Performance optimization of solar-powered heat engine using a multiobjective accelerated algorithm

In this study, a multiobjective optimization approach was used to conduct a thermodynamic investigation of a solar Brayton and endoreversible heat engine. The thermo-economic performance capabilities of such machines with hybrid input power, solar-fuel, are examined numerically. Throughout this study, three performance indicators of the cycle, including the power output, the thermo-economic performance function, and the thermal efficiency are optimized concurrently employing a multiobjective steepest descent method, named the Accelerated Diagonal Steepest Descent algorithm. Furthermore, to properly analyze the error, three strategies are employed in the decision-making step to identify the optimal compromise solution, and the deviation indices under these strategies are analyzed. The numerical experiments reveal that the present algorithm outperforms the two popular multiobjective algorithms: the multiobjective particle swarm optimization method and the elitist nondominated sorting genetic algorithm. The relevance of the presented algorithm with respect to the previous ones is examined by means of a deviation index. Finally, these experiments show the optimal design parameters which lead to the best performance of the heat engine.