Model-assisted probability of detection (MAPOD) and sensitivity analysis (SA) are widelyused for measuring the reliability of nondestructive testing (NDT) systems., such as ultrasonictesting (UT), and understanding the effects of uncertainty parameters. In this work, a stochastic expansion-based metamodel is used in lieu of the physics-based NDT simulation model for efficient uncertainty propagation while keeping satisfactory accuracy. The proposed stochasticmetamodeling approach is demonstrated for MAPOD and SA on a benchmark case for UT simulations on a fused quartz block with a spherically-void defect. The proposed approach is compared with direct Monte Carlo sampling (MCS), and MCS with Kriging metamodels. The results indicate that around one order of magnitude reduction in the number of model evaluations required for MAPOD analysis can be obtained. Moreover, the results indicate around two orders of magnitude reduction of the number of model evaluations for the convergence of the statistical moments and obtaining the problem sensitivities.
X. Du et al., "Fast Uncertainty Propagation of Ultrasonic Testing Simulations for MAPOD and Sensitivity Analysis," 2018 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2018, article no. 8503094, Institute of Electrical and Electronics Engineers, IEEE, Oct 2018.
The definitive version is available at https://doi.org/10.1109/NEMO.2018.8503094
Mechanical and Aerospace Engineering
Keywords and Phrases
Kriging metamodels; MAPOD; MCS; NDT; Nondestructive testing; Sensitivity analysis; Stochastic metamodeling
International Standard Book Number (ISBN)
Article - Conference proceedings
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23 Oct 2018