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A dynamic compact thermal model for data center analysis and control using the zonal method and artificial neural networks

Authors
Journal
Applied Thermal Engineering
1359-4311
Publisher
Elsevier
Publication Date
Volume
62
Issue
1
Identifiers
DOI: 10.1016/j.applthermaleng.2013.09.006
Keywords
  • Zonal Modeling
  • Cfd Modeling
  • Dynamic Compact Thermal Modeling
  • Control
  • Artificial Neural Network
  • Data Center
Disciplines
  • Computer Science
  • Design
  • Mathematics
  • Physics

Abstract

Abstract 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.

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