A fast three-dimensional flow field prediction around bluff bodies using deep learning

Abstract

This study presents a deep learning approach for predicting the flow field in the incompressible turbulent three-dimensional (3D) external flow around right-rhombic prism-shaped bluff bodies. The approach involves treating the nodes of the unstructured grid in the computational fluid dynamics domain as a point cloud, which is used as an input for a neural network. The neural network is trained to map the spatial coordinates of the nodes to the corresponding velocity and pressure values in the domain. The PointNet, a reliable solution in 3D vision tasks, is selected as the neural network architecture. Implementing this architecture makes it feasible to use irregular positions of the nodes of an unstructured grid as an input without needing interpolation. A dataset, comprising 3511 cases, is generated for training and testing the network. This is achieved by changing the geometric parameters of a right rhombic prism and varying its angle to the flow stream. Then, the continuity and momentum equations for turbulent flow are solved using a solver. Given the need for a larger number of points to accurately represent a 3D flow, the architecture of PointNet is modified. This modification involves adding extra layers and adjusting the number of neurons inside the layers to overcome this challenge. Once the training is completed, given the unseen samples from the test dataset to the model, our model can predict the velocity and pressure of the flow field at a speed that exceeds our conventional solver by several orders of magnitude with a maximum relative error of 4.58%.