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 (PCE), integrated with least-angle regression (LARS), a basis-adaptive technique, and a hyperbolic truncation scheme, in lieu of 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 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.
Efficient Model-Assisted Probability of Detection and Sensitivity Analysis for Ultrasonic Testing Simulations using Stochastic Metamodeling
Manuscript received January 22, 2019; final manuscript received July 29, 2019; published online xx xx, xxxx. Assoc. Editor: Shiv Joshi.
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Du, X., Leifsson, L., Meeker, W., Gurrala, P., Song, J., and Roberts, R. (August 2, 2019). "Efficient Model-Assisted Probability of Detection and Sensitivity Analysis for Ultrasonic Testing Simulations using Stochastic Metamodeling." ASME. ASME J Nondestructive Evaluation. doi: https://doi.org/10.1115/1.4044446
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