Local and global dimensional CFD simulations and analyses to optimize server-fin design for improved energy efficiency in data centers

Sakaino, Hidetomo

doi:10.1109/itherm.2014.6892330

Cited by 5 publications

(1 citation statement)

References 40 publications

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“…In previous studies using CFD include the following. Sakaino uses CFD to set the CRAC based on the load balance of the ICT equipment and to predict the temperature environment in the server room [11]. Winbron et al and Lin et al use CFD to understand the effect of aisle containment on the temperature environment of the server room [12,13].…”

Section: Introductionmentioning

confidence: 99%

A Temperature-Risk and Energy-Saving Evaluation Model for Supporting Energy-Saving Measures for Data Center Server Rooms

et al. 2020

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As data centers have become increasingly important in recent years their operational management must attain higher efficiency and reliability. Moreover, the power consumption of a data center is extremely large, and it is anticipated that it will continue to increase, so energy saving has become an urgent issue concerning data centers. In the meantime, the environment of the server rooms in data centers has become complicated owing to the introduction of virtualization technology, the installation of high-heat density information and communication technology (ICT) equipment and racks, and the diversification of cooling methods. It is very difficult to manage a server room in the case of such a complicated environment. When energy-saving measures are implemented in a server room with such a complicated environment, it is important to evaluate “temperature risks” in advance and calculate the energy-saving effect after the measures are taken. Under those circumstances, in this study, two prediction models are proposed: a model that predicts the rack intake temperature (so that the temperature risk can be evaluated in support of energy-saving measures implemented in the server room) and a model that evaluates the energy-saving effect (in relation to a baseline). Specifically, the models were constructed by using machine learning. The first constructed model evaluates the temperature risk in a verification room in advance, and it was confirmed that the model can evaluate the risk beforehand with high accuracy. The second constructed model—“baseline model” hereafter—supports energy-saving measures, and it was confirmed that the model can calculate the baseline (energy consumption) with high accuracy as well. Moreover, the effect of proposal process of energy-saving measures in the verification room was verified by using the two proposed models. In particular, the effectiveness of the model for evaluating temperature risk in advance and that of a technology for visualizing the energy-saving effect were confirmed.

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Section: Introductionmentioning

confidence: 99%