Next Article in Journal
Hyperloop Academic Research: A Systematic Review and a Taxonomy of Issues
Next Article in Special Issue
Proposal of a Nature-Inspired Shape for a Vertical Axis Wind Turbine and Comparison of Its Performance with a Semicircular Blade Profile
Previous Article in Journal
Immediate Insulin Treatment Prevents Diabetes-Induced Gut Region-Specific Increase in the Number of Myenteric Serotonergic Neurons
Previous Article in Special Issue
Hydraulic Vibration and Possible Exciting Sources Analysis in a Hydropower System
 
 
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Evaluation of the Thermal Performance and Energy Efficiency of CRAC Equipment through Mathematical Modeling Using a New Index COP WEUED

1
Department of Electromechanical Engineering, University of Beira Interior, 6201-001 Covilhã, Portugal
2
FAPRO—Professional College, Curitiba 80230-040, Brazil
3
C-MAST—Centre for Mechanical and Aerospace Science and Technologies, 6201-001 Covilhã, Portugal
*
Author to whom correspondence should be addressed.
Appl. Sci. 2021, 11(13), 5950; https://doi.org/10.3390/app11135950
Received: 21 May 2021 / Revised: 22 June 2021 / Accepted: 23 June 2021 / Published: 26 June 2021
(This article belongs to the Special Issue Numerical Modeling in Energy and Environment)

Abstract

:
As the world data traffic increasingly grows, the need for computer room air conditioning (CRAC)-type equipment grows proportionally. The air conditioning equipment is responsible for approximately 38% of the energy consumption of data centers. The energy efficiency of these pieces of equipment is compared according to the Energy Standard ASHRAE 90.1-2019, using the index Net Sensible Coefficient Of Performance (NetSCOP). This method benefits fixed-speed compressor equipment with a constant inlet temperature air-cooled condenser (35 °C). A new method, COP WEUED (COP–world energy usage effectiveness design), is proposed based on the IPLV (integrated part load value) methodology. The IPLV is an index focused on partial thermal loads and outdoor temperature data variation for air intake in the condenser. It is based on the average temperatures of the USA’s 29 major cities. The new method is based on the 29 largest cities worldwide and with data-center-specific indoor temperature conditions. For the same inverter compressor, efficiencies of 4.03 and 4.92 kW/kW were obtained, using ASHRAE 90.1-2019 and the proposed method, respectively. This difference of almost 20% between methods is justified because, during less than 5% of the annual hours, the inlet air temperature in the condenser is close to the NetSCOP indication.

1. Introduction

The Cisco Annual Internet Report forecasts the global adoption of the Internet. The proliferation of devices/connections and network performance, by the year 2023, will be [1]:
  • 5.3 billion internet users (66% of the estimated population in 2023).
  • 3.6 global devices and connections per capita.
  • Average global speed of fixed broadband of 110 Mbps.
  • In North America, 92% of the population will use the Internet.
In addition to the increasing number of users, there have been systems improvements, such as lower response time to search information, lower downtime (online for longer, without problems at critical moments), upgrade without interruption (in one click, management of active and unlimited resources), resilience and self-repair (makes the data pulverized automatically, and its update process is much simpler and optimized).
To sustain this growth in the global database and in user demand, the number of data centers and their energy consumption have been increasing. In 2018, it was estimated that data centers consumed 1% of all the global electricity generated. From 2010 to 2018, the number of computers skyrocketed (Figure 1) [2].
Despite this growth, it is estimated that due to the increase in efficiency from 2010 to 2018, the total energy to serve the data centers grew by only 6%, and this is directly explained by the better efficiency of the data center equipment. The energy consumption of a data center is 10 to 100 times greater than that of a standard commercial building of the same dimensions. In Leadership in Energy and Environmental Design (LEED) buildings, the average energy consumption with an electrical load of 68% of the buildings was 10.8 W/m2 [3]. According to the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE), data centers are installations with an enormous demand for energy, highlighting the relevance of the theme. High-density data centers can reach 10,764 W/m2, although on average, their consumption ranges from 430 to 861 W/m2 [4]. Even though specific information technology (IT) equipment has evolved, within the data center, air conditioning systems are one of the main sinks of energy consumption. If energy consumption in data centers is considered of high relevance, the air conditioning topic becomes an indispensable item of discussion. On average, air conditioning systems are responsible for 38% of the energy consumption of data centers [5]. According to Santos et al. [6], the load distribution of a data center is distributed as shown in Figure 2.
Analyzing the current methodologies to measure the energy efficiency of computer room air conditioning (CRAC) equipment in data centers is an important action since it is the largest load apart from the IT equipment itself. Thus, suggesting a new methodology to measure the thermal performance and energy efficiency that considers the characteristics of the data center location is relevant in terms of sustainability.

2. Current Methodology (ASHRAE 90.1-2019 Standard)

A data center is different from ordinary commercial facilities, as it has a high sensible heat rate. Rack coolers are better if designed only for a sensible rate (without wet coils), and the coils can even be above the dew temperature [7].
Based on this high sensible heat rate, wisely, the ASHRAE 90.1-2019 Standard uses the Net Sensible Coefficient Of Performance (NetSCOP) index as a basis for the user to compare the efficiency of the air conditioning machine with a minimum of efficiency specified in the standard. This condition is important because, in some medium data centers, standard equipment (self-contained air conditioning) can be used, and split-type air conditioning system may be used in some smaller data centers [8]. Different from common equipment, the CRAC is designed specifically for data centers. The project type of downflow air supply is used often, and CRAC has the appearance of a closet, designed for a high sensible heat rate, providing more reliability. Figure 3 shows a sectional view of CRAC equipment installed in a data center, and Figure 4 shows a plan view of CRAC installation within a downflow safe room.
Table 1 shows the minimum efficiency requirements and the Net Sensible COP in ASHRAE 90.1-2019 Standard indicated for floor-mounted air conditioners and condensing units serving computer rooms [8] considering the rating conditions for dry-bulb (DBT) and dew-point (DPT) temperatures.
The values for the most used equipment, which is the downflow, are based on a fixed characteristic of return temperature and dew point based on Class 2. According to TC 9.9, classes are divided according to the types of equipment/needs of a data center, as shown in Table 2 [9].
A1—A data center environment with strict control of the psychrometric parameters: dry-bulb temperature (DBT), dew-point temperature (DPT), relative humidity (RH), and in a mission-critical operation. Generally developed for large companies with a large number of racks.
A2—Generally a technological production environment or an office or a laboratory with some control over environmental parameters. They are locations that shelter small racks; they can be personal servers or workstations.
The values of return dry-bulb temperature (DBT) and dew-point temperature (DPT) of 29 and 11 °C, respectively (see Table 3), are recommended for Class 2. These values are not recommended for Class 1, but they could be allowable.
It is also important to note that a return temperature does not mean the intake of air in the rack, which will certainly be at a lower temperature.
The parameters and methodologies to arrive at the values of ASHRAE 90.1-2019 are specified in AHRI 1361-2017. The air supply parameters of Table 4 of the standard of the Air-Conditioning, Heating, and Refrigeration Institute (AHRI) corroborate with ASHRAE 90.1-2019.
In contrast, AHRI 1361-2017 also offers the air intake temperatures in the condenser, as shown in Table 5. It must be noted that:
  • All ratings are at standard atmospheric pressure.
  • For the NetSCOP calculation, add allowance for cooling tower fan(s) and heat rejection loop, water pump power input in kW to the unit total input in kW = 5% of the unit net sensible capacity.
  • For the NetSCOP calculation, add allowance for dry cooler fan(s) and heat rejection loop glycol pump power input in kW to the unit total input in kW = 7.5% of the unit net sensible capacity.
  • For the NetSCOP calculation, add allowance for chilled water pump power input in kW to unit total input in kW (See Equation (1)).
It is appropriate that in a place of thermal load and air supply conditions constant at 8760 h, the priority is the efficiency in Net Sensible COP. However, although there is no variation internally, there is an external factor in the equipment: the temperature of the air inlet in the condenser constantly changes, implying a positive or negative change in the performance of the air conditioning equipment. This temperature variation has a relevant impact on the performance of the inverter or digital compressor equipment type.
The integrated part load value (IPLV) is a performance characteristic developed and used in AHRI’s methodology. This methodology considers variable air intake temperature in the condenser and variable thermal load based on the average temperature of the 29 major cities in the United States of America (U.S.A.).
IPLV is a methodology in which COP is measured in partial loads. IPLV is a parameter to consider even in chillers for data centers. Its parameters are described in AHRI 550/590-2015 [12]. These parameters are in accordance with Equation (1) and described in Table 6.
IPLV   ( or   NPLV ) = 0.01 · A + 0.42 · B + 0.45 · C + 0.12 · D
where,
  • A: COP at 100% capacity, (kW/kW).
  • B: COP at 75% capacity, (kW/kW).
  • C: COP at 50% capacity, (kW/kW).
  • D: COP at 25% capacity, (kW/kW).
From a macro point of view, there is an index that analyzes the conditions of free cooling, evaporative system, and variable COP in data centers: the Energy Usage Effectiveness Design (EUED), with the following characteristics (see Figure 5) [13]:
  • When the outside air temperature is below 20 °C and the enthalpy is below 42.7979 kJ/kg, only free cooling will be used.
  • When the temperature is between 15 and 24 °C and the enthalpy is from 42.7979 to 55.8233 kJ/kg, the evaporative system will be used.
  • When the temperature is above 20 °C and the enthalpy is over 55.8233 kJ/kg, the normal system will be used under the following conditions:
    • Air intake temperature between 24.1 and 27 °C, called COP1.
    • Air intake temperature between 27.1 and 30 °C, called COP2.
    • Air intake temperature between 30.1 and 33 °C, called COP3.
    • Air intake temperature above 33.1 °C in any condition, called COP4.
    • If a geothermal temperature is available, it will be used to determine the COP, with a differential of 4 °C of the geothermal temperature.

3. New Methodology

The new methodology proposed in this paper considers the superposition of the three methodologies: Net Sensible COP, IPLV, and EUED, to create a specific index for CRAC equipment with an air-cooled condenser with downflow air supply named COP WEUED (COP World Energy Usage Effectiveness Design). This index emphasizes the CRAC equipment with the compressor on (i.e., active refrigeration cycle), knowing that indexes such as EUED or statistical analysis for predicting location-specific data center PUE and its improvement potential, already use free cooling and evaporative cooling for their methodologies. The air intake characteristics in the condenser must consider the use of the refrigeration cycle in the data center air conditioning system. Among the characteristics of the new index are [3,13]:
(1)
Fixed air return temperature conditions equivalent to ASHRAE 90.1-2019, that is, 29 °C with a dew point temperature of 11 °C, thus considering a standard evaporation temperature of 12 °C.
(2)
Air intake temperatures in the condenser calculated for four values (levels) shown in Table 7. Each of the values of COP1, COP2, and COP3 are the average values of the EUED methodology, whereas COP4 is the value used by AHRI 1361 for air intake in the condenser.
(3)
Considering the principle in the IPLV that is based on the 29 largest cities in the U.S.A., in this case, the 29 largest cities in the world are used. Table 8 lists the 29 most populous cities according to the 2018 United Nations report [14].
The ASHRAE Weather Data Viewer [15] was used to determine the average temperature condition of each of the 29 largest cities in the world. Table 9 shows how many hours in each of these cities in the world are for COP1, COP2, COP3, and COP4. It is important to remember that the hours of free cooling and evaporative cooling will not be part of the refrigeration equipment index with the compression cycle.
As shown in Table 9, using the weighted average of the 29 largest cities in the world, the use of compression refrigeration in data centers is essential in 3727.2 h per year of the 8760 h available. That is, it is feasible to use free cooling and evaporative cooling for the other 5032.8 h per year, as shown in Figure 6.
Using part of the IPLV formula, EUED, and AHRI 13621 concepts, Equation (2) was determined using percentages of COPs shown in Figure 7 [16].
COP   WEUED = ( ( 0.34 · COP 1 ) + ( 0.34 · COP 2 ) + ( 0.20 · COP 3 ) + ( 0.12 · COP 4 ) ) · SLR
where SLR is the sensible heat rate.

4. Analysis and Discussion

To demonstrate experimentally the difference between a system with both conditions, a calculation using the Bitzer Inverter Scroll compressor software is firstly developed for the AHRI 1361 condition and then with the COP WEUED condition. The considerations exposed in Table 10 were used.
AHRI conditions and thermal load:
  • Sensible cooling capacity = 50 kW;
  • Total cooling capacity = 55 kW;
  • Inlet air condenser temperature = 35 °C (AHRI 1361 conditions) [16];
  • Approach between bubble temperature and condenser air inlet = 10 °C;
  • Condensing temperature = 45 °C;
  • Evaporating temperature = 10 °C;
  • Suction gas superheat = 10 °C (EN 12900-2013 conditions);
  • Liquid subcooling (in condenser) = 0 °C (EN 12900-2013 conditions) [17].
COP Sensible WEUED conditions:
The conditions are the same as the AHRI conditions except for the air inlet temperature in the condenser. The comparison of results is shown in Table 10 for the compressor Bitzer GSD60137VA4.
The COP WEUED is determined using Equation (2):
COP WEUED = ((0.34 × 5.98) + (0.34 × 5.46) + (0.2 × 4.97) + (0.12 × 4.43)) × 0.91 = 4.92 kW/kW
As can be analyzed from results, while the Net Sensible COP with AHRI 1361 conditions is 4.43 kW/kW (but with sensible heat equal to 4.03 kW/kW), the proposed COP WEUED index value is 5.41 kW/kW (but with sensible heat equal to 4.92 kW/kW). A considerable difference of 19% is determined, due to:
(a)
With the AHRI 1361 method for CRAC equipment, it is impossible to show the difference between fixed compressors and inverter for data centers in COP evaluations.
(b)
It has been proven that even at fixed thermal loads, there is an advantage of an air conditioning system with a variable flow of refrigerant fluid (inverter system).
(c)
Just as in AHRI, there are the IPLV and NPLV that use the same formula. The main difference between them is that IPLV is based on AHRI characteristics and NPLV is based on local characteristics. COP WEUED can also be used based on local characteristics. For example, using the same methodology for the city of São Paulo, Equation (2) provides:
COP NEUED = ((0.66 × 5.98) + (0.25 × 5.46) + (0.08 × 4.97) + (0.01 × 4.43)) × 0.91 = 5.23 kW/kW
That is, in the case of São Paulo, the difference would be 23% to COP NEUED vs. COP WEUED. While the current NetSCOP method value was 4.03 kW/kW, COP WEUED was 4.92 kW/kW, and with specific data from the city of São Paulo, COP NEUED was 5.23 kW/kW, all simulated with the same inverter compressor.

5. Conclusions

Despite the evolution of data centers in reducing energy consumption, the index used to measure and compare energy efficiency between CRAC equipment does not yet use inverter technology resources (variable refrigerant flow) in its methodology. The IPLV for equipment already was developed for comfort air conditioning, but the Net Sensible COP methodology favors fixed-capacity equipment. The energy efficiency index needs to keep up with new technologies; according to Wen et al. [18], the compressor frequency variation is one of the greatest technologies for reducing energy consumption in CRAC equipment. Both data center equipment and air conditioning systems are evolving. Another technology is microchannels coils with microfluids that can reduce heat dissipation from IT equipment with both air and water cooling [19]. However, microchannel coils can also be used in air conditioning equipment and improve energy savings [20]. The COP WEUED index measures more accurately the benefits of these new technologies.
Just as the IPLV is an important milestone for air conditioning equipment, a COP WEUED index was created based on the 29 major cities in the world, which could be an important tool to compare CRAC equipment, gathering the best of the AHRI 1361, which prioritizes sensible heat, with the calculation of the EUED method and also with the IPLV formula.
This methodology can be useful for further studies, as it can serve as a basis for manufacturing CRAC equipment with more connection to the real temperatures of the outside air, even recalculating the condenser fans, since the specific mass of the air is also related to temperatures.
In addition to these advantages, the proposed method favors high-performance air conditioning equipment in the range in which they will be truly used, since technologies such as free cooling and evaporative cooling are already realities in data centers.

Author Contributions

Conceptualization, A.F.S. and P.D.G.; methodology, A.F.S.; validation, A.F.S. and H.J.L.d.S.; formal analysis, A.F.S. and P.D.G.; investigation, A.F.S.; resources, H.J.L.d.S.; data curation, A.F.S. and P.D.G.; writing—original draft preparation, A.F.S. and H.J.L.d.S.; writing—review and editing, P.D.G.; visualization, H.J.L.d.S.; supervision, P.D.G.; project administration, A.F.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

Authors acknowledge Fundação para a Ciência e a Tecnologia (FCT—MCTES) for its financial support via the project UIDB/00151/2020 (C-MAST).

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

CRACComputer room air conditioner.
ASHRAEAmerican Society of Heating, Refrigeration, and Air Conditioning Engineers.
COPCoefficient of performance.
SLRSensible heat rate.
EUEDEnergy Usage Effectiveness Design.
WEUEDNWorld Energy Usage Effectiveness Design Nonstandard.
IPLVIntegrated part load value.
LEEDLeadership in Energy and Environmental Design.
ITInformation technologies.
TCTechnical committee.
AHRIAir-conditioning, Heating, and Refrigeration Institute.
NPLVNon-standard part load value.
PUEPower usage effectiveness

References

  1. CISCO. By The Numbers. Projecting the Future of Digital. 2020. Available online: https://www.cisco.com/c/en/us/solutions/executive-perspectives/annual-internet-report/infographic-c82-741491.html (accessed on 12 October 2020).
  2. Masanet, E.; Shehabi, A.; Lei, N.; Smith, S.; Kooney, J. Recalibrating global data center energy-use estimates. Science 2020, 367, 984–986. [Google Scholar] [CrossRef] [PubMed]
  3. Lei, N.; Masanet, E. Statistical analysis for predicting location-specific data center PUE and its improvement potential. Energy 2020, 201, 117556. [Google Scholar] [CrossRef]
  4. Amiri, A.; Ottelin, J.; Sorvari, J. Are LEED-Certified Buildings Energy-Efficient. Sustainability 2019, 11, 1672. [Google Scholar] [CrossRef][Green Version]
  5. Ni, J.; Bai, X. A review of air conditioning energy performance in data centers. Renew. Sustain. Energy Rev. 2017, 67, 625–640. [Google Scholar] [CrossRef]
  6. Santos, A.F.; Gaspar, P.D.; Souza, H.J.L.d. New Data Center Performance Index: Perfect Design Data Center—PDD. Climate 2020, 8, 110. [Google Scholar] [CrossRef]
  7. NREL. High-Performance Computing Data Center; National Renewable Energy Laboratory (NREL), U.S. Department of Energy: Denver, CO, USA, 2014.
  8. ASHRAE. Standard 90.1-2019 (SI Edition)—Energy Standard for Buildings Except Low-Rise Residential Buildings (ANSI Approved; IES Co-sponsored); American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE): Atlanta, GA, USA, 2019. [Google Scholar]
  9. ASHRAE. ASHRAE TC 9.9—Data Center Networking Equipment—Issues and Best Practices; American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE): Atlanta, GA, USA, 2016. [Google Scholar]
  10. ASHRAE. ASHRAE—Thermal Guidelines Classes; American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE): Atlanta, GA, USA, 2015. [Google Scholar]
  11. AHRI. AHRI 1360. Performance Rating of Computer and Data Processing Room Air Conditioners; Air-Conditioning, Heating, and Refrigeration Institute (AHRI): Arlington, VA, USA, 2017. [Google Scholar]
  12. AHRI. AHRI Standard 550/590 (I-P). Performance Rating of Water-Chilling and Heat Pump Water-Heating Packages Using the Vapor Compression Cycle; Air-Conditioning, Heating, and Refrigeration Institute (AHRI): Arlington, VA, USA, 2015. [Google Scholar]
  13. Santos, A.F.; Gaspar, P.D.; Souza, H.J.L. Evaluation of the Heat and Energy Performance of a Datacenter Using a New Efficiency Index: Energy Usage Effectiveness Design—EUED. Braz. Arch. Biol. Technol. 2019, 62, e19190021. [Google Scholar] [CrossRef]
  14. United Nations Department of Economic and Social Affairs, Population Division. The World’s Cities in 2018; ST/ESA/SER.A/417 Data Booklet; UN: New York, NY, USA, 2018. [Google Scholar]
  15. ASHRAE. ASHRAE Weather Data Viewer, Version 5.0; American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE): Atlanta, GA, USA, 2016. [Google Scholar]
  16. AHRI. AHRI Standard 1361. Standard for Performance Rating of Computer and Data Processing Room Air Conditioners; Air-Conditioning, Heating, and Refrigeration Institute (AHRI): Arlington, VA, USA, 2017. [Google Scholar]
  17. ES. DIN EN 12900—Refrigerant Compressors—Rating Conditions, Tolerances and Presentation of Manufacturer’s Performance Data; European Standard (ES), Deutsches Institut fur Normung E.V. (DIN): Berlin, Germany, 2013. [Google Scholar]
  18. Wen, J.; Wenlun, C.; Bei, C.; Yuyao, H. An inverter testing system for CRAC. In Proceedings of the 2014 International Conference on Intelligent Green Building and Smart Grid (IGBSG), Taipei, Taiwan, 23–25 April 2014; pp. 1–5. [Google Scholar] [CrossRef][Green Version]
  19. Cairone, F.; Gagliano, S.; Bucolo, M. Experimental study on the slug flow in a serpentine microchannel. Exp. Therm. Fluid Sci. 2016, 76, 34–44. [Google Scholar] [CrossRef]
  20. Park, C.Y.; Hrnjak, P. Experimental and numerical study on microchannel and round-tube condensers in a R410A residential air-conditioning system. Int. J. Refrig. 2008, 31, 822–831. [Google Scholar] [CrossRef]
Figure 1. Growth of global data center instances [2].
Figure 1. Growth of global data center instances [2].
Applsci 11 05950 g001
Figure 2. Distribution of electricity consumption in a typical DC with power usage effectiveness (PUE) = 2.1 [6].
Figure 2. Distribution of electricity consumption in a typical DC with power usage effectiveness (PUE) = 2.1 [6].
Applsci 11 05950 g002
Figure 3. Sectional view of CRAC equipment installed in a data center.
Figure 3. Sectional view of CRAC equipment installed in a data center.
Applsci 11 05950 g003
Figure 4. Plan view of CRAC installation within a downflow safe room.
Figure 4. Plan view of CRAC installation within a downflow safe room.
Applsci 11 05950 g004
Figure 5. Psychrometric chart with variation of case studies.
Figure 5. Psychrometric chart with variation of case studies.
Applsci 11 05950 g005
Figure 6. COPs vs. annual hours of refrigeration system usage.
Figure 6. COPs vs. annual hours of refrigeration system usage.
Applsci 11 05950 g006
Figure 7. COPs percentages.
Figure 7. COPs percentages.
Applsci 11 05950 g007
Table 1. Minimum efficiency requirements and the Net Sensible COP in ASHRAE 90.1-2019 Standard indicated for floor-mounted air conditioners and condensing units serving computer rooms [8].
Table 1. Minimum efficiency requirements and the Net Sensible COP in ASHRAE 90.1-2019 Standard indicated for floor-mounted air conditioners and condensing units serving computer rooms [8].
Equipment TypeStandard ModelNet Sensible Cooling Capacity
COP
Minimum Net Sensible COP (kW/kW)Rating Conditions Return Air (DBT/DPT)Test Procedure
Air-cooledDownflow<23 kW2.7029 °C/11 °C
(Class 2)
AHRI 1361
≥23 and <86 kW2.58
≥86 kW2.36
Upflow-duct<23 kW2.67
≥23 and <86 kW2.55
≥86 kW2.33
Upflow-nonduct<19 kW2.1624 °C/11 °C
(Class 1)
≥19 and <70 kW2.04
70 kW1.89
Horizontal<19 kW2.6535 °C/11 °C
(Class 3)
≥19 and <70 kW2.55
70 kW2.47
Table 2. Classes for certain characteristics of data center enclosures [9].
Table 2. Classes for certain characteristics of data center enclosures [9].
2008 Classes2011 ClassesApplicationsInformation Technology EquipmentEnvironmental Control
1A1 Applsci 11 05950 i001Enterprise servers,
storage products
Tightly controlled
2A2Volume servers,
storage products,
personal computers,
workstations,
laptop,
printers
Some control
A3Some control,
use of free cooling techniques when allowable
A4Some control,
near full-time usage of free cooling techniques
3BOffice,
home,
transportable environment, etc.
Personal computers,
workstations,
laptops,
printers
Minimal control
4CPoint of sale,
industrial,
factory, etc.
Point of sale equipment,
ruggedized controllers or computers,
PDAs
No control
Table 3. ASHRAE 2015 Thermal Guidelines classes [10].
Table 3. ASHRAE 2015 Thermal Guidelines classes [10].
Equipment Environmental Specifications for Air Cooling
Product OperationsProduct Power Off
ClassDBT (°C)Humidity RangeMDP (°C)Max. Elevation(m)Max. ΔT/hour (°C/h)DBT (°C)RH (%)
Recommend (suitable for all four classes)
A1–A418 to 27−9 °C (DPT) to 15 °C (DPT) and 60% RH
Allowable
A115 to 32−12 °C (DPT) and 8% RH to 17 °C (DPT) and 80% RH1730505/205 to 458 to 80
A210 to 35−12 °C (DPT) and 8% RH to 21 °C( DPT) and 80% RH2130505/205 to 458 to 80
A35 to 40−12 °C (DPT) and 8% RH to 24 °C (DPT) and 85% RH2430505/205 to 458 to 80
A45 to 45−12 °C (DPT) 8% RH to 24 °C (DPT) and 90% RH2430505/205 to 458 to 80
B5 to 358% RH to 28 °C(DPT) and 80% RH283050N.A.5 to 458 to 80
C5 to 408% RH to 28 °C(DPT) and 80% RH283050N.A.5 to 458 to 80
Table 4. Indoor return air temperature standard rating conditions [11].
Table 4. Indoor return air temperature standard rating conditions [11].
Mounting LocationsStandard ModelCooling (Return Air DBT/DPT) (°C)Humidification (Return Air DBT/DPT) (°C)
Ceiling mounted unitCeiling mounted unit ducted24.0/11.024.0/5.6
Ceiling mounted unit nonducted
Floor mounted unitUpflow unit nonducted24.0/11.0
Upflow unit ducted29.5/11.0
Downflow unit29.5/11.0
Horizontal flow unit35.0/11.0
Table 5. Heat rejection/cooling fluid standard rating conditions [11].
Table 5. Heat rejection/cooling fluid standard rating conditions [11].
System TypeFluid ConditionTest Condition
Air-cooled unitsEntering outdoor ambient DBT (°C)35.0
Water-cooled units (typically connected to a common glycol loop)Entering water temperature (EWT) (°C)28.5
Leaving water temperature (LWT) (°C)35.0
Water Flow rate (L/s)N/A
Glycol-cooled units (typically connected to a common glycol loop)Entering glycol temperature (°C)40.0
Leaving glycol temperature (°C)46.0
Glycol Flow rate (L/s)N/A
Glycol solutions concentration40% Propylene glycol by volume
Chiller-water units (typically connected to a common chilled water loop)Entering water temperature (EWT) (°C)10.0
Leaving water temperature (LWT) (°C)16.5
Table 6. Partial load conditions for calculating IPLV/NPLV.
Table 6. Partial load conditions for calculating IPLV/NPLV.
Evaporator (all types)UnitValues
100% capacity leaving water temperature (LWT)(°C)6.7
Volumetric flow(m3/h.ton)5.45
Fouling Factor(m2. °C/W)0.000018
Water condenser
100% capacity entering water temperature (EWT)(°C)29.4
75% capacity EWT(°C)23.9
50% capacity EWT(°C)18.3
25% capacity EWT(°C)18.3
0% capacity EWT(°C)18.3
Volumetric flow(m3/h.ton)6.81
Fouling Factor(m2. °C/W)0.000044
Air condenser
100% capacity entering water temperature (EWT)(°C)35.0
75% capacity EWT(°C)26.7
50% capacity EWT(°C)18.3
25% capacity EWT(°C)12.8
Table 7. Air inlet temperatures in the condenser.
Table 7. Air inlet temperatures in the condenser.
LevelCalculation Temperature (°C)
COP125.5 (COP1 average from 24.1 to 27 °C)
COP228.5 (COP2 average from 27.1 to 30 °C)
COP331.5 (COP3 average from 30.1 to 33 °C)
COP435 (All temperatures above 33.1 °C)
Table 8. World cities and their populations [14].
Table 8. World cities and their populations [14].
No.CityCountryPopulation [×1000]
1TokyoJapan37,468
2DelhiIndia28,514
3ShanghaiChina25,582
4São PauloBrazil21,650
5Mexico CityMexico21,581
6Al-Qahirah-CairoEgypt20,076
7MumbaiIndia19,980
8BeijingChina19,618
9DhakaBangladesh19,578
10OsakaJapan19,281
11New YorkUSA18,810
12KarachiPakistan15,400
13Buenos AiresArgentina14,967
14ChongqingChina14,838
15IstambulTurkey14,751
16CalcuttaIndia14,681
17ManilaPhilippines13,482
18LagosNigeria13,462
19Rio de JaneiroBrazil13,293
20TianjinChina13,215
21KinshasaDemocratic Republic Congo13,171
22GuangzhouChina12,638
23Los AngelesUSA12,458
24MoscowRussia12,410
25ShenzhenChina11,908
26LahorePakistan11,738
27BangaloreIndia11,440
28ParisFrance10,901
29BogotaColombia10,574
Table 9. COPs of cities worldwide [15].
Table 9. COPs of cities worldwide [15].
CityCountryAnnual Hours COP1 (24 to 26.9 °C)Annual Hours COP2 (27 to 29.9 °C)Annual Hours COP3 (30 to 32.9 °C)Annual Hours COP4 (≥33 °C)
TokyoJapan91067428457
DelhiIndia879148113231873
ShanghaiChina1190817340125
São PauloBrazil1.21545414713
Mexico CityMexico2.610166911681020
Al-Qahirah-CairoEgypt1345947785660
MumbaiIndia191534121844468
BeijingChina105152626286
* DhakaBangladesh15612.6851.487794
OsakaJapan959849416130
New YorkUSA5452609518
KarachiPakistan1201247618481053
Buenos AiresArgentina1083389859
ChongqingChina743743437309
IstambulTurkey83044312615
Kolkata (Calcutta)India156126851487794
ManilaPhilippines195944362024262
* LagosNigeria1345176217082686
Rio de JaneiroBrazil2807153349491
TianjinChina95566832194
* KinshasaDem. Rep. Congo29661746997216
GuangzhouChina17371685822341
Los AngelesUSA19863184
MoscowRussia20585236
ShenzhenChina17082049911138
LahorePakistan985132011031525
BangaloreIndia21641156520156
ParisFrance2571134212
BogotaColombia20.20.040
TOTAL 36,88637,12621,11712,955
Average 1271.921280.20728.17446.72
Total Hours 3727.02
Note: * Specifically, the ASHRAE Weather Data Viewer [15] has temperatures in 26 of the 29 cities. For the cities of Dhaka, Lagos, and Kinshasa values from nearby cities, Calcutta, Niamey, Brazzaville, respectively, were used.
Table 10. AHRI vs. COP WEUED result comparison.
Table 10. AHRI vs. COP WEUED result comparison.
AHRI COP1COP2COP3COP4
Compressor freq. (Hz) 7364676973
Cooling capacity (kW)5555555555
Evaporator capacity (kW)5555555555
Condenser capacity (kW)67.564.165.66667.5
COP/EER (kW/kW)4.435.985.464.974.43
Min. cooling capacity (kW)26.4 (35 Hz)329.6 (35 Hz)28.6 (35 Hz)27.7 (35 Hz)26.4 (35 Hz)
Max. cooling capacity (kW)56.4 (75 Hz)63.5 (75 Hz)61.3 (75 Hz)59 (75 Hz)56.4 (75 Hz)
Mass flow (kg/h)12401115115911861240
Discharge gas temp. w/o cooling (ºC)76.764.26871.976.7
Result COP (kW/kW)4.034.92
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Santos, A.F.; Gaspar, P.D.; de Souza, H.J.L. Evaluation of the Thermal Performance and Energy Efficiency of CRAC Equipment through Mathematical Modeling Using a New Index COP WEUED. Appl. Sci. 2021, 11, 5950. https://doi.org/10.3390/app11135950

AMA Style

Santos AF, Gaspar PD, de Souza HJL. Evaluation of the Thermal Performance and Energy Efficiency of CRAC Equipment through Mathematical Modeling Using a New Index COP WEUED. Applied Sciences. 2021; 11(13):5950. https://doi.org/10.3390/app11135950

Chicago/Turabian Style

Santos, Alexandre F., Pedro D. Gaspar, and Heraldo J. L. de Souza. 2021. "Evaluation of the Thermal Performance and Energy Efficiency of CRAC Equipment through Mathematical Modeling Using a New Index COP WEUED" Applied Sciences 11, no. 13: 5950. https://doi.org/10.3390/app11135950

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop