Four-Objective Optimization of an Irreversible Stirling Heat Engine with Linear Phenomenological Heat-Transfer Law

This paper combines the mechanical efficiency theory and finite time thermodynamic theory to perform optimization on an irreversible Stirling heat-engine cycle, in which heat transfer between working fluid and heat reservoir obeys linear phenomenological heat-transfer law. There are mechanical losses, as well as heat leakage, thermal resistance, and regeneration loss. We treated temperature ratio [Formula: see text] of working fluid and volume compression ratio [Formula: see text] as optimization variables, and used the NSGA-II algorithm to carry out multi-objective optimization on four optimization objectives, namely, dimensionless shaft power output [Formula: see text] , braking thermal efficiency [Formula: see text] , dimensionless efficient power [Formula: see text] and dimensionless power density [Formula: see text]. The optimal solutions of four-, three-, two-, and single-objective optimizations are reached by selecting the minimum deviation indexes [Formula: see text] with the three decision-making strategies, namely, TOPSIS, LINMAP, and Shannon Entropy. The optimization results show that the [Formula: see text] reached by TOPSIS and LINMAP strategies are both 0.1683 and better than the Shannon Entropy strategy for four-objective optimization, while the [Formula: see text] s reached for single-objective optimizations at maximum [Formula: see text] , [Formula: see text] , [Formula: see text] , and [Formula: see text] conditions are 0.1978, 0.8624, 0.3319, and 0.3032, which are all bigger than 0.1683. This indicates that multi-objective optimization results are better when choosing appropriate decision-making strategies.