Numerical and experimental investigations on thermal management for data center with cold aisle containment configuration

Lee, Yee-Ting; Wen, Chih-Yung; Shih, Yen‐Ping; Li, Zhengtong; Yang, An-Shik

doi:10.1016/j.apenergy.2021.118213

Cited by 19 publications

(8 citation statements)

References 41 publications

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“…In Figure 6b, the daily PUE value ranges from 1.7 to 5.2; the 30-day average is 2.5. This average value is higher than the US average value of 1.6 [8].…”

Section: Server Room Cse and Puecontrasting

confidence: 58%

“…PUE is the ratio of total facility power to ICT equipment power; the closer PUE is to 1, the more efficient the data center is considered to be [6]. In 2020, the average PUE value for typical data centers in the US was 1.6 [8]. In comparison, the best hyper-scale data centers run by Google, Microsoft, and Amazon achieved PUE values of 1.1 [2,5,9], 1.25 [9], and 1.14 [9].…”

Section: Introductionmentioning

confidence: 99%

“…In comparison, the best hyper-scale data centers run by Google, Microsoft, and Amazon achieved PUE values of 1.1 [2,5,9], 1.25 [9], and 1.14 [9]. Large-scale data centers in the private sector following LEED design concepts reported an average PUE value of 1.38 in 2022 [8]. Academic data centers are typically known to be less efficient.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Anthropogenic Waste Heat Emission from an Academic Data Center

Ding,

Ebrahimi,

Kim

et al. 2024

Energies

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The rapid growth in computing and data transmission has significant energy and environmental implications. While there is considerable interest in waste heat emission and reuse in commercial data centers, opportunities in academic data centers remain largely unexplored. In this study, real-time onsite waste heat data were collected from a typical academic data center and an analysis framework was developed to determine the quality and quantity of waste heat that can be contained for reuse. In the absence of a comprehensive computer room monitoring system, real-time thermal data were collected from the data center using two arrays of thermometers and thermo-anemometers in the server room. Additionally, a computational fluid dynamics model was used to simulate temperature distribution and identify “hot spots” in the server room. By simulating modification of the server room with a hot air containment system, the return air temperature increased from 23 to 46 °C and the annual waste heat energy increased from 377 to 2004 MWh. Our study emphasizes the importance of containing waste heat so that it can be available for reuse, and also, that reusing the waste heat has value in not releasing it to the environment.

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“…In Figure 6b, the daily PUE value ranges from 1.7 to 5.2; the 30-day average is 2.5. This average value is higher than the US average value of 1.6 [8].…”

Section: Server Room Cse and Puecontrasting

confidence: 58%

Section: Introductionmentioning

confidence: 99%

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Analysis of Anthropogenic Waste Heat Emission from an Academic Data Center

Ding,

Ebrahimi,

Kim

et al. 2024

Energies

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“…Compared to the typical arrangement, test findings show that CAC consistently produces low cabinet inlet air temperatures and saves around 22% on cooling energy. Lee et al, [12] have studied a CAC with an overhead downward flow cooling system with a heat exchanger and an evaporative water chiller as a part of the design. The power usage effectiveness of 1.38 demonstrates the substantial energy savings potential offered by this approach.…”

Section: Challenges In Traditional Air Coolingmentioning

confidence: 99%

A Comparative Numerical Study of Effectiveness of Cold Aisle Containment in Data Centers by Varying Rack Porosity Using Computational Fluid Dynamics

Chethana G D,

Sadashivegowda

2024

CFDL

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Depending on specific needs and workloads, several racks with varied component densities may be used in a data center. As server density increases, porosity decreases, and the opposite is also true. A frequently used method called cold aisle containment separates hot air and cold air flows in data center settings to improve cooling effectiveness. In this paper, the performance of a data center is investigated using computational fluid dynamics, and the influence of porosity on cold aisle containment is evaluated using well-established non-dimensional performance parameters. The value of RTI without containment increased with an increase in porosity and a maximum RTI of 217 was found with a porosity of 0.75. Regardless of the rack number and porosity, containment provides the optimal RTI values. The results indicate that the SHI and RHI values for rack 1 for all porosities, without confinement, are outside of the permissible range and at higher porosities containment has no significant effects on SHI and RHI. RCILO values for racks 2, 3, and 4, with or without containment, fall within the 80-85% range, indicating temperatures below 13°C. RCIHI value is 1 for all cases considered indicating no rack is out of the recommended temperature of 25°C.

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“…1. Cold aisle containment system (CACS) or hot aisle containment system (HACS) is considered to be an important way to avoid hot and cold air mixing in the DC [33,34]. Unlike traditional DCs such as raised-floor air supply and row-based air conditioning, which normally use the cold aisle containment systems (CACS) [35][36][37], while fan wall DCs use hot aisle containment systems (HACS) for better adaptability and scalability [38,39].…”

Section: Introductionmentioning

confidence: 99%