Modèles éléments finis pour l'étude de la fragilisation par l'hydrogène des structures en acier
Year: 2025
Type: dissertation
Abstract: Industrial power generation and transportation structures, typically designed for a service life of up to 40 years, requirea deep understanding of long-term material behavior to ensure safety and reliability. With increasing demand for energy,hydrogen is emerging as a promising energy vector in the decarbonization of energy sources. However, the abilityof hydrogen to diffuse into materials can lead to premature failure characterized by reduced ductility and toughness, aphenomenon known as hydrogen embrittlement (HE). This complex mechanism involves both mechanical and chemicalstresses.This study presents a finite element method (FEM) strategy to simulate HE by integrating plasticity and damage withhydrogen diffusion. To address volumetric locking, mixed and selective integration methods were employed. The Gurson-Tvergaard-Needleman (GTN) model, enhanced by an implicit gradient nonlocal formulation with two internal lengths,enabling the regularization of void growth and strain-controlled nucleation. In this model, damage evolution is increased byhydrogen concentration, effectively modeling hydrogen-enhanced decohesion (HEDE) and subsequent premature failure.The developed framework was applied to various simulations, including pressurized disks, tensile tests, and fracture toughnesstests with standard and sub-size specimens. Numerical results demonstrated good agreement with experimentaldata, demonstrating the model's capacity to estimate hydrogen distribution within the metal, accounting for diffusion,trapping, multi-crack initiation, and premature failure. This numerical framework not only allows better understanding ofhydrogen embrittlement but also serves as a valuable tool for interpreting experimental results.
Source: theses.fr (ABES)
Author: Lopes pinto, Daniella
Language: English
Subfield: Metals and Alloys
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7083634822
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