贝叶斯参数估计用于将不确定性纳入复合材料渐进损坏模拟中的不确定性
Johannes Reiner1, Nathaniel Linden2, Reza Vaziri3
1School of Engineering, Faculty of Science Engineering and Built Environment, Deakin University, Geelong, Australia.
概括
这项研究结合了有限元分析 (FEA),机器学习和马尔科夫链蒙特卡洛,通过考虑材料性质变化,准确模拟复合板材的渐进损伤.
科学领域:
- 计算力学是计算力学.
- 材料科学是一种材料科学.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 在复合板材中模拟渐进性损伤是具有挑战性的,因为材料属性的不确定性.
- 准确的材料属性估计对于可靠的复合结构分析至关重要.
研究的目的:
- 开发一个强大的计算框架来模拟复合板材的渐进性损伤.
- 为了估计有限元素分析 (FEA) 输入参数的概率密度,考虑机械性质变化.
- 为了确定纤维增强复合材料的虚拟设计允许值.
主要方法:
- 利用 15,000 个有限元素分析 (FEA) 模拟与连续损伤力学模型.
- 在合成生成的FEA数据上训练了一个神经网络代理模型.
- 应用马尔科夫链蒙特卡洛 (MCMC) 算法用于使用实验数据估计贝叶斯参数.
主要成果:
- 成功创建了一个有效的替代模型来预测渐进的损伤.
- 实现了对FEA输入参数的准确贝叶斯参数估计.
- 根据各种开孔张力测试几何形状验证了方法.
结论:
- 结合FEA,机器学习和MCMC的方法有效地解决了复合材料特性中的不确定性.
- 这种方法提供了一种可靠的方法来模拟渐进的损伤演变.
- 该方法可以确定可允许的虚拟设计,改进复合设计过程.
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