In actual combat, the attack of the warhead on the target is a dynamic process, and there is a significant difference in shock wave pressure between dynamic and static explosions of ammunition, while dynamic explosions are more in line with actual combat situations. Therefore, conducting research on the distribution law of dynamic explosion shock wave pressure in ammunition has more practical value for evaluating the damage power of ammunition and guiding its use. This study used the display explosion dynamics simulation software AUTODYN to conduct simulation analysis on the pressure distribution patterns of static and dynamic explosion shock waves, clarifying the differences in pressure distribution between dynamic and static explosions. Considering the factors that affect the distribution law of dynamic explosion shock wave pressure, a BP neural network based correlation prediction model for static and dynamic explosion shock wave pressure was constructed, and the prediction accuracy of the model was verified. The analysis results indicate that the pressure distribution of dynamic explosion shock waves has a significant velocity tendency; The prediction accuracy of the static and dynamic shock wave pressure correlation prediction model based on BP neural network is better than 90.7%. The research results have improved the accuracy of the calculation of dynamic explosion shock wave pressure in warheads, providing effective calculation methods and scientific data support for the calculation of dynamic explosion shock wave pressure and the evaluation of damage power.

中文翻译：

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静态爆炸与动态爆炸冲击波压力相关预测模型研究

实战中，弹头对目标的攻击是一个动态过程，弹药动态爆炸和静态爆炸的冲击波压力存在显着差异，而动态爆炸更符合实战情况。因此，研究弹药动态爆炸冲击波压力的分布规律对于评价弹药的毁伤威力、指导其使用具有更实际的价值。本研究利用显示爆炸动力学模拟软件AUTODYN对静态和动态爆炸冲击波的压力分布模式进行模拟分析，明确了动态和静态爆炸压力分布的差异。考虑影响动态爆炸冲击波压力分布规律的因素，构建了基于BP神经网络的静态和动态爆炸冲击波压力相关预测模型，并验证了模型的预测精度。分析结果表明，动态爆炸冲击波的压力分布具有明显的速度趋势；基于BP神经网络的静动冲击波压力相关性预测模型预测精度优于90.7%。研究成果提高了战斗部动态爆炸冲击波压力计算的精度，为动态爆炸冲击波压力计算和毁伤威力评估提供有效的计算方法和科学数据支撑。