|Publication type:||Article in scientific journal|
|Type of review:||Peer review (publication)|
|Title:||Neural network-based prediction and control of air flow in a data center|
|Authors:||De Lorenzi, Flavio|
|Published in:||Journal of Thermal Science and Engineering Applications|
|Publisher / Ed. Institution:||The American Society of Mechanical Engineers|
|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%.|
|Fulltext version:||Published version|
|License (according to publishing contract):||Licence according to publishing contract|
|Departement:||School of Engineering|
|Organisational Unit:||Institute of Applied Mathematics and Physics (IAMP)|
|Appears in Collections:||Publikationen School of Engineering|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.