Robust optimization of a dynamic Black-box system under severe uncertainty: A distribution-free framework. (15th March 2022)

• Record Type: Journal Article
• Authors: Lye, Adolphus; Kitahara, Masaru; Broggi, Matteo; Patelli, Edoardo
• Is Part Of: Mechanical systems and signal processing. Volume 167:Part A(2022)
• Publication Date: 2022-03-15
• Subjects: Uncertainty quantification -- Model class selection -- Sensitivity analysis -- Staircase density function -- Robust optimization -- Non-intrusive imprecise stochastic simulation; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108522
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: In the real world, a significant challenge faced in designing critical systems is the lack of available data. This results in a large degree of uncertainty and the need for uncertainty quantification tools so as to make risk-informed decisions. The NASA-Langley UQ Challenge 2019 seeks to provide such setting, requiring different discipline-independent approaches to address typical tasks required for the design of critical systems. This paper addresses the NASA-Langley UQ Challenge by proposing 4 key techniques to provide the solution to the challenge: (1) a distribution-free Bayesian model updating framework for the calibration of the uncertainty model; (2) an adaptive pinching approach to analyse and rank the relative sensitivity of the epistemic parameters; (3) the probability bounds analysis to estimate failure probabilities; and (4) a Non-intrusive Stochastic Simulation approach to identify an optimal design point. Highlights: A distribution-free Bayesian model updating approach is used for model calibration. An adaptive pinching approach is proposed for sensitivity analysis. Probability bounds analysis with P-boxes are adopted for reliability analysis. Non-intrusive stochastic simulation is used for reliability based design.