Estimation of the Reynolds number in a Poiseuille flow using artificial neural networks

8th International Congress of Engineering Physics

物理学;工业技术

Carrillo, M.^1 ; Gónzalez, J.A.^1 ; Que, U.^1

Laboratorio de Inteligencia Artificial y Supercómputo, Instituto de Física y Matemáticas, Universidad Michoacana de San Nicolás de Hidalgo, Cd. Universitaria, Michoacán, Edificio C-3, Morelia

58040, Mexico^1

关键词: Input datas;    Physical parameters;    Poiseuille flow;    Reynolds;    Velocity field;    Velocity profiles;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/792/1/012071/pdf
DOI  :  10.1088/1742-6596/792/1/012071

学科分类：工业工程学

来源: IOP

【 摘 要 】

In this work the estimation of Reynolds number in a 2-dimensional Poiseuille flow is explored employing artificial neural networks (ANNs). The velocity fields of the fluids were generated evaluating the Hage-Poiseuille equation for different Reynolds (Re) from 20 to 2000. The velocity profile obtained for each case is used as input data for the ANNs, which is then trained to predict the Re. The results show an accuracy of at least of 99.5% in all prediction cases. This analysis is the first step towards the construction of a Machine Learning algorithm capable of computing physical parameters in more general scenarios.