Performance analysis and multi-objective optimization of a Stirling engine based on MOPSOCD

Abstract

A Stirling engine displays an aptitude for utilizing sustainable energy (such as solar energy) and exhibits the same theoretical efficiency as that of a Carnot cycle. However, the actual efficiency of a Stirling engine is far from the ideal Carnot efficiency due to irreversibilities. Models proposed in previous studies that focused on the imperfect regenerative process are crude and require improvements. In this study, finite time thermodynamics is employed to construct a refined model that considers the finite rate of heat transfer, conductive thermal bridging loss, and regenerative loss that is supplied by the heat source. Based on the model, three objective functions including power, efficiency, and ecological coefficient of performance (ECOP) of a Stirling engine are simultaneously optimized for maximization. A multi-objective optimization method based on a multi-objective particle swarm optimization algorithm using crowding distance (MOPSOCD) is employed to optimize the Stirling engine for the first time. Solutions obtained using the MOPSOCD comprise the Pareto set. The optimal solution is then selected using the technique for order of preference by similarity to ideal solution. The performance under the multi-objective optimization is compared with those of single-objective optimization methods in terms of power, efficiency, and ECOP. The results reveal that MOPSOCD exhibits good coordination in terms of the power, efficiency, and ECOP of the Stirling engine and may serve as a promising guide for operating and designing Stirling engines.