使用内斯特罗夫梯度下降方法优化约翰逊-库克建构模型参数
Sergey A Zelepugin1, Roman O Cherepanov1, Nadezhda V Pakhnutova1
1Tomsk Scientific Center of the Siberian Branch of the Russian Academy of Sciences, 634055 Tomsk, Russia.
Materials (Basel, Switzerland)
|August 12, 2023
概括
本研究介绍了一种优化方法,用于准确选择约翰逊-库克 (JC) 材料模型常数,用于模拟高速冲击. 与实验数据相比,优化的参数显著提高了模拟准确性.
科学领域:
- 固体力学 固体力学是什么
- 计算材料科学科学 计算材料科学
- 数字模拟 数字模拟
背景情况:
- 准确模拟撞击下的可变形固体需要强大的材料模型.
- 约翰逊-库克 (JC) 模型被广泛使用,但需要精确的恒定校准,特别是在高冲击速度时.
研究的目的:
- 开发和验证一种使用Nesterov梯度下降优化JC模型常数的方法.
- 为了提高高速撞击事件的数值模拟的准确性.
主要方法:
- 采用了一种基于Nesterov梯度下降方法的优化算法.
- 定义了一个溶液质量函数来量化模拟和实验数据之间的偏差.
- 对铜样进行了泰勒杆对杆撞击测试的数值模拟.
主要成果:
- 优化的JC模型参数与实验和文献数据达成良好一致.
- 在所有测试的实验中,模拟精度 (溶液质量) 提高了10%.
- 该方法在校准用于高速冲击模拟的材料模型方面表现出有效性.
结论:
- 建议的优化方法有效地确定了最佳的JC模型常数,以提高模拟准确性.
- 这种方法提高了对冲击和冲击波相互作用的数值模拟的可靠性.
- 该方法可适应其他材料模型和模拟代码,用于高速冲击分析.
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