数字海洋与水下攻防2025，Vol.8Issue(3)：251-266,16.DOI:10.19838/j.issn.2096-5753.2025.03.003
基于机器学习的射弹高速入水弹道预测
Machine Learning-based Prediction of Trajectories of Projectile High-speed Water Entry
摘要
Abstract
Current methods for projectile trajectory prediction face limitations in accurately describing key physical phenomena,such as turbulence and cavitation,which occur during water entry.These limitations result in reduced prediction accuracy and applicability in complex scenarios.To address these challenges,this study proposes an efficient method for predicting projectile water entry trajectories by integrating numerical simulation and machine learning techniques.The water entry hydrodynamics are simulated using the Reynolds-Averaged Navier-Stokes(RANS)equations,the SST turbulence model,the Volume of Fluid(VOF)multiphase model,and the Schnerr-Sauer cavitation model.Additionally,the six-degree-of-freedom(6-DOF)rigid-body motion model and overset grid techniques are employed to analyze projectile trajectories under various water entry angles.To improve prediction accuracy,a fully connected neural network(FCNN)is introduced as the prediction model to train and optimize the simulation data.Experimental results show that the proposed model effectively predicts projectile trajectories under different water entry angles,maintaining prediction errors within±5%.Furthermore,predicted quantities such as velocity,depth,and lateral displacement exhibit high consistency with simulation data.These findings demonstrate that the proposed machine learning model significantly enhances the accuracy of water entry trajectory predictions,offering robust engineering applications and valuable technical support for related fields.关键词
射弹入水/机器学习/弹道预测/CFD仿真/神经网络/六自由度Key words
projectile water entry/machine learning/trajectory prediction/CFD simulation/neural network/six degrees of freedom分类
信息技术与安全科学引用本文复制引用
钱志龙,蔡晓伟,漆培龙,何贤军,陈志华..基于机器学习的射弹高速入水弹道预测[J].数字海洋与水下攻防,2025,8(3):251-266,16.基金项目
国家自然科学基金委青年科学基金"空心射弹高速入水空泡时空演化与弹道稳定机理研究"(12002165). （12002165）
