Prediction of Cooling Load for a Standing Wave Thermoacoustic Refrigerator through Artificial Neural Network Technique

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Abstract

This study involves the application of artificial neural network (ANN) as a new approach to predict cooling load of thermoacoustic refrigerator under some operating conditions. One ANN model for a standing wave thermoacoustic refrigerator had been developed based on the experimental data from other literature. Cooling load was chosen as a response to input parameters, mean pressure and frequency in ANN model. A multi-layer feed-forward network with a back propagation algorithm had been proposed for predicting cooling load of the thermoacoustic refrigerator. This proposed ANN model has three layers with the configuration of 2-10-1, namely, input layer with two neurons representing the two operating parameters, one hidden layer with an optimal 10 hidden neurons, and output layer with one neuron representing the cooling load. The ANN model had been proven to be desirable in accuracy for predicting the cooling load by comparing model results with experimental results in literature work. This research work would provide a new modelling approach based on ANN technique for solving complex thermoacoustic problems with linear or nonlinear nature.

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APA

Rahman, A. A., & Zhang, X. (2017). Prediction of Cooling Load for a Standing Wave Thermoacoustic Refrigerator through Artificial Neural Network Technique. In Energy Procedia (Vol. 142, pp. 3780–3786). Elsevier Ltd. https://doi.org/10.1016/j.egypro.2017.12.276

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