一个数据驱动的减少顺序替代模型,用于对代表体积元素应用整个弹性塑料模拟.
S Vijayaraghavan1, L Wu2, L Noels2
1Faculty of Science, Technology and Medicine, University of Luxembourg, 6 Avenue de la Fonte, Esch-Sur-Alzette, Luxembourg.
Scientific reports
|August 7, 2023
概括
这项研究引入了一种使用神经网络加速弹性塑料固体模拟的新型代孕模型. 这种方法通过避免代式方程解决,显著减少了计算时间,为复杂材料建模提供了一个有希望的替代方案.
科学领域:
- 计算型固体力学 计算型固体力学
- 在工程领域的机器学习.
背景情况:
- 传统的对弹性塑料固体的数值模拟由于代式方程解法而具有计算密集性.
- 替代模型提供了更快的替代方案,但与流体动力学相比,对弹性可塑性的探索较少.
研究的目的:
- 为了研究弹性塑料模拟的特定替代模型的潜力和局限性.
- 为了证明代孕体在代表体积元素中模拟宏观行为和微观结构量的能力.
主要方法:
- 采用一个替代模型,将解决方案场的特征模式与神经网络相结合.
- 使用循环神经网络来处理与速率无关的弹性塑性固有的路径依赖性.
- 避免对线性控制方程的代解决,每增量计算一次塑性变量.
主要成果:
- 与直接的数值模拟相比,代用模型显著减少了计算时间.
- 该模型有效地模拟了宏观压力-变形关系.
- 可以恢复代表体积元素中的微结构量.
结论:
- 开发的代孕模型显示了加速弹性塑料模拟的重大前景.
- 这种方法可以大大节省时间,并保持复杂材料行为的准确性.
- 需要进一步研究,以充分了解这些替代品在弹性可塑性的能力和局限性.
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