Uncertainty quantification and sensitivity analysis with CASL Core Simulator VERA-CS
- North Carolina State Univ., Raleigh, NC (United States). Dept. of Nuclear Engineering
- Idaho National Lab. (INL), Idaho Falls, ID (United States)
Uncertainty quantification and sensitivity analysis are important for nuclear reactor safety design and analysis. A 2x2 fuel assembly core design was developed and simulated by the Virtual Environment for Reactor Applications, Core Simulator (VERA-CS) coupled neutronics and thermal-hydraulics code under development by the Consortium for Advanced Simulation of Light Water Reactors (CASL). An approach to uncertainty quantification and sensitivity analysis with VERA-CS was developed and a new toolkit was created to perform uncertainty quantification and sensitivity analysis with fourteen uncertain input parameters. Furthermore, the minimum departure from nucleate boiling ratio (MDNBR), maximum fuel center-line temperature, and maximum outer clad surface temperature were chosen as the selected figures of merit. Pearson, Spearman, and partial correlation coefficients were considered for all of the figures of merit in sensitivity analysis and coolant inlet temperature was consistently the most influential parameter. We used parameters as inputs to the critical heat flux calculation with the W-3 correlation were shown to be the most influential on the MDNBR, maximum fuel center-line temperature, and maximum outer clad surface temperature.
- Research Organization:
- Idaho National Laboratory (INL), Idaho Falls, ID (United States)
- Sponsoring Organization:
- USDOE Office of Nuclear Energy (NE)
- Grant/Contract Number:
- AC07-05ID14517
- OSTI ID:
- 1357750
- Alternate ID(s):
- OSTI ID: 1326416
- Report Number(s):
- INL/JOU-16-38314; PII: S0306454916302894
- Journal Information:
- Annals of Nuclear Energy (Oxford), Vol. 95, Issue C; ISSN 0306-4549
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Combining simulations and data with deep learning and uncertainty quantification for advanced energy modeling
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journal | August 2019 |
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