Computational Fluid Dynamics and Machine Learning as tools for Optimization of Micromixers geometry
This is an incremental improvement for microfluidics researchers, enabling more efficient design optimization with lower computational expense.
This work tackled the optimization of micromixer geometry by combining Computational Fluid Dynamics (CFD) and Machine Learning to reduce computational costs, achieving an optimal configuration with an obstruction diameter of 131 mm and offset of 10 mm that maximizes mixing percentage and minimizes pressure drop.
This work explores a new approach for optimization in the field of microfluidics, using the combination of CFD (Computational Fluid Dynamics), and Machine Learning techniques. The objective of this combination is to enable global optimization with lower computational cost. The initial geometry is inspired in a Y-type micromixer with cylindrical grooves on the surface of the main channel and obstructions inside it. Simulations for circular obstructions were carried out using the OpenFOAM software to observe the influences of obstacles. The effects of obstruction diameter (OD), and offset (OF) in the range of [20,140] mm and [10,160] mm, respectively, on percentage of mixing ($\varphi$), pressure drop ($ΔP$) and energy cost ($ΔP/\varphi$) were investigated. Numerical experiments were analyzed using machine learning. Firstly, a neural network was used to train the dataset composed by the inputs OD and OF and outputs $\varphi$ and $ΔP$. The objective functions (ObF) chosen to numerically optimize the performance of micromixers with grooves and obstructions were $\varphi$, $ΔP$, $ΔP/\varphi$. The genetic algorithm obtained the geometry that offers the maximum value of $\varphi$ and the minimum value of $ΔP_s$. The results show that $\varphi$ increases monotonically with increasing OD at all values of OF. The inverse is observed with increasing offset. Furthermore, the results reveal that $ΔP$ e $ΔP/\varphi$ also increase with OD. On the other hand, the pressure drop and the cost of mixing energy present a maximum close to the lowest values of OF. Finally, the optimal value obtained for the diameter was OD=131 mm and for the offset OF=10 mm, which corresponds to obstruction of medium size close to the channel wall.