人工神经网络用于预测复合障碍物的冲击强度
Yuyi Zhang1, Andrey Logachev1, Aleksandr Smirnov1
1Faculty of Mathematics and Mechanics, Saint Petersburg State University, Saint Petersburg 199034, Russia.
Materials (Basel, Switzerland)
|July 12, 2025
概括
本研究以有限元法 (FEM) 和人工神经网络 (ANN) 来模拟钢弹对复合物目标的冲击. 这种方法提高了复杂冲击模拟的计算效率.
科学领域:
- 材料科学 材料科学 材料科学
- 计算力学 计算力学 计算力学
- 影响力动态的影响力动态
背景情况:
- 了解射弹对复合材料的影响对于结构完整性至关重要.
- 模拟高速冲击带来了计算挑战,包括资源需求和数值不稳定性.
研究的目的:
- 调查钢弹弹对复合物目标的冲击和穿透情况.
- 开发一种高效的计算方法来模拟复合目标冲击.
主要方法:
- 聚甲基甲酸 (PMMA) 目标的有限元素方法 (FEM) 模拟.
- 纳入化时间断裂标准 (ITFC).
- 在FEM结果上训练的人工神经网络 (ANN) 模型的开发.
主要成果:
- 该研究成功模拟了射弹对同质和复合目标的影响.
- 拟议的ANN方法证明了一种可行的方法来克服冲击模拟中的计算限制.
- 该方法解决了与接触区的高压力和应变率相关的挑战.
结论:
- 结合FEM和ANN的方法为复杂的冲击模拟提供了有效的解决方案.
- 这种方法可以显著减少计算资源和时间来分析冲击下的复合材料行为.
- 这些发现有助于改进弹子与目标相互作用的预测建模.
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