arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2001.11328cond-mat.mtrl-sciphysics.comp-phphysics.data-an
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用机器学习加速高应变连续尺度脆性断裂模拟

Accelerating High-Strain Continuum-Scale Brittle Fracture Simulations with Machine Learning

M. Giselle Fernández-Godino, Nishant Panda, Daniel O'Malley, Kevin Larkin, Abigail Hunter, Raphael T. Haftka, Gowri Srinivasan

摘要:

脆性材料在动态载荷条件下的失效是微裂纹扩展和合并的结果。在连续介质水平上模拟这种机制在计算上是昂贵的,或者在某些情况下是棘手的。计算成本是由于需要高分辨率的计算网格需要捕捉复杂的裂纹扩展行为,如分支,转向等,通常情况下,连续尺度模型,占脆性损伤演化均匀的裂纹网络以某种方式,这降低了整体计算成本,但也可以忽略关键物理的亚网格裂纹扩展行为,牺牲精度的效率。我们已经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微观尺度物理的问题。我们的方法利用来自高保真模型的损伤和应力数据,该模型明确地解决了微裂纹行为,以构建一个廉价的机器学习仿真器,该仿真器在几秒钟内运行,而高保真模型需要几个小时。一旦经过训练,机器学习仿真器就用于预测裂纹长度统计的演变。一个连续尺度的本构模型,然后通知这些裂纹统计,加快工作流程的四个数量级。机器学习模型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,显示出良好的一致性。有两个关键发现。首先,我们可以降低问题的维度,建立机器学习仿真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。另一个令人信服的发现是,模拟器可以在一个实验环境中训练,并成功地转移到预测不同环境中的行为。

英文摘要:

Failure in brittle materials under dynamic loading conditions is a result of the propagation and coalescence of microcracks. Simulating this mechanism at the continuum level is computationally expensive or, in some cases, intractable. The computational cost is due to the need for highly resolved computational meshes required to capture complex crack growth behavior, such as branching, turning, etc. Typically, continuum-scale models that account for brittle damage evolution homogenize the crack network in some way, which reduces the overall computational cost, but can also neglect key physics of the subgrid crack growth behavior, sacrificing accuracy for efficiency. We have developed an approach using machine learning that overcomes the current inability to represent micro-scale physics at the macro-scale. Our approach leverages damage and stress data from a high-fidelity model that explicitly resolves microcrack behavior to build an inexpensive machine learning emulator, which runs in seconds as opposed to the high-fidelity model, which takes hours. Once trained, the machine learning emulator is used to predict the evolution of crack length statistics. A continuum-scale constitutive model is then informed with these crack statistics, speeding up the workflow by four orders of magnitude. Both the machine learning model and the continuum-scale model are validated against a 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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