Robust optimization of a dynamic Black-box system under severe uncertainty : a distribution-free framework

Lye, Adolphus and Kitahara, Masaru and Broggi, Matteo and Patelli, Edoardo (2022) Robust optimization of a dynamic Black-box system under severe uncertainty : a distribution-free framework. Mechanical Systems and Signal Processing, 167. 108522. ISSN 0888-3270 (https://doi.org/10.1016/j.ymssp.2021.108522)

[thumbnail of Lye-etal-MSSP-2021-Robust-optimization-of-a-dynamic-Black-box-system-under]
Preview
 Text. Filename: Lye_etal_MSSP_2021_Robust_optimization_of_a_dynamic_Black_box_system_under_.pdf
Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo
Download (7MB)| Preview

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.

ORCID iDs

Lye, Adolphus, Kitahara, Masaru, Broggi, Matteo and Patelli, Edoardo ORCID logoORCID: https://orcid.org/0000-0002-5007-7247;
CORE (COnnecting REpositories)