MICROMIXERS OPTIMIZATION WITH GAUSSIAN PROCESS AND UNCERTAINTY QUANTIFICATION

Microfluidic devices are gaining attention for their small size. This study introduces an optimization approach in microfluidics, combining Computational Fluid Dynamics (CFD) with Machine Learning (ML). It analyses a Y-type micromixer, initially featuring cylindrical grooves on the main channel’s surface and internal obstructions. Simulations using OpenFOAM 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) 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.