精确的动态行为的有限元模拟:构成模型和深度学习的分析
Yiwei Zhang1,2, Chengcheng Guo2, Yahui Huang2
1Hainan Institute, Wuhan University of Technology, Sanya 572000, China.
Materials (Basel, Switzerland)
|April 9, 2024
概括
高精度模拟分析金属的动态行为. ZA构成模型和深度学习显著提高了Hugoniot弹性极限应力预测的准确性.
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
- 应用物理 应用物理
背景情况:
- 实验捕捉动态金属行为是具有挑战性的.
- 高精度的数值模拟对于分析动态特征至关重要.
- 构造模型和深度学习可以提高模拟的准确性.
研究的目的:
- 在动态金属行为分析中提高计算精度.
- 评估不同构成模型对有限元 (FE) 模拟的影响.
- 采用深度学习 (DL) 来分析模拟结果.
主要方法:
- 利用有限元素 (FE) 模型来模拟金属的动态行为.
- 分析了各种构成模型对模拟准确性的影响.
- 采用深度学习 (DL) 模型进行结果分析和预测.
- 研究了准静态收益强度和弹性波传播之间的关系.
主要成果:
- FE模拟有效地捕获了金属的弹性-塑性反应.
- ZA构成模型显示了最高的准确性,在502 m/s的Hugoniot弹性极限 (HEL) 应力时,预测得到了26.0%的改进.
- 温度效应会影响弹性-塑性反应中的应力描述.
- 准静态收益率强度参数与弹性波传播速度相关.
- DL模型实现了对HEL应力的高预测精度 (R平方=0.998,MAE=0.0062).
结论:
- 在模拟金属动态反应方面,ZA模型的精度很高.
- 构成模型的选择会影响模拟的准确性,特别是在温度和产量强度方面.
- 深度学习为分析和预测HEL压力提供了强大的方法.
- 这项研究为在模拟中选择合适的构成模型提供了有价值的指导.
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