Prediction of oscillatory heat transfer coefficient for a thermoacoustic heat exchanger through artificial neural network technique

Heat exchangers under oscillatory flow condition in thermoacoustic devices are quite different with the traditional ones in heat transfer and flow behavior of thermo-viscous fluid. As a result, one cannot directly apply the heat transfer correlations for the steady flow to design thermoacoustic heat exchangers, otherwise, significant deviation will arise. However, some correlations of heat transfer for the oscillatory flow have not been well established yet. This study involves the application of artificial neural network (ANN) as a new approach to predict oscillatory heat transfer coefficient of one thermoacoustic heat exchanger under some operating conditions. One ANN model for the oscillatory heat exchanger used in one standing wave thermoacoustic refrigerator has been developed based on the published experimental data. This proposed ANN model has three layers with the configuration of 2-10-1, namely one input layer with two neurons representing two operating parameters, oscillating frequency and mean pressure, one hidden layer with optimal ten hidden neurons and one output layer with one neuron representing the oscillatory heat transfer coefficient as response. Moreover, a statistical analysis has been provided for studying the influence strength of these two input parameters on the oscillatory heat transfer coefficient. This ANN model had been proven to be desirable in accuracy for predicting oscillatory heat transfer coefficient by comparing ANN model results with both experimental results and calculated results by several other correlations from the published literature at the same operating conditions. This research work provides a new and accurate modeling approach based on ANN technique for the research of thermoacoustic heat exchangers and solving heat transfer problems related with oscillatory flow condition.