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Probabilistic fatigue damage prognosis using surrogate models trained via three-dimensional finite element analysis

Auteur(s):






Médium: article de revue
Langue(s): anglais
Publié dans: Structural Health Monitoring, , 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.

Structurae ne peut pas vous offrir cette publication en texte intégral pour l'instant. Le texte intégral est accessible chez l'éditeur. DOI: 10.1177/1475921716643298.
  • Informations
    sur cette fiche
  • Reference-ID
    10561960
  • Publié(e) le:
    11.02.2021
  • Modifié(e) le:
    19.02.2021
 
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