  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.
