Numerical Analysis and Moth Flame Optimization of Passive T-Micromixer with Twist and Bend mixing channel

Abstract

In this study, we investigate a novel micromixer for numerical analysis with evolutionary algorithm optimization, using Computational Fluid Dynamics (CFD) and Machine Learning (ML) tools while maintaining relatively low computational costs. The initial design is motivated by a simple-T micromixer using different twists and bends. Ansys fluent with finite volume analysis, conducts a simulation for this parameter. We investigate the impact of twist and bend on mixing performance and pressure drops. For data analysis, we consider the twist, bend angle, and Reynold number as input variables and mixing index and pressure drop as outputs variables for training using a neural network. With the moth flame optimization algorithm, the micromixer model was optimized for the maximum mixing performance and the minimum pressure drop. The optimal value for the twist and bend angle is 4 and 700, respectively, at Reynold number (Re) 10. This technique has a significant benefit for microfluidic channel optimization.