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

Naoki Futawatari, Yosuke Udagawa, Taro Mori and Hirofumi Hayama
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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

Energies, 2020, vol. 13, issue 18, 1-21

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.

Keywords: data center; air-conditioning; computational fluid dynamics (CFD); design of experiments (DOE); prediction accuracy; air-management metrics; energy conservation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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