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:

当前射弹弹道预测方法在处理入水后复杂非线性流场变化时，存在对湍流与空化等关键物理现象描述不足的问题，导致预测精度和适用性受限。针对上述问题，本研究结合数值仿真与机器学习技术，提出了一种高效的射弹入水弹道预测方法。首先，采用雷诺平均 Navier-Stokes（RANS）方程、SST 湍流模型、 VOF 多相流模型及 Schnerr-Sauer 空化模型，模拟射弹入水过程中的流体动力学特性，并利用六自由度刚体运动模型与重叠网格技术，分析射弹在不同入水角度下的运动轨迹。为提高预测精度，引入全连接神经网络 （FCNN）作为预测模型，对仿真结果进行训练与优化。结果表明：所提模型能够在不同入水角度下有效预测射弹的运动轨迹，预测误差控制在±5%以内。此外，研究发现射弹入水后的速度、深度及偏移量等物理量的预测结果与仿真数据高度一致。结论表明：所提出的机器学习模型显著提高了射弹入水弹道的预测精度，具有较强的工程应用价值，为相关领域提供了有效的技术支持。

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.