From inference to design: A comprehensive framework for uncertainty quantification in engineering with limited information

Ander Gray, Alexander Wimbush, Marco de Angelis (Corresponding author), Peter O. Hristov (Corresponding author), Dominic Calleja, Enrique Miralles-Dolz, Roberto Rocchetta (Corresponding author)

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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 languageEnglish
Article number108210
Number of pages39
JournalMechanical Systems and Signal Processing
Volume165
DOIs
Publication statusPublished - 15 Feb 2022

Funding

This research was funded in part by EPSRC and ESRC CDT in Risk and Uncertainty, EP/L015927/1, EPSRC “Digital twins for improved dynamic design” EP/R006768/1, the Digital Lifecycle Twins for predictive maintenance “ITEA3-2018-17030-DayTiMe” grant, and EUROfusion Consortium and Euratom research and training programme under grant agreement No 633053. The views and opinions expressed herein do not necessarily reflect those of the funding organisations that have supported this work. The authors would like to thank Professor Scott Ferson, Chair of Risk and Uncertainty at the Institute for Risk and Uncertainty, University of Liverpool, in particular for his guidance, support, mentorship and seemingly bottomless knowledge of uncertainty analysis. This research was funded in part by EPSRC and ESRC CDT in Risk and Uncertainty, EP/L015927/1, EPSRC “Digital twins for improved dynamic design” EP/R006768/1, the Digital Lifecycle Twins for predictive maintenance “ITEA3-2018-17030-DayTiMe” grant, and EUROfusion Consortium and Euratom research and training programme under grant agreement No 633053. The views and opinions expressed herein do not necessarily reflect those of the funding organisations that have supported this work.

FundersFunder number
EUROfusion Consortium and Euratom633053
Institute for Risk and Uncertainty
Engineering and Physical Sciences Research Council
Economic and Social Research CouncilEP/R006768/1, EP/L015927/1
University of Liverpool

    Keywords

    • Bayesian calibration
    • Probability bounds analysis
    • Epistemic uncertainty
    • Uncertainty propagation
    • Uncertainty reduction
    • Optimisation under uncertainty

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