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Probabilistic Fatigue Damage Prognosis Using a Surrogate Model Trained Via 3D Finite Element Analysis

P.E. LESER, J.D. HOCHHALTER, J.A. NEWMAN, W.P. LESER, J.E. WARNER, P.A. WAWRZYNEK, F.-G. YUAN

Abstract


Utilizing inverse uncertainty quantification techniques, structural health monitoring data can be integrated with damage progression models to form probabilistic predictions of a structure’s remaining useful life. 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 significantly reduced using a surrogate model trained via finite element analysis. The new approach is applied to experimental damage diagnosis data to form a probabilistic prediction of remaining useful life for a test specimen under mixed-mode conditions.

doi: 10.12783/SHM2015/299


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