线性和非线性复合材料的多尺度计算和人工智能模型:一篇评论
Mohit Agarwal1, Parameshwaran Pasupathy1, Xuehai Wu1
1Mechanical and Aerospace Engineering Rutgers University-New Brunswick Piscataway NJ 08854 USA.
Small science
|April 11, 2025
概括
本研究回顾了复合材料的多尺度建模,涵盖了分子到宏观尺度的方法. 它强调了有限元素和数据驱动模型的进步,用于预测材料行为和生命周期.
科学领域:
- 材料科学与工程 材料科学与工程
- 计算力学 计算力学 计算力学
- 多尺度建模多尺度建模
背景情况:
- 复合材料,无论是硬的 (聚合物,金属,纤维) 和软的 (生物组织,如大脑白质),都需要复杂的建模技术.
- 现有的数值模型范围从分子动力学到有限元素 (FE) 分析,但计算资源,模型忠实性和可重复性仍然存在挑战.
- 需要先进的构成模型,包括粘弹性和微分粘弹性类型,对于准确捕捉材料行为至关重要.
研究的目的:
- 描述各种复合材料的最先进的多尺度建模方法.
- 概述当前多尺度建模方法中的关键挑战和局限性.
- 介绍复合材料计算建模的最新进展和未来趋势.
主要方法:
- 对分子动力学模拟,有限元素 (FE) 分析和机器学习/深度学习替代模型的审查.
- 构成材料模型的总结:粘弹性,超弹性和分数粘弹性.
- 探索先进的 FE 技术,如无网格方法和混合机器学习 (ML) 和 FE 模型.
主要成果:
- 识别挑战,包括网格化,数据变性,非线性驱动的不确定性,计算约束和模型可重复性.
- 介绍 FE 建模和数据驱动模型的最新进展,这些模型利用了大量的实验数据.
- 在各种长度和时间尺度上开发的数据驱动模型的演示.
结论:
- 最近FE和数据驱动模型的进展为复合多尺度建模的未来趋势提供了更清晰的前景.
- 数据驱动型号为复合结构的实时监测和生命周期预测提供了必要的工具.
- 将先进的数学和数值技术与大型实验数据集集的整合对于开发强大的数字模型至关重要.
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