Abstract
This study investigates the vibrations of graphene oxide powders (GOPs) reinforced perovskite solar cells surrounded by an elastic foundation using both mathematical modeling and innovative machine learning algorithms. The incorporation of GOPs into the perovskite matrix enhances the mechanical properties and stability of the solar cells, which are crucial for their durability and efficiency. The analysis is conducted through the application of Hamilton’s principle, providing a robust theoretical framework for deriving the governing equations of motion. An analytical method is employed to solve these equations, allowing for the accurate prediction of the vibrational behavior of the reinforced solar cells. The effects of various parameters, including the stiffness of the elastic foundation and the concentration of GOPs, are systematically examined. This study presents the application of an innovative Support Vector Machine (SVM)-Particle Swarm Optimization (PSO)-Genetic Algorithm (GA) to analyze the vibrations of GOPs reinforced perovskite solar cells surrounded by an elastic foundation using mathematical modeling datasets. The SVM-PSO-GA algorithm to enhance predictive accuracy. The integrated approach leverages the strengths of each method to model and predict the vibrational behavior of the reinforced solar cells. The results highlight the algorithm’s effectiveness in capturing complex interactions and optimizing design parameters, providing valuable insights for improving the stability and performance of perovskite solar cells in practical applications.
Published Version
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