Neural networks analysis of thermal characteristics on plate-fin heat exchangers with limited experimental data

作者：Peng Hao; Ling Xiang^*

来源：Applied Thermal Engineering, 2009, 29(11-12): 2251-2256.

DOI：10.1016/j.applthermaleng.2008.11.011

• Plate-fin heat exchanger
• Fin
• Artificial neural network
• Back propagation algorithm
• Colburn factor
• Friction factor

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.

• 出版日期2009-8
• 单位南京工业大学