期刊
APPLIED THERMAL ENGINEERING
卷 62, 期 1, 页码 48-57出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.applthermaleng.2013.09.006
关键词
Zonal modeling; CFD modeling; Dynamic compact thermal modeling; Control; Artificial neural network; Data center
资金
- Small Scale Systems Integration and Packaging Center (S3IP) at Binghamton University
- New York State Office of Science, Technology and Innovation (NYSTAR)
- Empire State Development Corporation
Full-scale data center thermal modeling and optimization using computational fluid dynamics (CFD) is generally an extremely time-consuming process. This paper presents the development of a velocity propagation method (VPM) based dynamic compact zonal model to efficiently describe the airflow and temperature patterns in a data center with a contained cold aisle. Results from the zonal model are compared to those from full CFD simulations of the same configuration. A primary objective of developing the compact model is real-time predictive capability for control and optimization of operating conditions for energy utilization. A scheme is proposed that integrates zonal model results for temperature and air flow rates with a proportional-integral-derivative (PID) controller to predict and control rack inlet temperature more precisely. The approach also uses an Artificial Neural Network (ANN) in combination with a Genetic Algorithm (GA) optimization procedure. The results show that the combined approach, built on the VPM based zonal model, can yield an effective real-time design and control tool for energy efficient thermal management in data centers. (c) 2013 Elsevier Ltd. All rights reserved.
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