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Structure optimization of intercooler bionic fins based on artificial neural network and genetic algorithms

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  • Yao, Jin
  • Zhang, Zijin
  • Saari, Jussi
  • Wang, Jin
  • Čuček, Lidija
  • Zheng, Dan

Abstract

The bionic fins inspired by the shark are proposed to enhance the thermal-hydraulic performance of the intercoolers in this work. The geometric parameters of the fins are investigated to examine the effects on thermal-hydraulic performance. The height and width of protrusions are chosen as optimization parameters based on the results of the geometric parameter analysis. Non-dominated genetic algorithms are combined with artificial neural networks to obtain the optimal solution for the geometry parameters. The artificial neural networks used in this study exhibit a reliable accuracy with errors below 10 % (with mostly remaining below 5 %). The optimal values for the height and width of the protrusions are determined to be 1.01 mm and 0.82 mm, which results in a Colburn factor of 0.01923 and a comprehensive index of 0.04305. Compared with the original design, the optimized structure yields 17.69 % and 4.77 % increments for the Colburn factor and comprehensive index. Compared with the original model, the intercooler with the optimal structure exhibits a 9.9 % increase in entropy generation and a 20.2 % enhancement in the exergy difference between the inlet and outlet.

Suggested Citation

  • Yao, Jin & Zhang, Zijin & Saari, Jussi & Wang, Jin & Čuček, Lidija & Zheng, Dan, 2024. "Structure optimization of intercooler bionic fins based on artificial neural network and genetic algorithms," Energy, Elsevier, vol. 307(C).
    • Handle: RePEc:eee:energy:v:307:y:2024:i:c:s0360544224023892
    DOI: 10.1016/j.energy.2024.132615
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    References listed on IDEAS

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