Estimation of turbulence closure coefficients for data centers using machine learning algorithms

S. Yarlanki, B. Rajendran, H. Hamann

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Scopus citations

Abstract

CFD models of data centers often use two equation turbulence models such as the k-ε model. These models are based on closure coefficients or turbulence model constants determined from a combination of scaling/dimensional analysis and experimental measurements of flows in simple configurations. The simple configurations used to derive the turbulence model constants are often two dimensional and do not have many of the complex flow characteristics found in engineering flows. Such models perform poorly, especially in flows with large pressure gradients, swirl and strong three dimensionality, as in the case of data centers. This study attempts to use machine learning algorithms to optimize the model constants of the k-ε turbulence model for a data center by comparing simulated data with experimentally measured temperature values. For a given set of turbulence constants, we determine the Root Mean Square 'error' in the model, defined as the difference between experimentally measured temperature from a data center test cell and CFD calculations using the k-ε model. An artificial neural network (ANN) based method for parameter identification is then used to find the optimal values for turbulence constants such that the error is minimized. The optimum turbulence model constants obtained by our study results in lowering the RMS error by 25% and absolute average error by 35% compared to the error obtained by using standard k-ε model constants.

Original languageEnglish (US)
Title of host publicationProceedings of the 13th InterSociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems, ITherm 2012
Pages38-42
Number of pages5
DOIs
StatePublished - Sep 18 2012
Externally publishedYes
Event13th InterSociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems, ITherm 2012 - San Diego, CA, United States
Duration: May 30 2012Jun 1 2012

Publication series

NameInterSociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems, ITHERM
ISSN (Print)1936-3958

Other

Other13th InterSociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems, ITherm 2012
Country/TerritoryUnited States
CitySan Diego, CA
Period5/30/126/1/12

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Keywords

  • CFD
  • artificial neural network
  • data center
  • k-ε model
  • non-linear constrained minimization
  • optimal model constants
  • turbulence

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