Published March 2017 | Version v1
Journal article

Predictive uncertainty reduction in coupled neutron-kinetics/thermal hydraulics modeling of the BWR-TT2 benchmark

  • 1. Karlsruhe Institute of Technology, Vincenz-Prießnitz-Str. 3, 76131 Karlsruhe (Germany)
  • 2. Center for Nuclear Science and Energy/Dept. of ME, University of South Carolina, 300 Main Street, Columbia, SC 29208 (United States)

Description

Highlights: • BWR Turbine Trip 2 (BWR-TT2) benchmark. • Substantial (up to 50%) reduction of uncertainties in the predicted transient power. • 6660 uncertain model parameters were calibrated. - Abstract: By applying a comprehensive predictive modeling methodology, this work demonstrates a substantial (up to 50%) reduction of uncertainties in the predicted total transient power in the BWR Turbine Trip 2 (BWR-TT2) benchmark while calibrating the numerical simulation of this benchmark, comprising 6090 macroscopic cross sections, and 570 thermal-hydraulics parameters involved in modeling the phase-slip correlation, transient outlet pressure, and total mass flow. The BWR-TT2 benchmark is based on an experiment that was carried out in 1977 in the NPP Peach Bottom 2, involving the closure of the turbine stop valve which caused a pressure wave that propagated with attenuation into the reactor core. The condensation of the steam in the reactor core caused by the pressure increase led to a positive reactivity insertion. The subsequent rise of power was limited by the feedback and the insertion of the control rods. The BWR-TT2 benchmark was modeled with the three-dimensional reactor physics code system DYN3D, by coupling neutron kinetics with two-phase thermal-hydraulics. All 6660 DYN3D model parameters were calibrated by applying a predictive modeling methodology that combines experimental and computational information to produce optimally predicted best-estimate results with reduced predicted uncertainties. Simultaneously, the predictive modeling methodology yields optimally predicted values for the BWR total transient power while reducing significantly the accompanying predicted standard deviations.

Availability note (English)

Available from http://dx.doi.org/10.1016/j.nucengdes.2016.12.011

Additional details

Identifiers

DOI
10.1016/j.nucengdes.2016.12.011;
PII
S0029-5493(16)30497-6;

Publishing Information

Journal Title
Nuclear Engineering and Design
Journal Volume
313
Journal Page Range
p. 330-344
ISSN
0029-5493
CODEN
NEDEAU

Optional Information

Copyright
Copyright (c) 2016 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.