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The present work investigates the application of Artificial Neural Networks(ANNs) to estimate the Reynolds ($Re$) number for flows around a cylinder. Thedata required to train the ANN was generated with our own implementation of aLattice Boltzmann Method (LBM) code performing simulations of a 2-dimensionalflow around a cylinder. As results of the simulations, we obtain the velocityfield ($\vec{v}$) and the vorticity ($\vec{\nabla}\times\vec{v}$) of the fluidfor 120 different values of $Re$ measured at different distances from theobstacle and use them to teach the ANN to predict the $Re$. The resultspredicted by the networks show good accuracy with errors of less than $4\%$ inall the studied cases. One of the possible applications of this method is thedevelopment of an efficient tool to characterize a blocked flowing pipe.

Estimation of Reynolds number for flows around cylinders with lattice Boltzmann methods and artificial neural networks