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Multi-objective optimization of elliptical tube fin heat exchangers based on neural networks and genetic algorithm

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  • Zhang, Tianyi
  • Chen, Lei
  • Wang, Jin

Abstract

The application of machine learning based on neural networks (NNs) and genetic algorithm (GA) in multi-objective optimization of heat exchangers is studied. Taking the tube fin heat exchanger (TFHE) as the research object, the inlet air velocity and the ellipticity of tubes are taken as the optimization variables. In order to obtain the optimal heat transfer performance and pressure drop performance, Computational Fluid Dynamics (CFD) simulation is carried out for different Reynolds based on the hydraulic diameter numbers (150–750) and tube ellipticity (0.2–1). Then use simulation data to train the Back-Propagation neural networks and establish the prediction model of heat transfer coefficient and pressure drop. The non-dominated multi-objective genetic algorithm with elitist retention strategy (NSGA-II) is used to optimize two prediction results of NNs. Finally, the optimal heat transfer coefficient and pressure drop are given in the form of Pareto front. The optimization results show that when the Reynolds number is 541 and the ellipticity is 0.34, the pressure drop of the TFHE decreases 20%, and the heat transfer coefficient is basically unchanged, whose j/f is 1.28 times as much as that of the original heat exchanger.

Suggested Citation

  • Zhang, Tianyi & Chen, Lei & Wang, Jin, 2023. "Multi-objective optimization of elliptical tube fin heat exchangers based on neural networks and genetic algorithm," Energy, Elsevier, vol. 269(C).
  • Handle: RePEc:eee:energy:v:269:y:2023:i:c:s0360544223001238
    DOI: 10.1016/j.energy.2023.126729
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    References listed on IDEAS

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    1. José Luis de Andrés Honrubia & José Gaviria de la Puerta & Fernando Cortés & Urko Aguirre-Larracoechea & Aitor Goti & Jone Retolaza, 2021. "Development and Application of a Multi-Objective Tool for Thermal Design of Heat Exchangers Using Neural Networks," Mathematics, MDPI, vol. 9(10), pages 1-23, May.
    2. Zheng, Dan & Du, Jianqiang & Wang, Wei & Klemeš, Jiří Jaromír & Wang, Jin & Sundén, Bengt, 2022. "Analysis of thermal efficiency of a corrugated double-tube heat exchanger with nanofluids," Energy, Elsevier, vol. 256(C).
    3. Wang, Jin & Yu, Kai & Ye, Mingzheng & Wang, Enyu & Wang, Wei & Sundén, Bengt, 2022. "Effects of pin fins and vortex generators on thermal performance in a microchannel with Al2O3 nanofluids," Energy, Elsevier, vol. 239(PE).
    4. Chen, Zhanxiu & Zheng, Dan & Wang, Jin & Chen, Lei & Sundén, Bengt, 2020. "Experimental investigation on heat transfer characteristics of various nanofluids in an indoor electric heater," Renewable Energy, Elsevier, vol. 147(P1), pages 1011-1018.
    5. Yue Sun & Xinting Wang & Rui Long & Fang Yuan & Kun Yang, 2019. "Numerical Investigation and Optimization on Shell Side Performance of A Shell and Tube Heat Exchanger with Inclined Trefoil-Hole Baffles," Energies, MDPI, vol. 12(21), pages 1-23, October.
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    Cited by:

    1. Nijie Jing & Yudong Xia & Qiang Ding & Yuezeng Chen & Zhiqiang Wang & Xuejun Zhang, 2023. "Simulation and Optimization Study on the Performance of Fin-and-Tube Heat Exchanger," Sustainability, MDPI, vol. 15(15), pages 1-15, July.
    2. Wang, Pengfei & Zhu, Ze & Liang, Wenlong & Liao, Longtao & Wan, Jiashuang, 2023. "Hybrid mechanistic and neural network modeling of nuclear reactors," Energy, Elsevier, vol. 282(C).
    3. Močnik, Urban & Čikić, Ante & Muhič, Simon, 2024. "Numerical and experimental analysis of fluid flow and flow visualization at low Reynolds numbers in a dimple pattern plate heat exchanger," Energy, Elsevier, vol. 288(C).

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