Details
| Original language | English |
|---|---|
| Article number | 108522 |
| Journal | Mechanical Systems and Signal Processing |
| Volume | 167 |
| Issue number | Part A |
| Early online date | 1 Nov 2021 |
| Publication status | Published - 15 Mar 2022 |
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.
Keywords
- Model class selection, Non-intrusive imprecise stochastic simulation, Robust optimization, Sensitivity analysis, Staircase density function, Uncertainty quantification
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Aerospace Engineering
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
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In: Mechanical Systems and Signal Processing, Vol. 167, No. Part A, 108522, 15.03.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Robust optimization of a dynamic Black-box system under severe uncertainty
T2 - A distribution-free framework
AU - Lye, Adolphus
AU - Kitahara, Masaru
AU - Broggi, Matteo
AU - Patelli, Edoardo
N1 - Funding Information: This work has been partially funded by the Deutsche Forschungsgemeinsschaft (DFG, German Research Foundation) — SFB1463-434502799 .
PY - 2022/3/15
Y1 - 2022/3/15
N2 - 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.
AB - 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.
KW - Model class selection
KW - Non-intrusive imprecise stochastic simulation
KW - Robust optimization
KW - Sensitivity analysis
KW - Staircase density function
KW - Uncertainty quantification
UR - http://www.scopus.com/inward/record.url?scp=85118231987&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2021.108522
DO - 10.1016/j.ymssp.2021.108522
M3 - Article
AN - SCOPUS:85118231987
VL - 167
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
SN - 0888-3270
IS - Part A
M1 - 108522
ER -