Shape optimization of pin fin array in a cooling channel using genetic algorithm and machine learning

Nam Phuong Nguyen, Elham Maghsoudi, Scott N. Roberts, Beomjin Kwon

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

This article reports the optimization of pin fin shape using a genetic algorithm (GA) coupled either to a machine learning (ML) model or a computational fluid dynamics (CFD) model. The ML model evaluates the temperature and pressure induced by the fins within a second and allows us to replace the time-consuming CFD simulations during the design stage. The optimization is conducted for a cooling channel with a uniform heat flux boundary condition (5 W/cm2) in the Reynolds numbers range of 3000 – 12000. The optimization identifies a funnel-shaped fin that enhances the heat transfer coefficient by 20% without an apparent increase of pressure drop as compared to the standard cylindrical pin fins. The funnel-shaped fin outperforms other conventional fins of elliptical, cubic, and drop shapes that induce a similar level of pressure drops. This work demonstrates the potential of ML-based optimization in searching unexplored shapes of heat transfer systems with superior performance.

Original languageEnglish (US)
Article number123769
JournalInternational Journal of Heat and Mass Transfer
Volume202
DOIs
StatePublished - Mar 2023

Keywords

  • Genetic algorithm
  • Machine learning model
  • Pin fin
  • Shape optimization

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Mechanical Engineering
  • Fluid Flow and Transfer Processes

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