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Simulation of ductile failure over long distances with a non–local GTN model

Abstract : The prediction of damage development under hypothetical accidental loading constitutes a major stake to guarantee the safety of industrial installations: nuclear power-plants, pipelines, etc. It is then important to correctly predict crack initiation and propagation. The aim of this work is to develop and implement a reliable and efficient strategy to predict crack initiation and propagation over long distances using the Finite element Method (FEM) in the scope of local approach ro failure. The first part of the thesis consists in developing a mesh objective GTN damage model adapted for metals such as construction steels. To do so an implicit gradient nonlocal model with two internal length scales was developed and implemented in the Z-set software. The two internal length scales model is tailored to represent both damage mechanisms active in steels: (i) void nucleation on strengthening particles, (ii) void growth. However, the use of nonlocal models is limited by the computational cost, especially for long distance propagation computations, due to the fine discretization needed in the localization band. This problem is addressed by using mesh adaption. Same stress-strain curves and crack–paths were obtained using mesh adaption in comparison to fixed mesh simulation with 20 times fewer DOFs. In addition, a continuous–discontinuous approach is adopted to accurately represent the kinematics of the crack propagation (i.e. displacement jumps). This new approach is applied to an existing experimental database, and good agreement between simulations and experiments is found.
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https://pastel.archives-ouvertes.fr/tel-03869005
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Submitted on : Thursday, November 24, 2022 - 9:57:19 AM
Last modification on : Friday, November 25, 2022 - 3:53:05 AM

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2022UPSLM027_archivage.pdf
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  • HAL Id : tel-03869005, version 1

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Amar El Ouazani Tuhami. Simulation of ductile failure over long distances with a non–local GTN model. Material chemistry. Université Paris sciences et lettres, 2022. English. ⟨NNT : 2022UPSLM027⟩. ⟨tel-03869005⟩

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