Investigation Into the Use of Machine Learning Assisted Prediction of Nodal Parameters for Reduced Order Neutronic Simulation Models
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Engineering topics
Publications and source records attributed to Gentry, Cole.
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MPACT is a whole-core 3D neutron transport code jointly developed by Oak Ridge National Laboratory and the University of Michigan to perform high-fidelity light water reactor (LWR) analysis. Its capability was recently extended for gas-cooled reactor analysis by coupling to the thermo-fluids code AGREE for thermal feedback. This paper presents the MPACT-AGREE code coupling for the simulation of MAGNOX-type gas-cooled graphite-moderated reactors. A coupling interface has been developed, along with methods to accurately simulate MAGNOX type reactors. The coupling mechanics were tested using two problems derived from the Calder Hall reactor. Simulations of these models demonstrated that the codes have been successfully coupled and can provide reasonable results in a tractable simulation time.
The goal of this document is to provide a set of comprehensive benchmarking neutronics problems for gas-cooled, graphite-moderated reactors. The problems are designed to be representative problems that increase in complexity to test performance of various codes. For the simulation of the benchmarking problems, the Shift Monte Carlo code and MPACT deterministic code are chosen because of their state-of-the-art capabilities, which are optimized for various reactor designs. Because these codes have been developed primarily for light water reactor applications, their application to graphite-moderated, gas-cooled reactors has not been extensively explored. This report attempts to evaluate benchmarks for a Magnox-style reactor, one that is graphite-moderated and gas-cooled. Parameters that are calculated and presented in this benchmark include the eigenvalue, peaking factors, and isotope concentrations. Results show good agreement between Shift and MPACT, and demonstrate the capability of MPACT to model graphite-moderated systems well.