Title: Neural network-based prediction and control of air flow in a data center
Authors : De Lorenzi, Flavio
Vömel, Christof
Published in : Journal of thermal science and engineering applications
Volume(Issue) : 4
Issue : 2
Pages : 021005
Publisher / Ed. Institution : The American Society of Mechanical Engineers
Issue Date: 2012
License (according to publishing contract) : Licence according to publishing contract
Type of review: Peer review (publication)
Language : English
Subject (DDC) : 690: Building and construction
Abstract: As modern data centers continue to grow in power, size, and numbers, there is an urgent need to reduce energy consumption by optimized cooling strategies. In this paper, we present a neural network-based prediction of air flow in a data center that is cooled through perforated floor tiles. With a significantly smaller execution time than computational fluid dynamics, it predicts in real-time server inlet temperatures and can detect whether prevalent air flow cools the servers sufficiently to guarantee safe operation. Combined with a cooling system model, we obtain a temperature and air flow control algorithm that is fast and accurate enough to find an optimal operating point of the data center cooling system in real-time. We also demonstrate the performance of our algorithm on a reference data center and show that energy consumption can be reduced by up to 30%.
Departement: School of Engineering
Organisational Unit: Institute of Applied Mathematics and Physics (IAMP)
Publication type: Article in scientific journal
DOI : 10.1115/1.4005605
ISSN: 1948-5085
URI: https://digitalcollection.zhaw.ch/handle/11475/13434
Appears in Collections:Publikationen School of Engineering

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