A point cloud deep neural network metamodel method for aerodynamic prediction  

作　　者：Fenfen XIONG Li ZHANG Xiao HU Chengkun REN 

机构地区：[1]School of Aerospace Engineering,Beijing Institute of Technology,Beijing 100081,China [2]Department of Mechanical Engineering,Imperial College London,London SW72AZ,UK

出　　处：《Chinese Journal of Aeronautics》2023年第4期92-103,共12页中国航空学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.52175214);the Basic Research Program of Equipment Development Department(No.514010103-302).

摘　　要：Aiming to reduce the high expense of 3-Dimensional(3D)aerodynamics numerical sim-ulations and overcome the limitations of the traditional parametric learning methods,a point cloud deep learning non-parametric metamodel method is proposed in this paper.The 3D geometric data,corresponding to the object boundaries,are chosen as point clouds and a deep learning neural net-work metamodel fed by the point clouds is further established based on the PointNet architecture.This network can learn an end-to-end mapping between spatial positions of the object surface and CFD numerical quantities.With the proposed aerodynamic metamodel approach,the point clouds are constructed by collecting the coordinates of grid vertices on the object surface in a CFD domain,which can maintain the boundary smoothness and allow the network to detect small changes between geometries.Moreover,the point clouds are easily accessible from 3D sensors.The point cloud deep learning neural network,which employs re-sampling technique,the spatial transformer network and the fully connected layer,is developed to predict the aerodynamic char-acteristics of 3D geometry.The effectiveness of the proposed metamodel method is further verified by aerodynamic prediction and robust shape optimization of the ONERA M6 wing.The results show that the proposed method can achieve more satisfactory agreement with the experimental measurements compared to the parametric-learning-based deep neural network.

关 键 词：Aerodynamic prediction Deep neural network METAMODEL Point clouds Robust shape optimization 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] V211[自动化与计算机技术—控制科学与工程]