Computational methods for flow and mixing analysis in passive micromixers: A comprehensive review

Abstract

This review explores key computational approaches to study the flow dynamics and mixing in micromixers. Conventional computational fluid dynamics (CFD) approaches based on finite volume, finite element, and finite difference methods are discussed, together with commonly used advection–diffusion and multicomponent transport models for species concentration. Lagrangian particle tracking techniques are reviewed as complementary tools for mixing quantification, enabling the analysis of chaotic advection through particle distribution maps, Poincaré sections, and residence time distribution metrics. In addition, emerging computational strategies, including the lattice Boltzmann method and physics-informed neural networks, are examined in terms of their theoretical foundations, capabilities, and recent applications. Finally, key numerical challenges such as grid dependency, discretization errors, and false diffusion are critically analyzed, and best-practice guidelines for reliable micromixing simulations are summarized. This review aims to provide a unified computational perspective to support accurate performance prediction and efficient passive micromixer design.