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

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  • Ahmed, Fawad
  • Zhu, Shunmin
  • Yu, Guoyao
  • Luo, Ercang

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.

Suggested Citation

  • Ahmed, Fawad & Zhu, Shunmin & Yu, Guoyao & Luo, Ercang, 2022. "A potent numerical model coupled with multi-objective NSGA-II algorithm for the optimal design of Stirling engine," Energy, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:energy:v:247:y:2022:i:c:s0360544222003711
    DOI: 10.1016/j.energy.2022.123468
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    References listed on IDEAS

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    Cited by:

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    3. Wang, Huaiyu & Ji, Changwei & Yang, Jinxin & Wang, Shuofeng & Ge, Yunshan, 2022. "Towards a comprehensive optimization of the intake characteristics for side ported Wankel rotary engines by coupling machine learning with genetic algorithm," Energy, Elsevier, vol. 261(PB).

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