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Classification of Condensing Heat Exchangers Performance Data by Gaussian Mixtures

[+] Author Affiliations
Arturo Pacheco-Vega

California State University at Los Angeles, Los Angeles, CA

Gabriela Avila

Universidad Autónoma de San Luis Potosí, San Luis Potosí, SLP, México

Paper No. HT2009-88627, pp. 737-745; 9 pages
  • ASME 2009 Heat Transfer Summer Conference collocated with the InterPACK09 and 3rd Energy Sustainability Conferences
  • Volume 1: Heat Transfer in Energy Systems; Thermophysical Properties; Heat Transfer Equipment; Heat Transfer in Electronic Equipment
  • San Francisco, California, USA, July 19–23, 2009
  • Conference Sponsors: Heat Transfer Division
  • ISBN: 978-0-7918-4356-7 | eISBN: 978-0-7918-3851-8
  • Copyright © 2009 by ASME


We introduce a methodology to extract the regimes of operation from condensing heat exchanger data. The methodology uses a Gaussian mixture clustering algorithm to determine the number of groups from the data, and a maximum likelihood decision rule to classify the data into these clusters. In order to assess the accuracy of clustering technique, experimental data from the literature visually classified as dry-surface, dropwise condensation, and film condensation, are used in the analysis. Though there is a discrepancy between the clustering classification and the visual one, an independent evaluation using artificial neural networks (ANNs) shows that the clustering methodology is able to both find the different regimes of operation and classify the data corresponding to each regime.

Copyright © 2009 by ASME
Topics: Heat exchangers



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