Stirling engine optimization using artificial neural networks algorithm

Abstract

Neural networks are a type of machine learning algorithm that are inspired by the structure and function of the human brain. They consist of layers of interconnected “neurons” which process and transmit information. Neural networks can learn to perform a variety of tasks by being trained on large datasets, and they have been successfully applied to a wide range of problems, including image and speech recognition, natural language processing (NLP), and predictive modeling. In this paper, we are combining Stirling engines with neural networks in order to improve the performance and efficiency of the engine by using machine learning to optimize their operation. For example, an optimizing neural network such as the Multi-Layer Perceptron (MLP) could be trained to predict the most efficient operating conditions for a Stirling engine based on design parameters such as displacer stroke, phase angle and working frequency. Additionally, neural networks could be used to diagnose and predict failures in Stirling engines, potentially improving their reliability and reducing maintenance costs.