A potent numerical model coupled with multi-objective NSGA-II algorithm for the optimal design of Stirling engine

Fawad Ahmed, Shunmin Zhu, Guoyao Yu and Ercang Luo

Energy, 2022, vol. 247, issue C

Abstract: In this article, a novel numerical model of the Stirling engine encompassing a potent loss mechanism coupled with the NSGA-II algorithm is proposed. Multi-objective optimization of GPU-3 Stirling engine was performed using a class of genetic algorithms, namely NSGA-II, with five decision variables to minimize the losses and increase the power output and efficiency of the GPU-3 engine. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Combinative Distance-based Assessment (CODAS) decision-making approaches were used to obtain the optimum solution from Pareto optimal space. Furthermore, the optimization results were compared with the experimental results of the GPU-3 Stirling engine. Results from the multi-objective optimization effort indicate that output power increases by approx. 500 W and efficiency enhances by approx. 5%, whereas losses decrease by 516 W. Later, to demonstrate the model's design capability, the developed model and optimization approach, i.e. (NSGA-II), is utilized to develop an optimal design of a beta-type free piston Stirling engine (FPSE) with an indicated power of 10 kW. After optimizing a combination of twelve operating and geometric parameters, the Stirling engine that yields a net power output of about 7.95 kW with a thermal efficiency of about 30% is developed. This work presents a novel and powerful numerical method for the optimal design of Stirling engine.

Keywords: Stirling engine; Modeling; Multi-objective optimization; NSGA-II; GPU-3; Design (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:247:y:2022:i:c:s0360544222003711

DOI: 10.1016/j.energy.2022.123468

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