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Comparing Performance Metrics of Partial Aisle Containments in Hard Floor and Raised Floor Data Centers Using CFD

Author

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  • 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

In data centers, efficient cooling systems are required to both keep the energy consumption as low as possible and to fulfill the temperature requirements. The aim of this work is to numerically investigate the effects of using partial aisle containment between the server racks for hard and raised floor configurations. The computational fluid dynamics (CFD) software ANSYS CFX was used together with the Reynolds stress turbulence model to perform the simulations. Velocity measurements in a server room were used for validation. Boundary conditions and the load of each rack were also retrieved from the experimental facility, implying an uneven load between the racks. A combination of the performance metrics Rack Cooling Index (RCI), Return Temperature Index (RTI) and Capture Index (CI) were used to evaluate the performance of the cooling systems for two supply flow rates at a 100% and 50% of operating condition. Based on the combination of performance metrics, the airflow management was improved in the raised floor configurations. With the supply flow rate set to operating conditions, the RCI was 100% for both raised floor and hard floor setups. The top- or side-cover fully prevented recirculation for the raised floor configuration, while it reduced the recirculation for the hard floor configuration. However, the RTI was low, close to 40% in the hard floor case, indicating poor energy efficiency. With the supply flow rate decreasing with 50%, the RTI increased to above 80%. Recirculation of hot air was indicated for all the containments when the supply rate was 50%, but the values of RCI still indicated an acceptable performance of the cooling system.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1473-:d:224066
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    References listed on IDEAS

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    1. Tatchell-Evans, Morgan & Kapur, Nik & Summers, Jonathan & Thompson, Harvey & Oldham, Dan, 2017. "An experimental and theoretical investigation of the extent of bypass air within data centres employing aisle containment, and its impact on power consumption," Applied Energy, Elsevier, vol. 186(P3), pages 457-469.
    2. Chu, Wen-Xiao & Wang, Chi-Chuan, 2019. "A review on airflow management in data centers," Applied Energy, Elsevier, vol. 240(C), pages 84-119.
    3. Ni, Jiacheng & Bai, Xuelian, 2017. "A review of air conditioning energy performance in data centers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 625-640.
    4. 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.
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    Cited by:

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    2. Kosuke Sasakura & Takeshi Aoki & Masayoshi Komatsu & Takeshi Watanabe, 2020. "A Temperature-Risk and Energy-Saving Evaluation Model for Supporting Energy-Saving Measures for Data Center Server Rooms," Energies, MDPI, vol. 13(19), pages 1-22, October.
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    4. Kosuke Sasakura & Takeshi Aoki & Masayoshi Komatsu & Takeshi Watanabe, 2020. "Rack Temperature Prediction Model Using Machine Learning after Stopping Computer Room Air Conditioner in Server Room," Energies, MDPI, vol. 13(17), pages 1-17, August.

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