  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 ($\Delta P$) and energy cost ($\Delta P/\varphi$)
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 $\Delta P$. The objective functions (ObF)
chosen to numerically optimize the performance of micromixers with grooves and
obstructions were $\varphi$, $\Delta P$, $\Delta P/\varphi$. The genetic
algorithm obtained the geometry that offers the maximum value of $\varphi$ and
the minimum value of $\Delta P_s$. 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 $\Delta P$ e
$\Delta P/\varphi$ 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.
