【摘要】 针对传统弹道预测方法计算成本高、难以满足快速评估需求的问题，提出了基于卷积神经网络（convolutional neural network,CNN）的多层混凝土薄靶侵彻弹道高效预测模型。首先，基于经过试验验证的数值模拟方法，分析并明确了弹体角速度对弹道偏转的重要影响，进而将其作为重要的弹靶交会条件，通过系统调整初始参数，构建了包含127组工况的单层混凝土薄靶侵彻数据集。在此基础上，建立了以弹体参数、靶体参数、弹靶交会条件为输入，弹体靶后运动参数为输出的高精度单层靶侵彻弹道预测模型，并进一步结合弹体靶间飞行的刚体运动学方程，构建了完整的侵彻-飞行迭代预测框架，实现了多层间隔混凝土薄靶弹道特性的快速预测。研究结果表明：逆时针角速度增大会导致靶后径向剩余速度正向增大，弹道轨迹向上偏转，顺时针角速度则产生相反效应，弹体角速度是薄靶侵彻过程中不可忽略的重要参数；针对单层靶工况，预测模型训练集和测试集的平均均方误差稳定在0.001 2与0.001 9左右，表现出良好的预测性能；在多层靶预测中，模型在保证精度（剩余速度最大相对误差10.65%，姿态角最大绝对误差3.47°）的前提下，求解时间仅为传统数值模拟方法的0.05%。本研究不仅揭示了弹体角速度这一关键因素对侵彻弹道的影响规律，更提供了一种“数据驱动+物理方程融合”的建模新范式，可为武器毁伤效能评估与设计优化提供参考。更多还原

【Abstract】 To overcome the high computational cost of traditional ballistic prediction methods and their inability to satisfy rapid assessment requirements, this study proposes an efficient predictive model for the penetration ballistics of multi-layer thin concrete targets based on a convolutional neural network(CNN). First, a numerically simulated approach, validated by experiments, was employed to analyze and confirm the significant influence of projectile angular velocity on trajectory deflection, and this parameter was consequently identified as a key projectile–target engagement condition. By systematically varying the initial conditions, a dataset comprising 127 cases of single-layer thin concrete target penetration was constructed.On this basis, a high-accuracy ballistic prediction model for single-layer targets was developed, taking projectile parameters,target parameters, and engagement conditions as inputs, and post-impact projectile motion parameters as outputs. Furthermore,by incorporating rigid-body kinematic equations describing the projectile flight between successive targets, a complete iterative penetration-flight prediction framework was established, enabling rapid prediction of ballistic characteristics for multi-layer spaced thin concrete targets. The results indicate that an increase in counterclockwise angular velocity leads to a positive increase in the radial residual velocity behind the target and an upward deflection of the trajectory, whereas clockwise angular velocity produces the opposite effect. These findings demonstrate that projectile angular velocity is a critical and nonnegligible factor in thin-target penetration. For single-layer target cases, the model exhibited strong predictive capability, with the mean MSE values of the training and test sets stabilizing at approximately 0.001 2 and 0.001 9, respectively. For multi-layer target predictions, while maintaining high accuracy(with a maximum relative error of 10.65% in residual velocity and a maximum absolute error of 3.47° in attitude angle), the computational time of the proposed method was only about 0.05% of that required by conventional numerical simulation. This study not only elucidates the influence of the key factor-projectile angular velocity-on penetration ballistics, but also proposes a novel “data-driven and physics-equation fusion” modeling paradigm, providing an important methodological reference for weapon damage effectiveness assessment and design optimization.更多还原