Efficient Model-Assisted Probability of Detection and Sensitivity Analysis for Ultrasonic Testing Simulations using Stochastic Metamodeling
Model-assisted probability of detection (MAPOD) and sensitivity analysis (SA) are important for quantifying the inspection capability of nondestructive testing (NDT) systems. To improve the computational efficiency, this work proposes the use of polynomial chaos expansions (PCEs), integrated with least-angle regression (LARS), a basis-adaptive technique, and a hyperbolic truncation scheme, in lieu of the direct use of the physics-based measurement model in the MAPOD and SA calculations. The proposed method is demonstrated on three ultrasonic testing cases and compared with Monte Carlo sampling (MCS) of the physics model, MCS-based kriging, and the ordinary least-squares (OLS)-based PCE method. The results show that the probability of detection (POD) metrics of interests can be controlled within 1% accuracy relative to using the physics model directly. Comparison with metamodels shows that the LARS-based PCE method can provide up to an order of magnitude improvement in the computational efficiency.
X. Du et al., "Efficient Model-Assisted Probability of Detection and Sensitivity Analysis for Ultrasonic Testing Simulations using Stochastic Metamodeling," Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, vol. 2, no. 4, article no. 041002, American Society of Mechanical Engineers, Nov 2019.
The definitive version is available at https://doi.org/10.1115/1.4044446
Mechanical and Aerospace Engineering
Keywords and Phrases
metamodeling; model-assisted probability of detection; nondestructive testing systems; physics-based measurement models; Sobol' indices; ultrasonics
International Standard Serial Number (ISSN)
Article - Journal
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01 Nov 2019