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arXiv 2107.05142cond-mat.mtrl-sci
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图神经网络在脆性材料裂纹贯通与扩展模拟中的应用

Graph neural networks for simulating crack coalescence and propagation in brittle materials

Roberto Perera, Davide Guzzetti, Vinamra Agrawal

摘要:

随着微裂纹数量的增加,通过基于物理的模型对多个微裂纹相互作用进行高保真断裂力学模拟在计算上很快变得昂贵。本文建立了一个基于图神经网络的框架来模拟脆性材料中多裂纹相互作用引起的断裂和应力演化。GNN框架是在基于XFEM的裂缝模拟器生成的数据集上训练的。我们的框架实现了高预测精度的测试集(相比基于XFEM的裂缝模拟器)通过工程一系列基于GNN的预测。第一预测阶段确定模式-I和模式-II应力强度因子(其可用于通过LEFM计算应力演化),第二预测阶段确定哪些微裂纹将传播,并且最后阶段实际上将所选微裂纹的裂纹尖端位置传播到下一时刻。经过训练的GNN框架能够模拟各种初始微裂纹配置(从5到19个微裂纹)的裂纹扩展,合并和相应的应力分布,而无需任何额外的修改。最后,与基于XFEM的模拟器相比,该框架的模拟时间显示了6倍至25倍的速度。这些特点,使我们的GNN框架模拟多个初始微裂纹的脆性材料中的微裂纹扩展和应力演化的一个有吸引力的方法。

英文摘要:

High-fidelity fracture mechanics simulations of multiple microcracks interaction via physics-based models quickly become computationally expensive as the number of microcracks increases. This work develops a Graph Neural Network (GNN) based framework to simulate fracture and stress evolution in brittle materials due to multiple microcracks' interaction. The GNN framework is trained on the dataset generated by XFEM-based fracture simulator. Our framework achieves high prediction accuracy on the test set (compared to an XFEM-based fracture simulator) by engineering a sequence of GNN-based predictions. The first prediction stage determines Mode-I and Mode-II stress intensity factors (which can be used to compute the stress evolution by LEFM), the second prediction stage determines which microcracks will propagate, and the final stage actually propagates crack-tip positions for the selected microcracks to the next time instant. The trained GNN framework is capable of simulating crack propagation, coalescence and corresponding stress distribution for a wide range of initial microcrack configurations (from 5 to 19 microcracks) without any additional modification. Lastly, the framework's simulation time shows speed-ups 6x-25x faster compared to an XFEM-based simulator. These characteristics, make our GNN framework an attractive approach for simulating microcrack propagation and stress evolution in brittle materials with multiple initial microcracks.

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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