Thermo-acoustic systems can convert thermal energy into acoustic waves and vice-versa. This conversion is due to the thermo-viscous interaction between the acoustically oscillating gas fluid within a porous medium, referred to as a regenerator, and the pore internal walls. The thermo-acoustic approach is proposed in this study as an alternative sustainable solution for addressing the issue of electricity in remote areas of developing countries. This approach is environmentally friendly as it utilises air as the working medium and therefore does not generate harmful emissions. In this study, a two-stage travelling-wave thermo-acoustic engine has been modelled using DeltaEC. The simulation was performed by considering various input heat for both of the engine stages. The heat input for the first stage was set within the range of 359.48 to 455.75W, while in the second stage was within the range of 1307.99 to 1656.35W. Hundred (100) data were generated. This dataset was used to build an Artificial Neural Network (ANN) model. The ANN model was validated using the data extracted from DeltaEC. A good agreement between DeltaEC simulation results and ANN predictions was observed. This study shows that the ANN approach is capable of analysing intricate nonlinear thermoacoustic issues.
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.
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