Efficient Uncertainty Propagation for High-Fidelity Simulations With Large Parameter Spaces: Application to Stiffened Plate Buckling

Abstract

Uncertainty quantification (UQ) and propagation are critical to the computational assessment of structural components and systems. In this work, we discuss the practical challenges of implementing uncertainty quantification for high-dimensional computational structural investigations, specifically identifying four major challenges: (1) Computational cost; (2) Integration of engineering expertise; (3) Quantification of epistemic and model-form uncertainties; and (4) Need for V&V, standards, and automation. To address these challenges, we propose an approach that is straightforward for analysts to implement, mathematically rigorous, exploits analysts' subject matter expertise, and is readily automated. The proposed approach utilizes the Latinized partially stratified sampling (LPSS) method to conduct small sample Monte Carlo simulations. A simplified model is employed and analyst expertise is leveraged to cheaply investigate the best LPSS design for the structural model. Convergence results from the simplified model are then used to design an efficient LPSS-based uncertainty study for the high-fidelity computational model investigation. The methodology is carried out to investigate the buckling strength of a typical marine stiffened plate structure with material variability and geometric imperfections.

Probability measure changes in Monte Carlo simulation

“…While this isn't critical for the simple model considered here, it would be a huge benefit if Eq. (34) were replaced with an expensive finite element model such as that studied in [22].…”