论文标题
使用生成对抗网络的物理信息生成分散多相流的机器学习
Machine Learning for Physics-Informed Generation of Dispersed Multiphase Flow Using Generative Adversarial Networks
论文作者
论文摘要
围绕固定球形颗粒的随机分布的流体流在研究分散多相流的研究中是一个重要的问题。在本文中，我们使用生成对抗网络框架和卷积神经网络体系结构提出了一种机器学习方法，以在单分散粒子的随机分布周围重新创建粒子分辨的流体流。该模型分别应用于[2.69，172.96]和[0.11，0.45]范围内的各种雷诺数和粒子体积分数组合。研究案例的模型测试性能是非常有希望的。
Fluid flow around a random distribution of stationary spherical particles is a problem of substantial importance in the study of dispersed multiphase flows. In this paper we present a machine learning methodology using Generative Adversarial Network framework and Convolutional Neural Network architecture to recreate particle-resolved fluid flow around a random distribution of monodispersed particles. The model was applied to various Reynolds number and particle volume fraction combinations spanning over a range of [2.69, 172.96] and [0.11, 0.45] respectively. Test performance of the model for the studied cases is very promising.
