UNCERTAINTY CHARACTERIZATION FRAMEWORK FOR STEADY-STATE AND TRANSIENT NEUTRONICS SIMULATIONS OF A CANDU REACTOR

Author:

Abdel-Khalik Hany S.,Trottier Alexandre,Serghiuta Dumitru,Huang Dongli

Abstract

This paper reports on the development and testing of a comprehensive few-group cross section input uncertainty library for the NESTLE-C nodal diffusion-based nuclear reactor core simulator. This library represents the first milestone of a first-of-a-kind framework for the integrated characterization of uncertainties in steady-state and transient CANDU reactor simulations. The objective of this framework is to propagate, prioritize and devise a mapping capability for uncertainties in support of model validation of best-estimate calculations. A complete framework would factor both input and modeling uncertainty contributions. The scope of the present work is limited to the propagation of multi-group cross-section uncertainties through lattice physics calculations down to the few-group format, representing the input to the NESTLE-C core simulator, and finally to core responses of interest.

Publisher

EDP Sciences

Reference14 articles.

1. Abdel-Khalik H.S., “Feasibility study of an integrated framework for characterization of uncertainties with application to CANDU steady state and transient reactor physics simulation”, Final Report RSP 598.1, June 2015, http://www.nuclearsafety.gc.ca/eng/pdfs/feasibility-study-integrated-framework.pdf

2. Serghiuta D., Tholammakkil J., Abdel-Khalik H.S. and Trottier A., 2017. Integrated Framework for Propagation of Uncertainties in Nuclear Cross-Sections in CANDU Steady-State and Transient Reactor Physics Simulations. 37th Annual Conference of the Canadian Nuclear Society, June 2017.

3. Serghiuta D., Tholammakkil J., and Abdel-Khalik H.S., “BEPU and Evaluation of Predictive Capability of Physics Simulations of CANDU Transients”, ANS Best Estimate Plus Uncertainty International Conference (BEPU 2018), Real Collegio, Lucca, Italy, May 13-19, 2018

4. Hybrid reduced order modeling applied to nonlinear models

5. Huang D., Abdel-Khalik H., Rabiti C., and Gleicher F., “Dimensionality Reducibility for Multi-Physics Reduced Order Modeling,” Annals of Nuclear Energy, 110, December 2017.

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