Heat Source Characterization in a TREAT Fuel Particle Using Coupled Neutronics-BCMC Calculations
This work presents a multi-physics, multi-scale approach to modeling the Transient Test Reactor (TREAT) currently prepared for restart at the Idaho National Laboratory. TREAT fuel is made up of microscopic fuel grains (r ˜ 20µm) dispersed in a graphite matrix. The novelty of this work is in coupling a binary collision Monte-Carlo (BCMC) model to the Finite Element based code Moose for solving a microsopic heat-conduction problem whose driving source is provided by the BCMC model tracking fission fragment energy deposition. This microscopic model is driven by a transient, engineering scale neutronics model coupled to an adiabatic heating model. The macroscopic model provides local power densities and neutron energy spectra to the microscpic model. Currently, no feedback from the microscopic to the macroscopic model is considered. TREAT transient 15 is used to exemplify the capabilities of the multi-physics, multi-scale model, and it is found that the average fuel grain temperature differs from the average graphite temperature by 80 K despite the low-power transient. The large temperature difference has strong implications on the Doppler feedback a potential LEU TREAT core would see, and it underpins the need for multi-physics, multi-scale modeling of a TREAT LEU core.
- Research Organization:
- Idaho National Lab. (INL), Idaho Falls, ID (United States)
- Sponsoring Organization:
- USDOE Office of Nuclear Energy (NE)
- DOE Contract Number:
- DE-AC07-05ID14517
- OSTI ID:
- 1369423
- Report Number(s):
- INL/CON-16-40179-Rev001
- Resource Relation:
- Conference: M&C 2017 International Conference on Mathematics & Computational Methods Applied to Nuclear Science & Engineering, Jeju, Republic of Korea (South Korea), 04/16/2017 - 04/20/2017
- Country of Publication:
- United States
- Language:
- English
Similar Records
Multi-Physics Simulation of TREAT Kinetics using MAMMOTH
A Coupled Multiscale Approach to TREAT LEU Feedback Modeling Using a Binary-Collision Monte-Carlo–Informed Heat Source