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

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

Energy, 2024, vol. 307, issue C

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.

Keywords: Bionic fins; Artificial neural network; Non-dominated genetic algorithms; Intercooler; Thermal-hydraulic performance (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224023892
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:307:y:2024:i:c:s0360544224023892

DOI: 10.1016/j.energy.2024.132615

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().