The Application Of Deep Learning In Computational Fluid Mechanics

In recent years,deep learning has become a new research direction in the field of machine learning.Deep learning has achieved many advancements in technologies such as intelligent search,intelligent robots,face recognition,language processing,and voice recognition music news recommendation,and has begun to a large number of Applied to various engineering problems.In the field of computational fluid mechanics,bottlenecks such as complex basic models,large amount of calculation,and difficulty in integration have existed for a long time,and thus have the development conditions for applying deep learning methods.Therefore,this study applies deep learning methods to solve fluid mechanics problems.This article first introduces the basic concepts of machine learning and deep learning,and shows the wide application of the two in the field of engineering and interdisciplinary.Therefore,we introduced a neural network based on physical knowledge,trained this neural network to solve partial differential equations,and specifically given the application in the equations of fluid mechanics.The main application of this article is to learn the physics-based neural network,and use the neural network to solve partial differential equations.We predicted the solutions of the convection equation and the Burgers equation and compared them with the exact ones.By controlling the number of neural network layers,the number of neurons in each layer,the amount of training data and other variables,observe the error between the predicted and accurate solutions.The effect of the coefficient of the viscosity term on the Burgers equation is further considered.Finally,combining the images,the predicted and exact solutions of the Euler equations in fluid mechanics are compared.