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An approximate integral model with an artificial neural network for heat exchangers

✍ Scribed by Guo-Liang Ding; Chun-Lu Zhang; Tao Zhan

Publisher
John Wiley and Sons
Year
2004
Tongue
English
Weight
388 KB
Volume
33
Category
Article
ISSN
1099-2871

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✦ Synopsis

Abstract

In order to improve predicting precision and increase computation speed of simulation for heat exchangers, a novel method is presented in this paper, whereby an approximate integral model is used to simplify the original distributed parameter model and an artificial neural network is combined to reflect the nonlinear relations. This model is applied in actual calculations of fin‐and‐tube condensers and high precision is achieved. Where the calculated outlet temperature of refrigerant and that of air, the average errors are both less than 0.2 Β°C. For the heat exchange of the condenser, the average error is less than 1 0.2 Β°C. For the heat exchange of the condenser, the average error is less than 1%. The calculation speed of the approximate integral model is two orders of magnitude faster than that of the distributed‐parameter model. Β© 2004 Wiley Periodicals, Inc. Heat Trans Asian Res, 33(3): 153–160, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/htj.20006

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