EVALUATION OF CRITICAL EXPERIMENTS IN THE UNIVERSITY OF WISCONSIN NUCLEAR REACTOR (UWNR) WITH UNCERTAINTY QUANTIFICATION

¹ Computational Nuclear Engineering Research Group, Department of Engineering Physics, University of Wisconsin-Madison 1415 Engineering Drive, Madison, WI 53705, US
² Computation and Optimization for Reactor Physics Simulation Group, Department of Mechanical and Nuclear Engineering, Kansas State University 3002 Rathbone Hall, Manhattan, KS 66506, US
³ Reactor Physics Analysis and Design Department, Idaho National Laboratory Idaho Falls, ID 83415, US

ypark234@wisc.edu
yc4v7@ksu.edu
rababelzohery@ksu.edu
paul.wilson@wisc.edu
jaroberts@ksu.edu
mark.dehart@inl.gov

Published online: 22 February 2021

Abstract

An improved computational model of the University of Wisconsin Nuclear Reactor (UWNR) was developed to support the benchmark evaluation of recent data acquired during an experimental campaign conducted at UWNR. Previous efforts led to a scripted UWNR model for automated generation of MCNP6 and Serpent inputs. This capability was extended to SCALE/KENO. All three tools were used to evaluate a variety of zero-power, fresh-critical configurations, and the results agreed well. The MCNP6 model was extended to support shuffling the core configuration, which allows the modeling of burnup for evaluation of depleted critical configurations. The MCNP6 model successfully predicts core reactivity over time, after accounting for the initial reactivity bias. The inclusion of SCALE/KENO input generation enables sensitivity and uncertainty analyses using the TSUNAMI and Sampler modules of SCALE. A preliminary uncertainty analysis was performed with TSUNAMI for nuclear data uncertainties while direct perturbation calculations were performed using MCNP6 for geometry and material uncertainties, which helped to identify model parameters with the largest effect on the eigenvalue. A transient UWNR transport Model in Mammoth/Rattlesnake is under development to simulate the transient experiments. The existing MCNP6 and Serpent models are used to provide the CAD file for meshing and homogenized cross-sections. In conclusion, the evaluation of UWNR benchmark data provides increased confidence in various states of the UWNR computational model and will provide a unique model for use by other analysts.