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Accelerating high-strain continuum-scale brittle fracture simulations with machine learning
Computational Materials Science ( IF 3.1 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.commatsci.2020.109959
M. Giselle Fernández-Godino , Nishant Panda , Daniel O’Malley , Kevin Larkin , Abigail Hunter , Raphael T. Haftka , Gowri Srinivasan

Abstract Failure in brittle materials under dynamic loading conditions is a result of the propagation and coalescence of microcracks. Simulating this discrete crack evolution at the continuum level is computationally expensive or, in some cases, intractable, resulting in the need to make broad assumptions or neglect key physics. We have developed an approach using machine learning that overcomes the current inability to represent meso-scale physics at the macro-scale. Our approach leverages damage and stress data from a computationally expensive high-fidelity model that explicitly resolves microcrack behavior to build an inexpensive machine learning emulator. Once trained, the machine learning emulator is used to predict the evolution of crack length statistics, which then informs a continuum-scale constitutive model. This results in a significant speed-up of the workflow by four orders of magnitude. Both the machine learning emulator and the continuum-scale model are validated against the high-fidelity model and experimental data, respectively, showing excellent agreement. There are two key findings. The first is that we can reduce the dimensionality of the problem, establishing that the machine learning emulator only needs the length of the longest crack and one of the maximum stress components to capture the necessary physics. Another compelling finding is that the emulator can be trained in one experimental setting and transferred successfully to predict behavior in a different setting.

中文翻译:

使用机器学习加速高应变连续尺度脆性断裂模拟

摘要 动态加载条件下脆性材料的破坏是微裂纹扩展和聚结的结果。在连续体水平上模拟这种离散裂纹演化在计算上是昂贵的,或者在某些情况下是难以处理的,导致需要做出广泛的假设或忽略关键物理。我们开发了一种使用机器学习的方法,克服了目前无法在宏观尺度上表示中尺度物理的问题。我们的方法利用来自计算成本高的高保真模型的损伤和应力数据,该模型明确解决微裂纹行为,以构建廉价的机器学习模拟器。经过训练后,机器学习模拟器用于预测裂纹长度统计的演变,然后通知连续尺度本构模型。这导致工作流程显着加快了四个数量级。机器学习模拟器和连续尺度模型分别针对高保真模型和实验数据进行了验证,显示出极好的一致性。有两个关键发现。第一个是我们可以降低问题的维度,确定机器学习模拟器只需要最长裂纹的长度和最大应力分量之一来捕获必要的物理。另一个引人注目的发现是,模拟器可以在一个实验环境中进行训练,并成功转移到不同环境中预测行为。机器学习模拟器和连续尺度模型分别针对高保真模型和实验数据进行了验证,显示出极好的一致性。有两个关键发现。第一个是我们可以降低问题的维度,确定机器学习模拟器只需要最长裂纹的长度和最大应力分量之一来捕获必要的物理。另一个引人注目的发现是,模拟器可以在一个实验环境中进行训练,并成功转移到不同环境中预测行为。机器学习模拟器和连续尺度模型分别针对高保真模型和实验数据进行了验证,显示出极好的一致性。有两个关键发现。第一个是我们可以降低问题的维度,确定机器学习模拟器只需要最长裂纹的长度和最大应力分量之一来捕获必要的物理。另一个引人注目的发现是,模拟器可以在一个实验环境中进行训练,并成功转移到不同环境中预测行为。确定机器学习模拟器只需要最长裂纹的长度和最大应力分量之一即可捕获必要的物理特性。另一个引人注目的发现是,模拟器可以在一个实验环境中进行训练,并成功转移到不同环境中预测行为。确定机器学习模拟器只需要最长裂纹的长度和最大应力分量之一即可捕获必要的物理特性。另一个引人注目的发现是,模拟器可以在一个实验环境中进行训练,并成功转移到不同环境中预测行为。
更新日期:2021-01-01
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