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.

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二渡 直樹 * ，羽山 広文 ** ，森 太郎 *** ，菊田 弘輝 **** ，豊原 範之 ***** Naoki FUTAWATARI Hirofumi HAYAMA Taro MORI Koki KIKUTA and Noriyuki TOYOHARAIn the conventional design method, it's difficult to estimate exhaust gas recirculation and short circuit that worsen thermal environment. It's preventing the improvement of air conditioning efficiency because thermal conditions become severe in anticipation of rising temperature caused to them. In this study, we create an air flow network model that predicts cooling characteristics of ICT equipment. We study how to predict machine cooling characteristics by analyzing this model.And we build a server rack which imitates the air flow system for machine cooling to carry out comparative evaluation between model analysis and actual measurement. Result of the comparison, the analysis captured the features of the actual tendency, especially average. Then, we made a simple prediction chart of machine cooling characteristics for air conditioning design. Keywords :

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Energy-saving in regard to heating, ventilation, and air-conditioning (HVAC) in data centers is strongly required. Therefore, to improve the operating efficiency of the cooling equipment and extend the usage time of the economizer used for cooling information-technology equipment (ITE) in a data center, it is often the case that a high air-supply temperature within the range in which the ITE can be sufficiently cooled is selected. In the meantime, it is known that when the ambient temperature of the ITE rises, the speed of the built-in cooling fan increases. Acceleration of the built-in fan is thought to affect the cooling performance and energy consumption of the data center. Therefore, a method for predicting the temperature of a data center—which simply correlates supply-air temperature with ITE inlet temperature by utilizing existing indicators, such as air-segregation efficiency (ASE)—is proposed in this study. Moreover, a method for optimizing the total energy consumption of a data center is proposed. According to the prediction results obtained under the assumption of certain computer-room air-conditioning (CRAC) conditions, by lowering the ITE inlet temperature from 27 °C to 18 °C, the total energy consumption of the machine room is reduced by about 10%.