Efficient uncertainty propagation for MAPOD via polynomial chaos-based Kriging

Abstract

Purpose Model-assisted probability of detection (MAPOD) is an important approach used as part of assessing the reliability of nondestructive testing systems. The purpose of this paper is to apply the polynomial chaos-based Kriging (PCK) metamodeling method to MAPOD for the first time to enable efficient uncertainty propagation, which is currently a major bottleneck when using accurate physics-based models. Design/methodology/approach In this paper, the state-of-the-art Kriging, polynomial chaos expansions (PCE) and PCK are applied to “a^ vs a”-based MAPOD of ultrasonic testing (UT) benchmark problems. In particular, Kriging interpolation matches the observations well, while PCE is capable of capturing the global trend accurately. The proposed UP approach for MAPOD using PCK adopts the PCE bases as the trend function of the universal Kriging model, aiming at combining advantages of both metamodels. Findings To reach a pre-set accuracy threshold, the PCK method requires 50 per cent fewer training points than the PCE method, and around one order of magnitude fewer than Kriging for the test cases considered. The relative differences on the key MAPOD metrics compared with those from the physics-based models are controlled within 1 per cent. Originality/value The contributions of this work are the first application of PCK metamodel for MAPOD analysis, the first comparison between PCK with the current state-of-the-art metamodels for MAPOD and new MAPOD results for the UT benchmark cases.

A dynamic adaptive hybrid surrogate-assisted particle swarm optimization algorithm for complex system design optimization

“…, 2018) and the Kriging model (You et al. , 2021; Du and Leifsson, 2020), among others. Ensemble surrogates, multiple surrogates and adaptive surrogates have also been implemented to enhance the accuracy of fitness approximation (Garbo and German, 2019).…”

“…Surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to solve this issue and use a computationally inexpensive surrogate model to replace expensive EC 39,7 high-fidelity simulations for fitness evaluations in evolutionary algorithms (Wang et al, 2021;Alizadeh et al, 2020;Koziel and Bekasiewicz, 2020). To this end, various surrogate models are used in SAEAs, including the response surface method (RSM) (Zhang et al, 2017), neural networks (NNs) (Carneiro et al, 2019), the radial basis function (RBF) (Jing et al, 2019), support vector machines (SVMs) (Tao et al, 2018) and the Kriging model (You et al, 2021;Du and Leifsson, 2020), among others. Ensemble surrogates, multiple surrogates and adaptive surrogates have also been implemented to enhance the accuracy of fitness approximation (Garbo and German, 2019).…”