Heat Transfer Correlations in an Air-Water Fin-Tube Compact Heat Exchanger by Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Pacheco-Vega:2003:IMECE,
  author =       "Arturo Pacheco-Vega and Weihua Cai and Mihir Sen and 
                 K. T. Yang",
  title =        "Heat Transfer Correlations in an Air-Water Fin-Tube
                 Compact Heat Exchanger by Symbolic Regression",
  booktitle =    "ASME International Mechanical Engineering Congress and
                 Exposition (IMECE2003)",
  year =         "2003",
  volume =       "3",
  pages =        "23--28",
  address =      "Washington, DC, USA",
  month =        nov # " 15-21",
  publisher =    "ASME",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7918-3718-1",
  DOI =          "doi:10.1115/IMECE2003-41977",
  abstract =     "In the present study we propose the application of
                 evolutionary algorithms to find correlations that can
                 predict the performance of a compact heat exchanger.
                 Genetic programming (GP) is a search technique in which
                 computer codes, representing functions as parse trees,
                 evolve as the search proceeds. As a symbolic regression
                 approach, GP looks for both the functional form and the
                 coefficients that enable the closest fit to
                 experimental data. Two different data sets are used to
                 test the symbolic regression capability of genetic
                 programming, the first being artificial data from a
                 one-dimensional function, while the second are data
                 generated by previously determined correlations from
                 experimental measurements of a single-phase air-water
                 heat exchanger. The results demonstrate that the
                 correlations found by symbolic regression are able to
                 predict well the data from which they were determined,
                 and that the GP technique may be suitable for modelling
                 the nonlinear behaviour of heat exchangers. It is also
                 shown that there is not a unique answer for the
                 best-fit correlation from this procedure. The advantage
                 of using genetic programming as symbolic regression is
                 that no initial assumptions on the functional forms are
                 needed, which is contrary to the traditional
                 approach.",
  notes =        "University Autonoma de San Luis Potosi, San Luis
                 Potosi, Mexico",
}

Genetic Programming entries for Arturo Javier Pacheco Vega Weihua Cai Mihir Sen K T Yang

Citations