Research on simulation of gun muzzle flow field empowered by artificial intelligence  

作　　者：Mengdi Zhou Linfang Qian Congyong Cao Guangsong Chen Jin Kong Ming-hao Tong 

机构地区：[1]School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing,210094,China [2]Northwest Institute of Mechanical and Electrical Engineering,Xianyang,712099,Shaanxi,China [3]School of Automation,Nanjing University of Science and Technology,Nanjing,210094,China

出　　处：《Defence Technology（防务技术）》2024年第2期196-208,共13页Defence Technology

基　　金：Supported by the Natural Science Foundation of Jiangsu Province of China(Grant No.BK20210347);Supported by the National Natural Science Foundation of China(Grant No.U2141246).

摘　　要：Artificial intelligence technology is introduced into the simulation of muzzle flow field to improve its simulation efficiency in this paper.A data-physical fusion driven framework is proposed.First,the known flow field data is used to initialize the model parameters,so that the parameters to be trained are close to the optimal value.Then physical prior knowledge is introduced into the training process so that the prediction results not only meet the known flow field information but also meet the physical conservation laws.Through two examples,it is proved that the model under the fusion driven framework can solve the strongly nonlinear flow field problems,and has stronger generalization and expansion.The proposed model is used to solve a muzzle flow field,and the safety clearance behind the barrel side is divided.It is pointed out that the shape of the safety clearance under different launch speeds is roughly the same,and the pressure disturbance in the area within 9.2 m behind the muzzle section exceeds the safety threshold,which is a dangerous area.Comparison with the CFD results shows that the calculation efficiency of the proposed model is greatly improved under the condition of the same calculation accuracy.The proposed model can quickly and accurately simulate the muzzle flow field under various launch conditions.

关 键 词：Muzzle flow field Artificial intelligence Deep learning Data-physical fusion driven Shock wave 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TJ301[自动化与计算机技术—控制科学与工程]