Estimation of Reynolds number for flows around cylinders with lattice Boltzmann methods and artificial neural networks
The present work investigates the application of Artificial Neural Networks (ANNs) to estimate the Reynolds ($Re$) number for flows around a cylinder. The data required to train the ANN was generated with our own implementation of a Lattice Boltzmann Method (LBM) code performing simulations of a 2-dimensional flow around a cylinder. As results of the simulations, we obtain the velocity field ($\vec{v}$) and the vorticity ($\vec{\nabla}\times\vec{v}$) of the fluid for 120 different values of $Re$ measured at different distances from the obstacle and use them to teach the ANN to predict the $Re$. The results predicted by the networks show good accuracy with errors of less than $4\%$ in all the studied cases. One of the possible applications of this method is the development of an efficient tool to characterize a blocked flowing pipe.