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Abstract: An accuracy self-adaptive model for fin-in-tube heat
exchangers is established, in which two artificial neural networks are
combined with a simplified traditional mathematical model. One of the
neural networks is used to compensate the difference between the
distributed-parameter model and the simplified one, the other is used to
improve the model accuracy by adaptively learning from experimental
data. The model is used for predicting fin-in-tube heat exchangers and
compared with experimental results. For condensers, it is shown that the
average and maximum deviations of heat flow rate are 0.63% and 1.72%
respectively, while the average and maximum deviations of subcooling are
0.9℃ and 3.2 ℃ respectively. For evaporators, the average and maximum
deviations of heat flow rate are 1.56% and 11.0% respectively, while the
average and maximum deviations of superheat is 1.5 ℃ and 9.8 ℃
respectively. For condensers and evaporators, the computational speed
with the new model is about two orders of magnitude faster than that
with the distributed-parameter model.
Key words: Heat exchanger Model Artificial neural network
CLC No: TB65
国家重点基础研究发展规划(973)资助项目(G2000026309). Received 20020118, received in
revised form 20020810
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