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Final Doctoral Defense: Andrei Rykhlevskii, PhD Candidate

Event Type
Department of Nuclear, Plasma, and Radiological Engineering
Jul 1, 2020   9:00 am  
Andrei Rykhlevskii, PhD Candidate
Free and Open to the Public
Originating Calendar
NPRE Events

Fuel Processing Simulation Tool for Liquid-Fueled Nuclear Reactors


Abstract:  Nuclear reactors with liquid fuel offer multiple advantages over their solid-fueled siblings: improved inherent safety, fuel utilization, thermal efficiency, online reprocessing, and potential for nuclear fuel cycle closure. Interest in advanced liquid-fueled nuclear systems, particularly Molten Salt Reactors (MSRs), has grown recently with numerous new commercial MSR concepts. However, most contemporary reactor physics software is incapable of performing fuel depletion calculations for such advanced reactors. To further develop liquid-fueled reactor designs, researchers need a simulation tool for performing fuel depletion calculations while taking into account online fuel reprocessing and refueling. Current modeling efforts in the literature usually assume ideal (e.g., 100% of neutron poison being removed) rather than realistically-constrained removal efficiency.


This work aims to provide a flexible tool, SaltProc, for simulating the fuel depletion in a generic nuclear reactor with liquid, circulating fuel. SaltProc allows the user to specify realistically constrained extraction efficiency of fission products based on physical models of fuel processing components appearing in various MSR systems. Moreover, SaltProc can maintain reactor criticality by adjusting the geometry of the core. This work demonstrates and validates SaltProc for lifetime-long and short-term depletion calculation in two MSR designs: the Molten Salt Breeder Reactor (MSBR) and the Transatomic Power (TAP) MSR. SaltProc successfully captured the evolution of fuel salt composition and major safety parameters (temperature and void coefficient of reactivity, shutdown margin) during reactor operation with various noble gas extraction efficiencies. Finally, a simple uncertainty propagation via Monte Carlo depletion calculations in this work shows that the nuclear data-related error (0.5-8% depending on the isotope) is two orders of magnitude greater than the stochastic error (< 0.07%).

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