Carrillo Mauricio, Que Ulices, González José A, López Carlos
Laboratorio de Inteligencia Artificial y Supercómputo, Instituto de Física y Matemáticas, Universidad Michoacana de San Nicolás de Hidalgo, Edificio C-3, Cd. Universitaria, 58040 Morelia, Michoacán, México.
Phys Rev E. 2017 Aug;96(2-1):023306. doi: 10.1103/PhysRevE.96.023306. Epub 2017 Aug 11.
In this work a series of artificial neural networks (ANNs) has been developed with the capacity to estimate the size and location of an obstacle 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 a certain distance from the obstacle. Data to train the ANN were generated using numerical simulations with a two-dimensional lattice Boltzmann code. We analyzed various cases varying both the diameter and the position of the obstacle on the y axis, obtaining good estimations using the R^{2} coefficient for the cases under study. Although the ANN showed problems with the classification of very small obstacles, the general results show a very good capacity for prediction.
在这项工作中，已开发出一系列人工神经网络（ANN），其能够估计阻碍管道内流体流动的障碍物的尺寸和位置。人工神经网络通过读取距障碍物一定距离处流体的动压q或速度vx的x分量的剖面来了解障碍物的尺寸和位置。使用二维格子玻尔兹曼代码进行数值模拟生成了用于训练人工神经网络的数据。我们分析了各种情况，改变了障碍物在y轴上的直径和位置，使用R²系数对所研究的情况获得了良好的估计。尽管人工神经网络在对非常小的障碍物进行分类时存在问题，但总体结果显示出非常好的预测能力。
