Grant: Alliance Grants

Small modular reactors (SMR) feature unusual fuel compositions (e.g. plutonium, enriched uranium, reprocessed spent fuel), new physical fuel forms (hexagonal lattices, molten salts, TRISO particles, etc.) and different neutron energies in the core (fast spectra in Advanced Reactor Concept (ARC-100) and some molten salt reactors). A consequence of these differences is that unusual nuclear material accountancy is expected so that resulting spent fuel from SMR is expected to be very different when compared with spent fuel from Canada Deuterium Uranium (CANDU) reactors.Over the last 40 years, Polytechnique Montréal has developed the computer codes DRAGON5 (lattice code) and DONJON5 (full-core simulation code) for deterministic nuclear reactor physics simulations.This research proposal consists in extending and applying DRAGON5 and DONJON5 to new reactor physics applications specific to SMR. Specific contributions are expected:(1) Gather information related to geometries, materials and operational data used in selected SMR concepts (sodium fast reactor, high temperature fast reactor and molten salt reactor);(2) Adapt the resonance self-shielding methodology in DRAGON5 to the specific SMR fuel (molten salt, TRISO particles, etc.);(3) Apply the Bateman solver in DRAGON5 and DONJON5 to SMR fuel;(4) Include SMR characteristics in the CLASS (Core Library for Advanced Simulation Scenarios) scenario simulator. CLASS is a dynamic fuel cycle simulation code based on neural networks that can use DRAGON5/DONJON5 reactor models to produce nuclear data and physical quantities. CLASS allows the analysis of complex nuclear power plants and of their constituting components.The combined benefits of this research will enable the optimization of the SMR deployment in such a way as to minimize radiological risks, to reduce proliferation issues, to evaluate reprocessing capabilities and to better understand the outcome of an accident.