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arXiv · 1812.04569

Recognition of an obstacle in a flow using artificial neural networks

Mauricio Carrillo·Ulices Que·José A. González·Carlos López
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Abstract

In this work a series of artificial neural networks (ANNs) have been developed with the capacity to estimate an obstacle's size and location obstructing the flow in a pipe. The ANNs learn the size and location of the obstacle by reading the profiles of the dynamic pressure $q$ or the $x$-component of the velocity $v_x$ of the fluid at certain distance from the obstacle. The data to train the ANN, was generated using numerical simulations with a 2D Lattice Boltzmann code. We analyzed various cases varying both the diameter and position of the obstacle on $y$-axis, obtaining good estimations using the $R^2$ coefficient for the cases of study. Although the ANN showed problems for the classification of the very small obstacles, the general results show a very good capacity of prediction.

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Mauricio Carrillo, Ulices Que, José A. González, Carlos López. 2018-12-11. Recognition of an obstacle in a flow using artificial neural networks. https://doi.org/10.1103/physreve.96.023306

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