Scholarlink

<p>This study uses an approach for optimizing 3D pin fin geometry within microchannels, utilizing Deep Reinforcement Learning (<i>DRL</i>) in combination with Radial Basis Function (<i>RBF</i>)-based mesh deformation. The performance of microchannels is significantly enhanced by optimizing the pin fin geometry to maximize the Thermohydraulic Performance Factor (<i>TPF</i>). The <i>DRL</i> algorithm autonomously explores and optimizes pin fin designs by interacting with its environment based on Computational Fluid Dynamics (<i>CFD</i>) simulation. To enhance computational efficiency, an <i>RBF</i>-based mesh deformation technique is employed, enabling dynamic modification of the computational grid without the need for time-intensive remeshing. This approach significantly reduces simulation time while maintaining accuracy. Furthermore, simulations are accelerated by utilizing <i>GPU</i> computing, allowing for faster iterations and more comprehensive analyses. Two distinct cases, defined by different control point configurations, are studied to evaluate the impact of the number of degrees of freedom on the optimization outcomes. The results demonstrate that the <i>DRL</i> agent achieves notable improvements in the <i>TPF</i>, with enhancements of approximately 35% and 40% for the two cases, respectively. Notably, the case with a higher number of control points, which offers greater design flexibility, yields a higher <i>TPF</i>, highlighting the importance of design freedom in achieving optimal thermal performance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

3D pin fin optimization in microchannels using deep reinforcement learning and RBF-based mesh deformation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月26日

查看全文
  • Abdolvahab Ravanji,
  • Ann Lee,
  • Javad Mohammadpour,
  • Shaokoon Cheng

摘要

This study uses an approach for optimizing 3D pin fin geometry within microchannels, utilizing Deep Reinforcement Learning (DRL) in combination with Radial Basis Function (RBF)-based mesh deformation. The performance of microchannels is significantly enhanced by optimizing the pin fin geometry to maximize the Thermohydraulic Performance Factor (TPF). The DRL algorithm autonomously explores and optimizes pin fin designs by interacting with its environment based on Computational Fluid Dynamics (CFD) simulation. To enhance computational efficiency, an RBF-based mesh deformation technique is employed, enabling dynamic modification of the computational grid without the need for time-intensive remeshing. This approach significantly reduces simulation time while maintaining accuracy. Furthermore, simulations are accelerated by utilizing GPU computing, allowing for faster iterations and more comprehensive analyses. Two distinct cases, defined by different control point configurations, are studied to evaluate the impact of the number of degrees of freedom on the optimization outcomes. The results demonstrate that the DRL agent achieves notable improvements in the TPF, with enhancements of approximately 35% and 40% for the two cases, respectively. Notably, the case with a higher number of control points, which offers greater design flexibility, yields a higher TPF, highlighting the importance of design freedom in achieving optimal thermal performance.