کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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648539 | 1457200 | 2009 | 6 صفحه PDF | دانلود رایگان |
In this paper, an application of artificial neural networks (ANNs) was presented to predict the pressure drop and heat transfer characteristics in the plate-fin heat exchangers (PFHEs). First, the thermal performances of five different PFHEs were evaluated experimentally. The Colburn factor j and friction factor f to different type fins were obtained under various experimental conditions. Then, a feed-forward neural network based on back propagation algorithm was developed to model the thermal performance of the PFHEs. The ANNs was trained using the experimental data to predict j and f factors in PFHEs. Different network configurations were also examined for searching a better network for prediction. The predicted values were found to be in good agreement with the actual values from the experiments with mean squared errors (MSE) less than 1.5% for j factor and 1% for f factor, respectively. This demonstrated that the neural network presented can help the engineers and manufacturers predict the thermal characteristics of new type fins in PFHEs under various operating conditions.
Journal: Applied Thermal Engineering - Volume 29, Issues 11–12, August 2009, Pages 2251–2256