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

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Title: Improving Prediction Accuracy Concerning the Thermal Environment of a Data Center by Using Design of Experiments
Authors: Futawatari, Naoki Browse this author
Udagawa, Yosuke Browse this author
Mori, Taro Browse this author →KAKEN DB
Hayama, Hirofumi Browse this author →KAKEN DB
Keywords: data center
computational fluid dynamics (CFD)
design of experiments (DOE)
prediction accuracy
air-management metrics
energy conservation
Issue Date: Sep-2020
Publisher: MDPI
Journal Title: Energies
Volume: 13
Issue: 18
Start Page: 4595
Publisher DOI: 10.3390/en13184595
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 degrees 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.
Rights: © [2020] by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution License (
Type: article
Appears in Collections:工学院・工学研究院 (Graduate School of Engineering / Faculty of Engineering) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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