
19000
19500
20000
20500
4/25 4/25 4/25 4/25 4/25 4/25 4/25 4/25
Cap
acit
y (B
tu/h
)
Date
25000
25500
26000
26500
27000
27500
28000
28500
12/5 12/5 12/5 12/5 12/5 12/5 12/5 12/5 12/5 12/5
Cap
acit
y (B
tu/h
)
Date
51
Mode Model Max error %
Cooling
Capacity .0451
Power .6590
Heating
Capacity .02892
Power .3618
Table 4.7: Model error analysis
52
CHAPTER 5
CONCLUSION AND RECOMMENDATION
5.1 Conclusions
This study included the development and modeling of a water-to-water ground-
source heat pump based on data collected from the field. The model was developed
based on the existing model in EnergyPlus, which was implemented by Tang (2005)
based on catalog data. The original model is not able to generate coefficients based on
constant flow rate. The coefficients dependent on flow rate were removed since the heat
pump in the Zero Energy Lab operates on fixed flow.
The cooling model was generated from data collected from two experiments: the
first one conducted at the beginning of the season and the second one conducted at the
peak of the season because one experiment was not sufficient to cover the heat pump
range of operation, which results in error in determining the performance of the heat pump
capacity and power consumption. The minimum period to generate the model was two
days and two hours from each experiment, the prediction of the heat pump cooling
capacity and power was validated against the measured data, and the error of the models
was within Β± 10% of the measured data. The same procedure was followed to generate
the heating mode capacity and power. The error also was within Β± 10%, but the required
time to generate the model was four days and two hours of each experiment. The reason
for that was the load of the heat pump is radiant floor and because of the bouncy effect
more time was required to cover the operation range of the heat pump.
The coefficients were applied in EnergyPlus to perform the simulation for Zero Energy
Lab in both modes, the heating and cooling. The results were more scattered when
53
compared to the measured data but still within Β± 10%. The increase in error is related to
the inaccuracy of each model in the simulation process. When using the coefficients
generated based on the catalog data to perform the simulation, the error of cooling
capacity reached +30% and +40 % for the cooling power, while the simulation error for
the heating mode capacity is +38% and +52 % for the heating power. This high
percentage of error was because the operation conditions were not available in the
catalog and the manufacturer performance data based on constant conditions so another
catalog data with the same capacity were used to generate the coefficients.
It can be concluded that using the actual data from the field to generate the model
would reduce the simulation error reduced by 20% to 42%, which would increase the
confidence in the simulation results.
5.2 Future Work
This model was generated from experiments on Florida heat pump. Other brands
with the same and different capacities could also be used to generate the model to
generalize its application. The data was collected in laboratory environment to further
validate the model. The data could be collected from residential and commercial buildings
that have old or new installed heat pumps.
The load side of the heat pump was a radiant floor that was controlled by a three-
way valve. In this study the valve control effects on the heat pump performance have not
been investigated by keeping the valve open for all experiment periods. Further study
could include different valve positon and its effects on the model. Furthermore, additional
experiments could be conducted for a fan coil as a load instead of the radiant floor.
54
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