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Computational Fluid Dynamics Modeling and Validating Experiments of Airflow in a Data Center

Author

Listed:
  • Emelie Wibron

    (Division of Fluid and Experimental Mechanics, Luleå University of Technology, SE-971 87 Luleå, Sweden)

  • Anna-Lena Ljung

    (Division of Fluid and Experimental Mechanics, Luleå University of Technology, SE-971 87 Luleå, Sweden)

  • T. Staffan Lundström

    (Division of Fluid and Experimental Mechanics, Luleå University of Technology, SE-971 87 Luleå, Sweden)

Abstract

The worldwide demand on data storage continues to increase and both the number and the size of data centers are expanding rapidly. Energy efficiency is an important factor to consider in data centers since the total energy consumption is huge. The servers must be cooled and the performance of the cooling system depends on the flow field of the air. Computational Fluid Dynamics (CFD) can provide detailed information about the airflow in both existing data centers and proposed data center configurations before they are built. However, the simulations must be carried out with quality and trust. The k – ε model is the most common choice to model the turbulent airflow in data centers. The aim of this study is to examine the performance of more advanced turbulence models, not previously investigated for CFD modeling of data centers. The considered turbulence models are the k – ε model, the Reynolds Stress Model (RSM) and Detached Eddy Simulations (DES). The commercial code ANSYS CFX 16.0 is used to perform the simulations and experimental values are used for validation. It is clarified that the flow field for the different turbulence models deviate at locations that are not in the close proximity of the main components in the data center. The k – ε model fails to predict low velocity regions. RSM and DES produce very similar results and, based on the solution times, it is recommended to use RSM to model the turbulent airflow data centers.

Suggested Citation

  • Emelie Wibron & Anna-Lena Ljung & T. Staffan Lundström, 2018. "Computational Fluid Dynamics Modeling and Validating Experiments of Airflow in a Data Center," Energies, MDPI, vol. 11(3), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:644-:d:136181
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    References listed on IDEAS

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    Cited by:

    1. Isazadeh, Amin & Ziviani, Davide & Claridge, David E., 2023. "Global trends, performance metrics, and energy reduction measures in datacom facilities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 174(C).
    2. Cho, Jinkyun & Kim, Youngmo, 2021. "Development of modular air containment system: Thermal performance optimization of row-based cooling for high-density data centers," Energy, Elsevier, vol. 231(C).
    3. Yeliang Qiu & Congfeng Jiang & Yumei Wang & Dongyang Ou & Youhuizi Li & Jian Wan, 2019. "Energy Aware Virtual Machine Scheduling in Data Centers," Energies, MDPI, vol. 12(4), pages 1-21, February.
    4. Rickard Brännvall & Jonas Gustafsson & Fredrik Sandin, 2023. "Modular and Transferable Machine Learning for Heat Management and Reuse in Edge Data Centers," Energies, MDPI, vol. 16(5), pages 1-24, February.
    5. Jinkyun Cho & Jesang Woo & Beungyong Park & Taesub Lim, 2020. "A Comparative CFD Study of Two Air Distribution Systems with Hot Aisle Containment in High-Density Data Centers," Energies, MDPI, vol. 13(22), pages 1-19, November.
    6. Emelie Wibron & Anna-Lena Ljung & T. Staffan Lundström, 2019. "Comparing Performance Metrics of Partial Aisle Containments in Hard Floor and Raised Floor Data Centers Using CFD," Energies, MDPI, vol. 12(8), pages 1-17, April.
    7. Chu, Wen-Xiao & Wang, Chi-Chuan, 2019. "A review on airflow management in data centers," Applied Energy, Elsevier, vol. 240(C), pages 84-119.

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