A comprehensive review on modeling and performance optimization of Stirling engine

Abstract

Summary Modeling with optimization has become a ubiquitous practice in the field of Stirling engine. A plethora of studies in the literature is dedicated in developing a feasible optimized model that can precisely predict the performance of Stirling engine. Hence, the purpose of this article is to compile and expansively review the thermodynamic models and optimization efforts made in pursuit of performance enhancement of the Stirling engine. An extensive range of models available in the literature is painstakingly discussed. Likewise, a wide variety of available optimization techniques spanning from conventional experimental and univariate methods to more complex multiobjective optimization approach are critically reviewed. A comparative analysis of the models is carried out based on the accuracy of their predictability of the performance of Stirling engines. Results obtained from the models are validated through the experimental data of the GPU‐3 Stirling engine prototype. Several optimization techniques are investigated based on the effective and efficient optimization of operating and geometric parameters of the Stirling engine. The review concluded that the Comprehensive Polytropic Model of Stirling engine (CPMS) demonstrated better accuracy in comparison with other models. In addition, the multiobjective particle swarm optimization (MOPSO) technique was found to be effective and computationally efficient.

A review study on mathematical modeling of solar parabolic d ish‐Stirling system used for electricity generation

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The examination of performance characteristics of a beta‐type Stirling engine with a rhombic mechanism: The influence of various working fluids and displacer piston materials

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Thermodynamic optimization of Stirling heat engine with methane gas using finite speed thermodynamic model

“…Multi‐objective optimizations were performed considering the engine efficiency, output power, and power per unit investment cost as the objective functions. Recently, Ahmadi et al 31 and Ahmed et al 32 have comprehensively reviewed various analytical modeling and performance optimization techniques that were applied to assess the Stirling heat engine. The authors have concluded the polytropic FST model as the better model for the numerical analysis and the PSO algorithm as the better technique for the optimization.…”