Abstract
In this paper we present a framework for addressing a variety of engineering design challenges with limited empirical data and partial information. This framework includes guidance on the characterisation of a mixture of uncertainties, efficient methodologies to integrate data into design decisions, and to conduct reliability analysis, and risk/reliability based design optimisation. To demonstrate its efficacy, the framework has been applied to the NASA 2020 uncertainty quantification challenge. The results and discussion in the paper are with respect to this application.
Original language | English |
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Article number | 108210 |
Number of pages | 39 |
Journal | Mechanical Systems and Signal Processing |
Volume | 165 |
Early online date | 25 Aug 2021 |
DOIs | |
Publication status | Published - 15 Feb 2022 |
Keywords
- Bayesian calibration
- probability bounds analysis
- uncertainty propagation
- uncertainty reduction
- epistemic uncertainty
- optimisation under uncertainty