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Neural network analysis of fin-tube refrigerating heat exchanger with limited experimental data.

Author(s) : PACHECO-VEGA A., SEN M., YANG K. T., et al.

Type of article: Article

Summary

The authors consider the problem of accuracy in heat rate estimations from artificial neural network models of heat exchangers used for refrigeration applications. Limited experimental measurements from a manufacturer are used to show the capability of the neural network technique in modeling the heat transfer phenomena in these systems. A well-trained network correlates the data with errors of the same order as the uncertainty of the measurements.

Details

  • Original title: Neural network analysis of fin-tube refrigerating heat exchanger with limited experimental data.
  • Record ID : 2002-0095
  • Languages: English
  • Source: International Journal of Heat and Mass Transfer - vol. 44 - n. 4
  • Publication date: 2001/02

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