Data-driven fracture mechanics
Data-driven fracture mechanics
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DOI：
10.1016/j.cma.2020.113390
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发表时间：
2020-12-01
影响因子：
7.2
通讯作者：
Ortiz, M.
中科院分区：
文献类型：
--
作者：
Carrara, P.;De Lorenzis, L.;Ortiz, M.
我们提出了一个新的数据驱动的范式变分脆性断裂力学。去除了与材料相关的建模假设，并将变分原理产生的控制方程与一组离散数据点相结合，从而形成无模型数据驱动的求解方法。在一个给定的负载步骤的解决方案被确定为数据集内的点，最好地满足来自变分断裂问题的Kuhn-Tucker条件或一个合适的能量泛函的全局最小化，导致数据驱动的对应的局部和全局最小化方法的变分断裂力学。这两种配方进行了测试，在不同的测试配置和没有噪音和格里菲斯和R曲线型断裂行为。(c)2020年，任作家。由爱思唯尔公司出版。这是一篇开放获取的文章，获得了CC BY-NC-ND许可证（http：//creativecommons.org/licenses/by-nc-nd/4.0/）。
We present a new data-driven paradigm for variational brittle fracture mechanics. The fracture-related material modeling assumptions are removed and the governing equations stemming from variational principles are combined with a set of discrete data points, leading to a model-free data-driven method of solution. The solution at a given load step is identified as the point within the data set that best satisfies either the Kuhn-Tucker conditions stemming from the variational fracture problem or global minimization of a suitable energy functional, leading to data-driven counterparts of both the local and the global minimization approaches of variational fracture mechanics. Both formulations are tested on different test configurations with and without noise and for Griffith and R-curve type fracture behavior. (c) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).