Open AccessCold Leg LBLOCA uncertainty analysis using TRACE/DAKOTA coupling
Author(s) -
G. Agnello,
P.A. Di Maio,
Andrea Bersano,
Fulvio Mascari
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2177/1/012023
Subject(s) - uncertainty analysis , trace (psycholinguistics) , probabilistic logic , thermal hydraulics , computer science , loss of coolant accident , coupling (piping) , uncertainty quantification , nuclear power , code (set theory) , nuclear power plant , environmental science , nuclear engineering , reliability engineering , coolant , engineering , simulation , mechanical engineering , mechanics , physics , nuclear physics , artificial intelligence , heat transfer , philosophy , linguistics , set (abstract data type) , machine learning , programming language
Safety analyses for nuclear power plants were carried out in the past using a conservative approach. With the increase of the phenomenological knowledge, through experimental data, and computational power, it became possible to adopt best estimate thermal- hydraulic system codes to perform deterministic safety analyses. However, some uncertainties are still present in the models, correlations, initial and boundary conditions, etc. Therefore, it is fundamental to quantify the uncertainty of calculation. This approach is called “Best Estimate Plus Uncertainty” (BEPU). Among the available uncertainty analysis methodologies, the probabilistic method to propagate input uncertainty is widely adopted. In the present study, an uncertainty analysis of a cold leg large break loss of coolant accident in a generic PWR-900 MWe has been developed and it has been carried out coupling the best estimate thermal- hydraulic system code TRACE and the uncertainty quantification tool DAKOTA in the SNAP environment/architecture.