Field Performance Measurements of VRF System - Purdue e-Pubs

Field Performance Measurements of VRF System - Purdue e-Pubs
Purdue University
Purdue e-Pubs
International Refrigeration and Air Conditioning
Conference
School of Mechanical Engineering
2012
Field Performance Measurements of VRF System
with Subcooling Heat Exchanger
Yunho Hwang
[email protected]
Laeun Kwon
Reinhard Radermacher
Byungsoon Kim
Follow this and additional works at: http://docs.lib.purdue.edu/iracc
Hwang, Yunho; Kwon, Laeun; Radermacher, Reinhard; and Kim, Byungsoon, "Field Performance Measurements of VRF System with
Subcooling Heat Exchanger" (2012). International Refrigeration and Air Conditioning Conference. Paper 1263.
http://docs.lib.purdue.edu/iracc/1263
This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact [email protected] for
additional information.
Complete proceedings may be acquired in print and on CD-ROM directly from the Ray W. Herrick Laboratories at https://engineering.purdue.edu/
Herrick/Events/orderlit.html
2330, Page 1
Modeling of Variable Refrigerant Flow System for the Cooling Season
Laeun KWON1, Yunho HWANG1*, Reinhard RADERMACHER1, Byungsoon KIM2
1
Center for Environmental Energy Engineering
Department of Mechanical Engineering, University of Maryland
4164 Glenn Martin Hall Bldg., College Park, MD, 20742, USA
2
SAC Research Lab., SAC Business Unit, AE Company, LG Electronics Inc.
Seongsan-dong 76, Changwon, Gyeongnam, 641-713, Republic of Korea
* Corresponding Author, Tel: (301) 405-5247, E-mail: [email protected]
ABSTRACT
In this study, the cooling performance of the multi-split variable refrigerant flow (VRF) air conditioning system
operated in the academic building environment was simulated with EnergyPlus software, which has a new module
for VRF heat pump systems. Simulation results were validated with the field test results during the cooling season.
The comparison result shows that 87.5% of all simulated daily power consumption data agree with the experimental
data within ±15% deviation. The root-mean-square deviations of daily, weekly and monthly electricity power
consumptions for the total simulation period between the simulated and measured values are 5.63 kWh, 11.12 kWh
and 37.58 kWh, respectively. The averages of the absolute values of the daily, weekly and monthly relative error for
the total simulation period are 7.97%, 2.40% and 2.22%, respectively.
1. INTRODUCTION
Variable refrigerant flow (VRF) air conditioning systems have been widely used for residential and commercial
buildings. VRF systems consist of an outdoor condensing unit and a network of indoor units connected through
refrigerant piping within the conditioned space. Cooling loads and system cooling capacity vary with many
parameters such as environmental conditions, the number of indoor units operating in cooling or heating mode,
airflow rates, internal loads in each zone, and indoor conditions. A VRF heat pump system’s indoor units control the
capacity by using an electronic expansion valve. At the system level, the outdoor unit conducts load management
through the inverter-driven variable-speed compressor, or alternative combinations of outdoor unit fan motors. The
target evaporating temperature and degree of superheating are monitored and used for controlling the compressor
speed, the fan motor speed, and the opening of the EEV to ensure a stable target temperature control, regardless of
varying loads and environmental conditions.
Since the VRF systems, which were introduced about 20 years ago in Asia, have become popular in many countries,
this technology has been widely studied, both experimentally and numerically. Zhou et al. (2007) compared the
energy consumption of the VRF system with variable air volume (VAV) system and fan-coil plus fresh air (FPFA)
system numerically. Their simulation results showed that the energy-saving potentials of the VRF system were
expected to be 22.2% and 11.7% as compared with the VAV system and the FPFA system, respectively. Zhou et al.
(2008) tested the performance of VRF system and used their data for validating their customized VRF simulation
module. The mean relative error of VRF system’s daily power consumption was 28.31%. Kang et al. (2009)
experimentally investigated a heat recovery type VRF system, which can provide heating and cooling
simultaneously to different zones of a building. The optimized system COP was 146.5% and 133.7% higher
compared to the cooling-only and heating-only mode of the non-heat recovery type VRF system, respectively. Li et
al. (2009) developed a module for a water-cooled VRF system in the software of EnergyPlus. They numerically
International Refrigeration and Air Conditioning Conference at Purdue, July 16-19, 2012
2330, Page 2
compared the power consumption of water-cooled VRF system with those of the air-cooled VRF system and the
FPFA system. Although the monthly power consumption of FPFA system was higher than the two systems, the
difference in power consumption between the water-cooled VRF and the air-cooled one was small. Liu and Hong
(2010) investigated the energy efficiency of the heat recovery type VRF system and the ground source heat pump
(GSHP) system using EnergyPro and eQUEST, respectively. For the locations representing hot and cold climates,
the GSHP system saves up to 24.1% of cooling and heating energy consumption as compared to the heat recovery
type VRF system. Aynur et al. (2006) experimentally investigated the performance of a VRF air conditioning
system in a field test during the cooling season with different control modes, master control and individual control.
They found that the performance of VRF system in the individual control mode was 8.6% higher than that of the
VRF master control mode. The main disadvantage of VRF system is that it cannot provide any ventilation by itself.
The effect of the ventilation on the VRF system’s performance including the indoor thermal comfort, energy
consumption and system efficiency was investigated experimentally and numerically by Aynur et al.(2008a and
2008b).
In this study, the cooling performance of the multi-split VRF air conditioning system operated in the academic
building environment was simulated with the VRF module developed for EnergyPlus version 7.0 software (DOE,
2011), which is verified and adopted by the EnergyPlus. Then the simulated performance of a multi-split VRF air
conditioning system was compared with test results for validation.
2. MODEL DEVELOPMENT
2.1 Building Description
Figure 1 illustrates the target building and its zones for modeling, located in College Park, Maryland. The Legacy
OpenStudio Plug-in for Google SketchUp (DOE, 2011) was used to create the building geometry in EnergyPlus
input file. Table 1 shows the characteristics of the internal loads in each thermal zone. A ceiling height is 2.48 m.
The hallway and three unconditioned zones were included for the simulation. Since there were two indoor units in
Room B and Room C, each room was sub-divided into two thermal zones with imaginary air wall, which had a very
low thermal conductivity, as shown in Figure 1. Construction material properties are listed in Table 2. Interior wall 2
was used between each room and hallway.
2.2 VRF System Description
Newly developed EnergyPlus Objects (DOE, 2011) (ZoneHVAC: Terminal: VariableRefrigerantFlow and
AirConditioner: VariableRefrigerantFlow) were used to model the multi-split VRF system. Figure 2 shows the node
connections of VRF terminal units with draw-through fan placement. EnergyPlus Object (DOE, 2011)
(Fan:ConstantVolume) was used to model the indoor fans since they were operated continuously with constant air
volume flow rate during the cooling operation. An operating mode, either cooling or heating, was decided from the
following options: LoadPriority, ZonePriority, ThermostatOffsetPriority and MasterThermostatPriority.
Figure 1: Illustration of 3D image of the target office suite
International Refrigeration and Air Conditioning Conference at Purdue, July 16-19, 2012
2330, Page 3
Table 1: Number of internal load sources
Room
Space Area (m2)
Number of Occupants
Office Equipment (W/m2)
Lighting (W/m2)
Room A
11.36
1
73.50
13.20
Room B1
11.07
3
56.46
13.55
Room B2
11.07
4
54.20
13.55
Room C1
11.19
3
55.85
13.40
Room C2
11.19
4
53.62
13.40
Room D
9.34
1
89.94
16.06
Table 2: Building material characteristics
Section
Constructions
Total Thickness (m)
Exterior
Wall
Bricks,
Air and
Plaster
0.177
Interior
Wall 1
Gymsum
Plaster and
Air Space
0.124
Interior
Wall 2
Ceiling
Bricks
Acoustic
Tile
0.152
0.025
Windows
Roof
Two SinglePane Glazing
with Air Gap
0.014
Insulation,
Brick and
Polyurethane
0.184
Figure 2: Nodes connection of VRF terminal unit and location of thermostats
The default option, MasterThermostatPriority, is the control based on the master thermostat by which all terminal
units are operated. Since the multi-split VRF system could not provide any ventilation, outdoor air flow rate to the
zone terminal unit during cooling operation was set to be zero in simulation. The model uses performance
information at reference conditions along with the correlations for the cooling capacity and power consumption. The
VRF system performance correlations were generated by curve fitting the manufacturer’s performance data.
The operating capacity of the heat pump ( CAPFTOU ,cooling ) is calculated using the Equation (1), which is a
function of a load-weighted average indoor web-bulb temperature ( Twb,avg ) and outdoor dry-bulb temperature ( Tc ).
CAPFTOU , cooling  a  b(Twb, avg )  c(Twb, avg ) 2  d (Tc )  e(Tc ) 2  f (Twb, avg )(Tc )
(1)
In order to correct the performance for off-design cases, the cooling combination ratio correction factor
( CRcooling ,correction ) is calculated as a function of the rated cooling combination ratio (CRcooling,rated), which is defined
International Refrigeration and Air Conditioning Conference at Purdue, July 16-19, 2012
2330, Page 4
as the total terminal unit’s rated cooling capacity divided by the heat pump system’s rated cooling capacity as in
Equation (2).
CRcooling , correction  a  b(CRcooling , rated )  c(CRcooling , rated ) 2  d (CRcooling , rated )3
6
where CRcooling ,rated 

Q
1
(2)
coil (i ),cooling ,rated

Q coil ,cooling ,rated

The heat pump’s total cooling capacity ( Q OU ,cooling ,total ) is then calculated from the rated cooling capacity

( Q OU ,cooling ,rated ) using Equations (1) and (2) as shown in Equation (3).
Q OU ,cooling ,total  Q OU , cooling , rated CAPFTOU , cooling CRcooling , correction 


(3)
The cooling energy input ratio modifier (EIRFT) is a function of temperature as shown in Equation (4). The cooling
energy input ratio modifier (EIRFPLR) is a function of Part Load Ratio (PLR) as shown in Equation (5).
EIRFTOU ,cooling  a  b(Twb ,avg )  c(Twb ,avg ) 2  d (Tc )  e(Tc ) 2  f (Twb ,avg )(Tc )
(4)
EIRFPLRcooling  a  b( PLR)  c( PLR ) 2  d ( PLR ) 3
(5)
The energy efficiency of the VRF system is highly affected by the capability of adjusting heating and cooling
outputs to meet the dynamic building heating and cooling loads. The part load ratio (PLR) is defined as the ratio of
total heat pump condenser cooling load and heat pump total available cooling capacity. The cycling ratio
(CyclingRatio) is defined as the ratio of the PLR and the minimum PLR obtained from the experiment as shown in
Equation (6).
CyclingRatio  PLR PLRmin
(6)
Equation (7) shows the Cycling Ratio Fraction correlation (CyclingRatioFrac) as a function of cycling ratio. The
cycling ratio was determined by the on time ratio of the compressor during the field test.
CyclingRatioFrac  a  b(CyclingRatio)  c (CyclingRatio) 2  d (CyclingRatio) 3
Equations (6) and (7) are used to obtain the heat pump runtime fraction (HPRTF) used in Equation (8).
CyclingRatio
HPRTF 
CyclingRatioFrac
(7)
(8)
Finally, the electric power consumption of VRF system is calculated using Equation (9).
 
 Q OU ,cooling ,rated CAPFTOU ,cooling
PowerConsumption  
COPcooling ,reference




 EIRFTOU ,cooling EIRFPLRcooling HPRTF 





(9)
Detailed information on calculating the total cooling capacity of the indoor units can be found in DOE’s EnergyPlus
documentation (2011). The rated cooling capacities of indoor units and outdoor unit are summarized in Table 3.
International Refrigeration and Air Conditioning Conference at Purdue, July 16-19, 2012
2330, Page 5
Table 3: Rated performance of VRF heat pump system
Unit
Outdoor Unit
Rated cooling capacity (kW)
Rated high air flow rate (m3/min)
22.4
180
IU 1 and IU 6
2.2
5.6
Indoor Units
IU 2 and IU 3
3.6
7.0
IU 4 and IU 5
5.6
12.0
2.3 Weather Data and Schedules
Papa et al. (2007) concluded that outdoor temperature is the most important factor of air conditioning energy
consumption. The changes of outside temperature account for 60 ~ 70% of building energy consumption (Yezioro et
al., 2008). The actual outdoor dry-bulb temperature and relative humidity were measured on site and used as the
weather data. Other actual 2011 weather conditions including solar radiation, solar illuminances, wind speed, wind
direction, and atmospheric pressure, was obtained from the integration of a newly released NOAA historical data set
for Arlington, Virginia (around 22.5 km southeast of College park, Maryland) (Weather Analytics, 2011).
Information on the operation schedules (occupancy, lighting and equipment) and thermostat set-point were specified
for more accurate simulation results. Although the operation schedules during the test period had a regular pattern,
the thermostat set point temperature was varied occasionally during the experimental period. Since the set point
temperature was recorded, it was reflected in the simulation for the accurate validation.
3. MODEL VALIDATION
VRF heat pump system charged with R410A was installed in educational offices and its field performance was
tested from June to August in 2011. The actual outdoor conditions measured were used in the weather file, and daily,
weekly, and monthly simulation results and experimental data were compared.
3.1 Experimental Set-up
Figure 3 shows the office suite with four rooms which was used for the field performance tests. Based on the
building load estimation, two wall-mounted type indoor units were installed in Room B and Room C. A detailed
schematic drawing of the system, measurement instrument, evaluation methodology and the accuracies of the
sensors can be found in the previous work by Laeun et al. (2012). The error analysis of the experimental results was
performed according to the propagation of the uncertainty (Kline and McClintock, 1953). Maximum uncertainty
values of the cooling capacity of the VRF system, the cooling performance factor, and the power consumption are
±0.15 kW, ±0.76 and ±0.78 kW, respectively.
Figure 3: Layout of the office suite
International Refrigeration and Air Conditioning Conference at Purdue, July 16-19, 2012
2330, Page 6
(a) Daily electricity power consumption
(b) Daily COP
Figure 4: Comparison between simulated results and experiment data
(a) Weekly electricity power consumption
(b) Monthly electricity power consumption
Figure 5: Comparison between simulated results and experimental data
3.3 Weekly and Monthly Electricity Power Consumption
Since the long-term power consumption is more interesting than the short-term power consumption in the early
design stage (Yezioro et al. 2008), the weekly and monthly electricity power consumptions were compared as
shown in Figure 5. Band bar in Figure 5 (a) and (b) represents the temperature range for each week and month.
Maximum errors in weekly and monthly electricity power consumptions are about 7.9% and 4.5%, respectively. The
root-mean-square deviations of weekly and monthly electricity power consumptions for the total simulation period
between the simulated and measured values are 11.12 kWh and 37.58 kWh, respectively. The averages of the
absolute values of the weekly and monthly relative errors for the total simulation period are 2.40% and 2.22%,
respectively. Comparison of the simulated and measured electricity power consumptions in a monthly basis shows
significantly smaller deviations than those in the daily basis in terms of the mean of the absolute values. The reasons
for small differences in monthly result are due to the factor that the underpredictions in some days compensate for
overpredictions in other days resulting in an improved monthly comparison. Because of this reason, computed
average results are generally in better agreement for longer time periods (Li et al., 2010). Therefore, the root-meansquare deviations should be compared in parallel.
International Refrigeration and Air Conditioning Conference at Purdue, July 16-19, 2012
2330, Page 7
4. CONCLUSIONS
In this paper, VRF air-conditioning system in an academic office suite was numerically and experimentally
investigated for the cooling season. The simulation study was performed by the newly developed building energy
simulation program, EnergyPlus. Compared to the result obtained by the customized VRF heat pump simulation
module (Zhou et al., 2008), the result of the current study shows a better agreement. This is because the cooling
combination ratio correction factor and cooling cycling ratio fraction correlation in response to the cycling ratio
were used in the current modeling work in order to correct the performance for off-design cases. The comparison of
the simulated data and measured data was performed at three levels of time scale: daily, weekly and monthly. The
root-mean-square deviation of daily, weekly and monthly electricity power consumptions for the total simulation
period between the simulated and measured values are 5.63 kWh, 11.12 kWh and 37.58 kWh, respectively. The
averages of the absolute values of the daily, weekly and monthly relative errors in electricity power consumption for
the total simulation period are 7.97%, 2.40% and 2.22%, respectively. A better agreement between simulation and
experimental results on a weekly and monthly basis was due to the reduced influences of uncertain factors.
NOMENCLATURE
CAPFT
CR
CyclingRatio
CyclingRatioFrac
EIRFT
EIRFPLR
HPRTF
Subscripts
OU
min
c
wb ,avg
Cooling Capacity Ratio Modifier Function of Temperature
Cooling Combination
Cycling Ratio
Cooling Part-Load Fraction Correlation Function of Cycling Ratio
Energy Input Ratio Modifier Function of Temperature
Energy Input Ration Modifier Function of Part Load Ratio
Heat Pump Run Time Fraction
Outdoor Unit
minimum
outdoor dry-bulb temperature
load-weighted average wet-bulb temperature
REFERENCES
Aynur, T.N., Hwang, Y.H., Radermacher, R., 2006, Field performance measurements of a VRV AC/HP system,
International Refrigeration and Air Conditioning Conference, Purdue, R085: pp. 1-8.
Aynur, T.N., Hwang, Y.H., Radermacher, R., 2008a, Experimental Evaluation of the Ventilation Effect on the
Performance of a VRV System in Cooling Mode-Part I: Experimental Evaluation, HVAC&R RESEARCH, vol.
14, no. 4: pp. 615-630.
Aynur, T.N., Hwang, Y.H., Radermacher, R., 2008b, Simulation Evaluation of the Ventilation Effect on the
Performance of a VRV System in Cooling Mode—Part II, Simulation Evaluation, HVAC&R RESEARCH, vol.
14, no. 4: pp. 783-795.
DOE, 2011, EnergyPlus, Documentation, Version 7.0. U.S. Department of Energy, Washington, DC.
Kang, H., Joo, Y.G., Chung, H.J., Kim, Y.C., Choi, J.M., 2009, Experimental study on the performance of a
simultaneous heating and cooling multi-heat pump with the variation of operation mode, Int. J. Refrig., vol. 32,
no. 6: pp. 1452-1459.
Kline, S.J., McClintock, F.A., 1953, Describing Uncertainties in Single-Sample Experiments, Mechanical
Engineering, vol. 75, no. 1: pp. 3-8.
Laeun, K., Hwang, Y.H., Radermacher, R., Kim, B., 2012, Field performance measurements of a VRF system with
sub-cooler in educational offices for the cooling season, Energy and Buildings, In press,
doi:10.1016/j.enbuild.2012.02.027
Li, Y.M., Wu, J.Y., Shiochi, S.M., 2009, Modeling and energy simulation of the variable refrigerant flow air
conditioning system with water-cooled condenser under cooling conditions, Energy and Buildings, vol. 41,
no.9: pp. 949-957.
Li, Y.M., Wu, J.Y., Shiochi, S., 2010, Experimental validation of the simulation module of the water-cooled
variable refrigerant flow system under cooling operation, Applied Energy, vol. 87, no. 5: pp. 1513-1521.
International Refrigeration and Air Conditioning Conference at Purdue, July 16-19, 2012
2330, Page 8
Liu, X.B., Hong, T.Z., 2010, Comparison of energy efficiency between variable refrigerant flow systems and ground
source heat pump systems, Energy and Buildings, vol. 42, no. 5: pp. 584-589.
Papa, Pietra, R., Jota, Silva, P.R., Sad, E., 2007, Energy Index Evaluation of Buildings in Function of the External
Temperature, Building Simulation: pp. 1890-1894.
Weather Analytics, 2011, www.weatheranalytics.com.
Yezioror, A., Dong, B., Leite, F., 2008, An applied artificial intelligence approach towards assessing building
performance simulation tools, Energy and Buildings, vol. 40, no. 4: pp.612-620.
Zhou, Y.P., Wu, J.Y., Wang, R.Z., Shiochi, S., 2007, Energy simulation in the variable refrigerant flow airconditioning system under cooling conditions, Energy and Buildings, vol. 39, no. 2: pp. 212-220.
Zhou, Y.P., Wu, J.Y., Wang, R.Z., Shiochi, S., Li, Y.M., 2008, Simulation and experimental validation of the
variable-refrigerant-volume (VRV) air-conditioning system in EnergyPlus, Energy and Buildings, vol. 40, no.6
: pp.1041-1047.
ACKNOWLEDGEMENT
We gratefully acknowledge the support of this effort from the sponsors of the Alternative Cooling Technologies and
Applications Consortium, the Center for Environmental Energy Engineering (CEEE) at the University of Maryland,
and LG Electronics Inc.
International Refrigeration and Air Conditioning Conference at Purdue, July 16-19, 2012
Was this manual useful for you? yes no
Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Download PDF

advertisement