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arXiv 2103.12396cond-mat.mtrl-sci
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数据驱动的率相关断裂力学

Data-Driven Rate-Dependent Fracture Mechanics

Pietro Carrara, Michael Ortiz, Laura De Lorenzis

摘要:

我们扩展了Carrara等人(2020),数据驱动断裂力学,Comp. Meth.应用机械工程师,372到率相关断裂和亚临界疲劳。该问题是制定相结合的平衡控制方程源于变分原理与一组数据点,编码的断裂本构行为的材料。该解决方案被发现的数据点,最好地满足亚稳定性条件的变分过程,并遵循基于最近点投影的距离最小化方法。采用不同类型的率相关断裂和疲劳模型的影响或不受白噪声的不同设置的方法进行了测试。

英文摘要:

We extend the model-free data-driven paradigm for rate-independent fracture mechanics proposed in Carrara et al. (2020), Data-driven Fracture Mechanics, Comp. Meth. App. Mech. Eng., 372 to rate-dependent fracture and sub-critical fatigue. The problem is formulated by combining the balance governing equations stemming from variational principles with a set of data points that encodes the fracture constitutive behavior of the material. The solution is found as the data point that best satisfies the meta-stability condition as given by the variational procedure and following a distance minimization approach based on closest-point-projection. The approach is tested on different setups adopting different types of rate-dependent fracture and fatigue models affected or not by white noise.

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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