计算同质化和识别辅助结构与间隔参数的辅助结构
Witold Beluch1, Marcin Hatłas2, Jacek Ptaszny1
1Faculty of Mechanical Engineering, Department of Computational Mechanics and Engineering, Silesian University of Technology, Konarskiego 18A, 44-100 Gliwice, Poland.
Materials (Basel, Switzerland)
|October 16, 2025
概括
本研究介绍了一种方法,用于使用计算同质化和人工神经网络分析辅助结构中的不确定材料特性. 该方法成功地从宏观变形数据中识别出微观材料参数,即使具有非线性特征.
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
- 机械工程 机械工程
背景情况:
- 具有非线性特征的异质材料,例如辅性结构,由于材料和拓参数的固有不确定性,在准确的建模中存在挑战.
- 传统方法往往难以捕捉这些材料在不确定性下的复杂行为,需要先进的计算方法.
研究的目的:
- 开发和验证具有不确定性质的异质辅助性材料的计算同质化和识别方法.
- 将基于间隔的不确定性有效地纳入对非线性材料行为的分析.
- 从宏观实验数据中准确识别微观材料参数.
主要方法:
- 利用有限元法 (FEM) 解决辅助性结构的边界值问题.
- 使用人工神经网络 (ANN) 响应表面来降低计算成本.
- 应用定向区间算术来最大限度地减少因参数不确定性而产生的区间宽度.
- 采用帕雷托方法和多目标进化算法来完成材料识别任务.
主要成果:
- 在不确定性下的计算同质化有效地捕获了异质辅助性材料的行为.
- 提出的方法证明了将不确定性纳入物质属性分析的重要性.
- 从宏观数据取得了微观材料参数的成功识别,包括非线性变形的间隔描述.
结论:
- 开发的计算框架提供了一种有效的手段来分析和识别带有间隔不确定性的异质辅助性材料的特性.
- 整合FEM,ANN和间隔算法为复杂的材料表征问题提供了强大的解决方案.
- 这项工作强调了考虑材料特性中的不确定性对于准确的预测建模至关重要.
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