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Determining wasted energy in the airside of a perimeter-cooled data center via direct computation of the Exergy Destruction

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  • Silva-Llanca, Luis
  • Ortega, Alfonso
  • Fouladi, Kamran
  • del Valle, Marcelo
  • Sundaralingam, Vikneshan

Abstract

To keep pace with the growing energy demand, legacy air-cooled data centers begun implementing energy efficiency strategies: Perfecting air flow management, enhancing cooling air delivery and collecting (re-using) waste heat. However, one may wonder: What is the magnitude of these energy savings? Is it worth the effort? The second law of Thermodynamics offers unique insights about energy wasteful practices by estimating the Exergy Destruction in a system. Exergy is equivalent to the “available energy”, hence the presence of inefficiencies “Destroys Exergy”. In this work, we numerically modeled the behavior of the airside in an existing data center laboratory (CEETHERM) using the commercial Finite Volume software 6SigmaDCXTM. The collected numerical data were used to post-process two Exergy Destruction approaches (Direct and Indirect method), whose behavior was tested against: (1) A simplified study case and (2) Actual data center flow. Both approaches worked well against the study case, although for case (2) the Indirect Method–which neglects turbulence effects–predicted zones of artificial negative Exergy Destruction. The Direct Method permitted associating large inefficiencies in the airflow to hot–cold airstream pre-mixing and important pressure drops in the raised floor. The airside Exergy Destruction encompassed a significant amount of the total irreversibilities in the system, suggesting that mitigating (or eliminating) it, can substantially improve energy saving efforts, especially in legacy data centers.

Suggested Citation

  • Silva-Llanca, Luis & Ortega, Alfonso & Fouladi, Kamran & del Valle, Marcelo & Sundaralingam, Vikneshan, 2018. "Determining wasted energy in the airside of a perimeter-cooled data center via direct computation of the Exergy Destruction," Applied Energy, Elsevier, vol. 213(C), pages 235-246.
    • Handle: RePEc:eee:appene:v:213:y:2018:i:c:p:235-246
    DOI: 10.1016/j.apenergy.2018.01.031
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    References listed on IDEAS

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    Cited by:

    1. Manaserh, Yaman M. & Tradat, Mohammad I. & Bani-Hani, Dana & Alfallah, Aseel & Sammakia, Bahgat G. & Nemati, Kourosh & Seymour, Mark J., 2022. "Machine learning assisted development of IT equipment compact models for data centers energy planning," Applied Energy, Elsevier, vol. 305(C).
    2. Gupta, Rohit & Moazamigoodarzi, Hosein & MirhoseiniNejad, SeyedMorteza & Down, Douglas G. & Puri, Ishwar K., 2020. "Workload management for air-cooled data centers: An energy and exergy based approach," Energy, Elsevier, vol. 209(C).
    3. Heran Jing & Zhenhua Quan & Yaohua Zhao & Lincheng Wang & Ruyang Ren & Ruixue Dong & Yuting Wu, 2022. "Experimental Investigation of Heat Transfer and Flow Characteristics of Split Natural Cooling System for Data Center Based on Micro Heat Pipe Array," Energies, MDPI, vol. 15(12), pages 1-22, June.
    4. Cheng Liu & Hang Yu, 2021. "Evaluation and Optimization of a Two-Phase Liquid-Immersion Cooling System for Data Centers," Energies, MDPI, vol. 14(5), pages 1-21, March.
    5. Gupta, Rohit & Asgari, Sahar & Moazamigoodarzi, Hosein & Pal, Souvik & Puri, Ishwar K., 2020. "Cooling architecture selection for air-cooled Data Centers by minimizing exergy destruction," Energy, Elsevier, vol. 201(C).
    6. Moazamigoodarzi, Hosein & Tsai, Peiying Jennifer & Pal, Souvik & Ghosh, Suvojit & Puri, Ishwar K., 2019. "Influence of cooling architecture on data center power consumption," Energy, Elsevier, vol. 183(C), pages 525-535.
    7. Gupta, Rohit & Asgari, Sahar & Moazamigoodarzi, Hosein & Down, Douglas G. & Puri, Ishwar K., 2021. "Energy, exergy and computing efficiency based data center workload and cooling management," Applied Energy, Elsevier, vol. 299(C).

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