Machine learning and numerical investigation on drag reduction of underwater serial multi-projectiles

作     者：Xi Huang Cheng Cheng Xiao-bing Zhang Xi Huang;Cheng Cheng;Xiao-bing Zhang

作者机构：School of Energy and Power EngineeringNanjing University of Science and TechnologyNanjing210094China 

出 版 物：《Defence Technology（防务技术）》 (Defence Technology)

年 卷 期：2022年第18卷第2期

页      面：229-237页

核心收录：

学科分类：08[工学] 082601[工学-武器系统与运用工程] 0806[工学-冶金工程] 0817[工学-化学工程与技术] 0826[工学-兵器科学与技术] 0805[工学-材料科学与工程（可授工学、理学学位）] 0802[工学-机械工程] 0703[理学-化学] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：supported by the National Natural Science Foun-dation of China(Grant No.11972194 12072160). 

主　　题：Drag reduction Serial multi-projectiles Machine learning Artificial neural network(ANN) Numerical simulation 

摘      要：To increase launching frequency and decrease drag force of underwater projectiles,a serial multiprojectiles structure based on the principle of supercavitation is proposed in this paper.The drag reduction and supercavitation characteristics of the underwater serial multi-projectiles are studied with computational fluid dynamics(CFD)and machine learning.Firstly,the numerical simulation model for the underwater supercavitating projectile is established and verified by experimental data.Then the evolution of the supercavitation for the serial multi-projectiles is described.In addition,the effects of different cavitation numbers and different distances between projectiles are investigated to demonstrate the supercavitation and drag reduction performance.Finally,the artificial neural network(ANN)model is established to predict the evolution of drag coefficient based on the data obtained by CFD,and the results predicted by ANN are in good agreement with the data obtained by CFD.The finding provides a useful guidance for the research of drag reduction characteristics of underwater serial projectiles.