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<p>The design optimization of passive micromixers for efficient fluid homogenization at low Reynolds numbers remains a multifaceted challenge, balancing geometric complexity, mixing performance, and manufacturing constraints. This work introduces a novel T-shaped passive micromixer incorporating a series of four concave-star units to induce chaotic advection. A comprehensive parametric study is conducted via two-dimensional (2D) finite element analysis (FEA) under the shallow-channel approximation, systematically varying the star tip radius (<i>R</i>), wall thickness (<i>t</i>), and concave angle (<i>θ</i>) across 9261 design points to establish a high-fidelity dataset. The results demonstrate that the concave-star geometry effectively disrupts laminar flow through flow splitting, acceleration, and secondary vortex generation, achieving a mixing index exceeding 0.99 at the outlet for optimal configurations. Furthermore, an integrated FEA and machine learning (FEA-ML) framework is proposed to rapidly predict performance. The Prior-Data Fitted Network (TabPFN) model achieves exceptional prediction accuracy (<i>R</i><sup>2</sup> &gt; 0.9998), significantly outperforming conventional algorithms like Random Forest and XGBoost. This study provides not only an effective micromixer design but also a robust data-driven framework that accelerates the exploration and optimization of complex microfluidic geometries.</p>

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Intelligent micromixer design via a synergistic framework: concave-star geometry exploration with prior-data-fitted neural networks

  • 期刊论文

  • 发表日期：

    2026年6月16日

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  • Yijie Liu

摘要

The design optimization of passive micromixers for efficient fluid homogenization at low Reynolds numbers remains a multifaceted challenge, balancing geometric complexity, mixing performance, and manufacturing constraints. This work introduces a novel T-shaped passive micromixer incorporating a series of four concave-star units to induce chaotic advection. A comprehensive parametric study is conducted via two-dimensional (2D) finite element analysis (FEA) under the shallow-channel approximation, systematically varying the star tip radius (R), wall thickness (t), and concave angle (θ) across 9261 design points to establish a high-fidelity dataset. The results demonstrate that the concave-star geometry effectively disrupts laminar flow through flow splitting, acceleration, and secondary vortex generation, achieving a mixing index exceeding 0.99 at the outlet for optimal configurations. Furthermore, an integrated FEA and machine learning (FEA-ML) framework is proposed to rapidly predict performance. The Prior-Data Fitted Network (TabPFN) model achieves exceptional prediction accuracy (R2 > 0.9998), significantly outperforming conventional algorithms like Random Forest and XGBoost. This study provides not only an effective micromixer design but also a robust data-driven framework that accelerates the exploration and optimization of complex microfluidic geometries.