Prediction of the oscillatory heat transfer coefficient in thermoacoustic refrigerators

Thermoacoustic refrigerators are emerging devices that make use of meaningful high-pressure sound waves to induce cooling. Despite the accelerated progress in the field of thermoacous-tics, knowledge of the heat transfer process in the heat exchange of the devices is still developing. This work applies different soft computing techniques, namely, an artificial neural network trained by particle swarm optimisation (ANN-PSO), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANNs) to predict the oscillatory heat transfer coefficient in the heat ex-changers of a thermoacoustic device. This study provides the details of the parametric analysis of an artificial neural network model trained by particle swarm optimisation. The solution model considers the number of neurons, the swarm population, and the acceleration factors to develop and analyse the architecture of several models. The regression model (R² ) and mean squared error (MSE) were used to evaluate the accuracy of the models. The result showed that the proposed soft computing techniques can potentially be used for the modelling and the analysis of the oscillatory heat transfer coefficient with a higher level of accuracy. The result reported in this study implies that the prediction of the OHTC can be considered for the enhancement of thermoacoustic refrigerators performances.