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<p>This study introduces a Flexible Physics-Informed Neural Network (FlexPINN) to overcome the limitations of standard PINNs in modeling fully three-dimensional, geometrically complex micromixers with internal baffles. The mesh-free framework integrates parallel-network architecture, first-order dimensionless governing equations, adaptive loss weighting, and a novel global conservation penalty to prevent trivial solutions and ensure robust convergence. Employing transfer learning reduces the training time for new baffle shapes by ~ 35%, requiring approximately 3.5&#xa0;h versus 5.5&#xa0;h for a base case on a single GPU. Validated against conventional Computational Fluid Dynamics, FlexPINN achieves high-fidelity predictions, with maximum relative errors of 3.25% for the pressure drop coefficient and 2.86% for the mixing index. A comprehensive parametric study evaluates three baffle shapes (rectangular, elliptical, triangular) across four configurations and Reynolds numbers (Re = 5, 20, 40, 80). Results demonstrate that rectangular baffles in a double-unit, staggered configuration (C) at Re = 40 yield the peak mixing efficiency of 1.63, significantly outperforming other designs. This work successfully bridges a critical gap in PINN applications by providing a validated, efficient tool for the analysis and optimization of intricate 3D passive micromixers.</p>

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Performance analysis of a three-dimensional micromixer with baffles using a flexible physics-informed neural network

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月21日

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  • Meraj Hassanzadeh,
  • Ehsan Ghaderi,
  • Mohamad Ali Bijarchi

摘要

This study introduces a Flexible Physics-Informed Neural Network (FlexPINN) to overcome the limitations of standard PINNs in modeling fully three-dimensional, geometrically complex micromixers with internal baffles. The mesh-free framework integrates parallel-network architecture, first-order dimensionless governing equations, adaptive loss weighting, and a novel global conservation penalty to prevent trivial solutions and ensure robust convergence. Employing transfer learning reduces the training time for new baffle shapes by ~ 35%, requiring approximately 3.5 h versus 5.5 h for a base case on a single GPU. Validated against conventional Computational Fluid Dynamics, FlexPINN achieves high-fidelity predictions, with maximum relative errors of 3.25% for the pressure drop coefficient and 2.86% for the mixing index. A comprehensive parametric study evaluates three baffle shapes (rectangular, elliptical, triangular) across four configurations and Reynolds numbers (Re = 5, 20, 40, 80). Results demonstrate that rectangular baffles in a double-unit, staggered configuration (C) at Re = 40 yield the peak mixing efficiency of 1.63, significantly outperforming other designs. This work successfully bridges a critical gap in PINN applications by providing a validated, efficient tool for the analysis and optimization of intricate 3D passive micromixers.