Artificial neural networks modelling of the performance parameters of the Stirling engine

The Stirling engine can theoretically be very efficient to convert heat into mechanical work at Carnot efficiency. Various parameters could affect the performance of the addressed Stirling engine which is considered in optimisation of the Stirling engine for designing purpose. Through addressed factors, torque has the highest effect on the robustness of the Stirling engines. Due to this fact, determination of the referred parameters with low uncertainty and high precision is needed. To solve the mentioned obstacle, throughout this paper, a generation of intelligent model called ‘artificial neural network’ (ANN) was implemented to estimate the torque of the Stirling heat engine. In addition, highly accurate actual values of the required parameters which were gained from open literature surveys from previous studies were implemented to develop a robust intelligent model. Based on the outcomes of the ANN approach, the output results of an ANN model were close to relevant actual values with a high degree of performance.

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