Uncertainty quantification and propagation with probability boxes
| Autor: | D. Cuervo, L. Duran-Vinuesa |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Mathematical optimization
Computer science 020209 energy Uncertainty-propagation 02 engineering and technology Field (computer science) 030218 nuclear medicine & medical imaging law.invention Uncertainty-quantification Wilks 03 medical and health sciences 0302 clinical medicine law Nuclear power plant 0202 electrical engineering electronic engineering information engineering Probability-boxes Uncertainty quantification Statistic Parametric statistics BEPU Propagation of uncertainty TK9001-9401 Nuclear Energy and Engineering Probability distribution Nuclear engineering. Atomic power Tolerance interval |
| Zdroj: | Nuclear Engineering and Technology, Vol 53, Iss 8, Pp 2523-2533 (2021) |
| ISSN: | 1738-5733 |
| Popis: | In the last decade, the best estimate plus uncertainty methodologies in nuclear technology and nuclear power plant design have become a trending topic in the nuclear field. Since BEPU was allowed for licensing purposes by the most important regulator bodies, different uncertainty assessment methods have become popular, overall non-parametric methods. While non-parametric tolerance regions can be well stated and used in uncertainty quantification for licensing purposes, the propagation of the uncertainty through different codes (multi-scale, multiphysics) in cascade needs a better depiction of uncertainty than the one provided by the tolerance regions or a probability distribution. An alternative method based on the parametric or distributional probability boxes is used to perform uncertainty quantification and propagation regarding statistic uncertainty from one code to another. This method is sample-size independent and allows well-defined tolerance intervals for uncertainty quantification, manageable for uncertainty propagation. This work characterizes the distributional p-boxes behavior on uncertainty quantification and uncertainty propagation through nested random sampling. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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