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The rapid global increase in energy consumption by data centres requires new improved cooling solutions and techniques to be developed and implemented. In a typical data centre, approximately a third of the total power consumption is needed for the cooling infrastructure, resulting in high power usage effectiveness (PUE) values. The main culprits of raised PUE are legacy air-cooled data centres, exhausting only low grade waste heat for which capture and re-use is challenging. This study investigates numerically the potential for energy recuperation by a server-level internal layout optimization for a hybrid air/liquid-cooled server. The approach combines multi-objective genetic algorithm (MOGA) and entropy generation minimization (EGM) techniques to incorporate the multiple objectives involved in solving this problem, and examines the cooling performance and waste heat recovery potential. In order to evaluate the potential for waste heat recovery, an extra entropy generation term ṠΔT,ext" role="presentation" style="box-sizing: border-box; margin: 0px; padding: 0px; display: inline-block; line-height: normal; font-size: 16.2px; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; position: relative;">ṠΔT,extis introduced, representing an air/liquid heat exchanger at the rear of the server. The effect of modifying the internal component layout on pressure drop and the outlet temperature profile are of primary interest, due to their direct impact on fan power usage and energy recuperation potential. The CFD model of the baseline configuration is validated using experimental pressure measurements conducted on a real blade server. The research demonstrates that a basic server layout optimization such as changing the memory module angles and spacing could enhance both the cooling effectiveness but also improve the potential for waste heat recovery from the air stream. The maximum reduction in entropy generation rate due to server layout optimization is 15%, while the outlet temperature uniformity can be improved by up to 42%.
Sakanova, A., Alimohammadi, S. McEvody, J., Battaglioi, S. & Persoons, T. (2019). Multi-objective layout optimization of a generic hybrid-cooled data centre blade server. Applied Thermal Engineering V156, 25 June 2019,pp. 514-523. doi:10.1016/j.applthermaleng.2019.04.071