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

## TL;DR

This paper demonstrates that artificial neural networks trained on flow data from lattice Boltzmann simulations can accurately estimate the Reynolds number around a cylinder, offering a potential tool for flow characterization.

## Contribution

It introduces a novel approach combining Lattice Boltzmann simulations and neural networks to estimate Reynolds numbers in fluid flows around cylinders.

## Key findings

- Neural networks achieved less than 4% error in Reynolds number prediction.
- The method effectively uses velocity and vorticity fields from simulations.
- Potential application in flow diagnostics for blocked pipes.

## Abstract

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.