PERFORMANCE ANALYSIS OF A TRAVELLING-WAVE THERMO-ACOUSTIC ENGINE USING ARTIFICIAL NEURAL NETWORK

Thermo-acoustic systems can convert thermal energy into acoustic waves and vice- versa. These acoustic waves can be used to induce cooling (thermo-acoustic refrigeration) or generate electricity (thermo-acoustic generator). This conversion is due to the thermo-viscous interaction between the acoustically oscillating gas medium within a porous material, referred to as a regenerator, and the pore internal walls. Although there has been significant progress in the development of efficient thermo-acoustic systems, their relatively low efficiency and the nonlinearity associated with more severe working conditions remain their major issues. Therefore, it is a major potential area of research. In this study, a one-stage travelling-wave thermo-acoustic engine has been modelled using DeltaEC. The simulation was performed by considering various input heat to the hot heat exchanger within the range of 8.2 to 227.91W, and sixty (60) datasets were generated. These data were used to build an Artificial Neural Network (ANN) model. The comparison between the output data extracted from the DeltaEC simulation and the results predicted from the ANN model was done. Both of the results obtained are in good agreement and prove that the ANN can be suitable for predicting configurations that were not previously simulated.