Deep reinforcement learning and mesh deformation integration for shape optimization of a single pin fin within a micro channel

Abdolvahab Ravanji*, Ann Lee, Javad Mohammadpour, Shaokoon Cheng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
15 Downloads (Pure)

Abstract

Advancements in machine learning have fueled a growing trend towards automating design optimization in heat transfer applications, moving away from traditional, manually-intensive techniques. Rather than iterating the specific features of a given design, algorithms can be harnessed to create configurations that meet the required criteria and outcomes. The suggested framework in this study, incorporating Deep Reinforcement Learning and a Computational Fluid Dynamics (CFD) solver, utilizes the Radial Basis Function interpolation technique to eliminate the meshing step by immediately changing the mesh to reduce the time required for optimization significantly. This learning process utilizes Proximal Policy Optimization with Fluent, which functions as the CFD solver based on the Finite Volume Method. The agent effectively navigates the design space, optimizing the Thermohydraulic Performance Factor (TPF) while maximizing heat transfer and minimizing pressure loss. This work shows how a microchannel with a pin fin design, with 9 degrees of freedom, can be deformed into a new configuration using the Free Form Deformation method. 20,000 simulations are conducted in less than 20 hours, and the outcomes highlight the feasibility and effectiveness of the framework to enhance simulation time, and performance. Based on the results of this study, the proposed method can effectively improve heat transfer by 23 %-41 % and reduce pressure drop by 68 %-79 %; demonstrating that the framework can achieve a remarkable 52 %-98 % improvement in TPF compared to the initial circular pin.

Original languageEnglish
Article number126242
Pages (from-to)1-14
Number of pages14
JournalInternational Journal of Heat and Mass Transfer
Volume236
Issue numberPart 1
DOIs
Publication statusPublished - Jan 2025

Bibliographical note

Copyright the Author(s) 2024. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Optimization
  • Machine Learning
  • Reinforcement Learning
  • Channel cooling
  • Pin fins
  • Heat transfer
  • Pressure drop
  • and Thermohydraulic performance

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