Full Content is available to subscribers

Subscribe/Learn More  >

Multivariate Prediction of Airflow and Temperature Distributions Using Artificial Neural Networks

[+] Author Affiliations
Zhihang Song, Bruce T. Murray, Bahgat Sammakia

SUNY Binghamton, Binghamton, NY

Paper No. IPACK2011-52167, pp. 595-604; 10 pages
  • ASME 2011 Pacific Rim Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Systems
  • ASME 2011 Pacific Rim Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Systems, MEMS and NEMS: Volume 2
  • Portland, Oregon, USA, July 6–8, 2011
  • ISBN: 978-0-7918-4462-5
  • Copyright © 2011 by ASME


Thermal Management optimization for data centers, including prediction of airflow and temperature distributions, is generally an extremely time-consuming process using full-scale CFD analysis. Reduced order models are necessary in order to provide real-time assessment of cooling requirements for data centers. The use of a simulation-based Artificial Neural Network (ANN) is being investigated as a predictive tool. A model for a basic hot aisle/cold aisle data center configuration was built and analyzed using the commercial software FloTHERM. The flow field and temperature distributions were obtained for 100 representative sets of operating conditions using the CFD package. The Latin Hypercube Sampling technique was employed to select values for three design variables: plenum height, percentage open area of the perforated tiles and air leakage fraction. The FloTHERM results were used to generate a database for the ANN training. The CFD results from 85 cases were used for training and 16 cases were used for validation. A multivariate mapping between the input design variables and output variables (individual tile flow rates and maximum rack temperatures) was obtained. Good agreement (0.5% average relative error) was obtained between the ANN model predictions and the CFD results. These preliminary results are promising and show that an ANN based model may yield an effective real-time thermal management design tool for data centers.

Copyright © 2011 by ASME



Interactive Graphics


Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In