Application of regression, variance, and density based global sensitivity methods to integrated VERA-CS and BISON simulations

作者：Blakely Cole; Zhang Hongbin^*; Folsom Charlie; Ban Heng; Szilard Ronaldo

来源：Nuclear Engineering and Design, 2018, 332: 186-201.

DOI：10.1016/j.nucengdes.2018.03.023

• Multiphysics environment
• Uncertainty quantification
• Sensitivity analysis
• VERA-CS
• BISON

Uncertainty quantification (UQ) and sensitivity analyses (SA) are performed for coupled simulations between VERA-CS, a coupled pin resolved neutron transport and subchannel thermal hydraulics code, and the fuel performance code BISON. The interface between VERA-CS and BISON is performed in a multiphysics environment known as the LOCA Toolkit for U.S. light water reactors (LOTUS) currently under development at Idaho National Laboratory (INL). A focus is placed on using a variety of SA measures, including two regression based (Pearson and Spearman), one variance based (Sobol indices), and three moment independent measures (Delta moment independent measures with L-1, L-2, and L-infinity norms). The problem under inspection is a single assembly depletion case for three fuel cycles. The figures of merit are the minimum departure from nucleate boiling ratio (MDNBR), maximum fuel centerline temperature (MFCT), and gap conductance at peak power (GCPP). SA results show MDNBR to be linear with consistent rankings throughout the fuel cycles. MFCT is linear, but with a change in rankings at the switch from open gap to closed gap models. GCPP is nonlinear at intermediate states that coincide with the onset of contact between fuel and cladding. These nonlinear states allow for the show-casing of higher order SA measures over first order methods.

• 出版日期2018-6