Probabilistic fatigue damage prognosis using surrogate models trained via three-dimensional finite element analysis
Auteur(s): |
Patrick E. Leser
Jacob D. Hochhalter James E. Warner John A. Newman William P. Leser Paul A. Wawrzynek Fuh-Gwo Yuan |
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Médium: | article de revue |
Langue(s): | anglais |
Publié dans: | Structural Health Monitoring, avril 2016, n. 3, v. 16 |
Page(s): | 291-308 |
DOI: | 10.1177/1475921716643298 |
Abstrait: |
Utilizing inverse uncertainty quantification techniques, structural health monitoring (SHM) can be integrated with damage progression models to form a probabilistic prediction of a structure’s remaining useful life (RUL). However, damage evolution in realistic structures is physically complex. Accurately representing this behavior requires high-fidelity models which are typically computationally prohibitive. In this paper, high-fidelity fatigue crack growth simulation times are reduced by three orders of magnitude using a model based on a set of surrogate models trained via three-dimensional finite element analysis. The developed crack growth modeling approach is experimentally validated using SHM-based damage diagnosis data. A probabilistic prediction of RUL is formed for a metallic, single-edge notch tension specimen with a fatigue crack growing under mixed-mode conditions. |
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10561960 - Publié(e) le:
11.02.2021 - Modifié(e) le:
19.02.2021