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Improving Prediction Accuracy Concerning the Thermal Environment of a Data Center by Using Design of Experiments

Author

Listed:
  • Naoki Futawatari

    (NTT FACILITIES, INC., Minato-ku, Tokyo 1080023, Japan)

  • Yosuke Udagawa

    (NTT FACILITIES, INC., Minato-ku, Tokyo 1080023, Japan)

  • Taro Mori

    (Faculty of Engineering, Hokkaido University, Kita-ku, Sapporo, Hokkaido 0608628, Japan)

  • Hirofumi Hayama

    (Faculty of Engineering, Hokkaido University, Kita-ku, Sapporo, Hokkaido 0608628, Japan)

Abstract

In data centers, heating, ventilation, and air-conditioning (HVAC) consumes 30–40% of total energy consumption. Of that portion, 26% is attributed to fan power, the ventilation efficiency of which should thus be improved. As an alternative method for experimentations, computational fluid dynamics (CFD) is used. In this study, “parameter tuning”—which aims to improve the prediction accuracy of CFD simulation—is implemented by using the method known as “design of experiments”. Moreover, it is attempted to improve the thermal environment by using a CFD model after parameter tuning. As a result of the parameter tuning, the difference between the result of experimental-measurement results and simulation results for average inlet temperature of information-technology equipment (ITE) installed in the ventilation room of a test data center was within 0.2 °C at maximum. After tuning, the CFD model was used to verify the effect of advanced insulation such as raised-floor fixed panels and show the possibility of reducing fan power by 26% while keeping the recirculation ratio constant. Improving heat-insulation performance is a different approach from the conventional approach (namely, segregating cold/hot airflow) to improving ventilation efficiency, and it is a possible solution to deal with excessive heat generated in data centers.

Suggested Citation

  • Naoki Futawatari & Yosuke Udagawa & Taro Mori & Hirofumi Hayama, 2020. "Improving Prediction Accuracy Concerning the Thermal Environment of a Data Center by Using Design of Experiments," Energies, MDPI, vol. 13(18), pages 1-21, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4595-:d:408769
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    References listed on IDEAS

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    1. Jing Ni & Bowen Jin & Bo Zhang & Xiaowei Wang, 2017. "Simulation of Thermal Distribution and Airflow for Efficient Energy Consumption in a Small Data Centers," Sustainability, MDPI, vol. 9(4), pages 1-16, April.
    2. Fujen Wang & Yishun Huang & BowoYuli Prasetyo, 2019. "Energy-Efficient Improvement Approaches through Numerical Simulation and Field Measurement for a Data Center," Energies, MDPI, vol. 12(14), pages 1-18, July.
    3. 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.
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    Cited by:

    1. Joanna Piotrowska-Woroniak & Krzysztof Cieśliński & Grzegorz Woroniak & Jonas Bielskus, 2022. "The Impact of Thermo-Modernization and Forecast Regulation on the Reduction of Thermal Energy Consumption and Reduction of Pollutant Emissions into the Atmosphere on the Example of Prefabricated Build," Energies, MDPI, vol. 15(8), pages 1-32, April.
    2. Joanna Kajewska-Szkudlarek & Jan Bylicki & Justyna Stańczyk & Paweł Licznar, 2021. "Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems," Energies, MDPI, vol. 14(22), pages 1-15, November.

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