2022
DOI: 10.1016/j.cma.2022.115347
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An adaptive mesh refinement algorithm for phase-field fracture models: Application to brittle, cohesive, and dynamic fracture

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Cited by 47 publications
(5 citation statements)
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“…To address this issue, many researchers usually employ adaptive mesh techniques, as shown in Fig. 4, where a fine mesh is used at the interface and a coarse mesh is applied elsewhere (Li and Kim, 2012;Chen and Shen, 2016;Greenwood et al, 2018;Kim and Kim, 2021;Gupta et al, 2022). This approach enables the precise capture of interface evolutions while reducing computational costs.…”
Section: Phase-field Methodsmentioning
confidence: 99%
“…To address this issue, many researchers usually employ adaptive mesh techniques, as shown in Fig. 4, where a fine mesh is used at the interface and a coarse mesh is applied elsewhere (Li and Kim, 2012;Chen and Shen, 2016;Greenwood et al, 2018;Kim and Kim, 2021;Gupta et al, 2022). This approach enables the precise capture of interface evolutions while reducing computational costs.…”
Section: Phase-field Methodsmentioning
confidence: 99%
“…The grid should not be overly refined to avoid a substantial increase in computational time due to a large number of grids. Additionally, regions far from the interface need timely coarsening of the grid [146,147].…”
Section: Grand Potential Phase-field Sintering Modelmentioning
confidence: 99%
“…The calculation efficiency of this method can be increased by around 30 times over global mesh refinement. Aiming at the problem that the existing adaptive mesh refinement technology cannot accurately simulate crack initiation without a prior local refinement, Gupta et al [64] developed an adaptive mesh refinement algorithm for phase field models using a multi-level mark-unmark strategy. The algorithm utilizes a threshold to mark elements based on effective crack driving energy and reduces the calculation time by 5-50 times as compared to simulations that adopt a priori non-adaptively strategy.…”
Section: Adaptive Mesh Refinement Technologymentioning
confidence: 99%