Predictive Model and Optimization of Micromixers Geometry using Gaussian Process with Uncertainty Quantification and Genetic Algorithm

TL;DR

This paper introduces a novel optimization method combining CFD, Gaussian Process modeling with uncertainty quantification, and genetic algorithms to enhance micromixer design, achieving efficient mixing with reduced computational costs.

Contribution

It presents a new integrated approach using GP and GA for optimizing micromixer geometries with uncertainty quantification, improving over previous methods.

Findings

Medium-sized obstructions optimize mixing performance.

The approach reduces computational expenses compared to traditional methods.

Results confirm the effectiveness of combining CFD, ML, and optimization techniques.

Abstract

Microfluidic devices are gaining attention for their small size and ability to handle tiny fluid volumes. Mixing fluids efficiently at this scale, known as micromixing, is crucial. This article builds upon previous research by introducing a novel optimization approach in microfluidics, combining Computational Fluid Dynamics (CFD) with Machine Learning (ML) techniques. The research focuses on improving global optimization while reducing computational expenses. It draws inspiration from a Y-type micromixer, initially featuring cylindrical grooves on the main channel's surface and internal obstructions. Simulations, conducted using OpenFOAM software, evaluate the impact of circular obstructions on mixing percentage and pressure drop, considering variations in obstruction diameter and offset. A Gaussian Process (GP) was utilized to model the data, providing model uncertainty. Thus, this study optimizes geometries by using genetic algorithm (GA) and least-square optimization based on the reduced order model provided by GP. Results align with previous research, showing that medium-sized obstructions (137 mm diameter, 10 mm offset) near the channel wall are optimal. This approach not only provides efficient microfluidic optimization with uncertainty quantification but also highlights the effectiveness of combining CFD and ML techniques in achieving desired outcomes.