No abstract

No abstract

Three advanced nuclear power systems use liquid salt coolants that generate tritium and thus face the common challenges of containing and capturing tritium to prevent its release to the environment. The fluoride salt-cooled high-temperature reactor (FHR) uses clean fluoride salt coolants and the same graphite-matrix coated-particle fuel as high-temperature gas-cooled reactors. Molten salt reactors (MSRs) dissolve the fuel in a fluoride or chloride salt with release of fission product tritium into the salt. In most FHR and MSR systems, the baseline salts contain lithium where isotopically separated 7 Li is proposed to minimize tritium production from neutron interactions with the salt. The Chinese Academy of Sciences plans to start operation of a 2-MW(thermal) molten salt test reactor by 2020. For high-magnetic-field fusion machines, the use of lithium enriched in 6 Li is proposed to maximize tritium generation-the fuel for a fusion machine. Advances in superconductors that enable higher power densities may require the use of molten lithium salts for fusion blankets and as coolants. Recent technical advances in these three reactor classes have resulted in increased government and private interest and the beginning of a coordinated effort to address the tritium control challenges in 700°C liquid salt systems. We describe characteristics of salt-cooled fission and fusion machines, the basis for growing interest in these technologies, tritium generation in molten salts, the environment for tritium capture, models for high-temperature tritium transport in salt systems, alternative strategies for tritium control, and ongoing experimental work. Several methods to control tritium appear viable. Limited experimental data are the primary constraint for designing efficient cost-effective methods of tritium control.

Molten salts have attracted interest as potential heat carriers and/or fuel solvents in the development of new Gen IV nuclear reactor designs, high-temperature batteries, and thermal energy storage. In nuclear engineering, salts containing lithium fluoride-based compounds are of particular interest due to their ability to lower the melting points of mixtures and their compatibility with alloys. A machine learning potential (MLP) combined with a molecular dynamics study is performed on two popular molten salts, namely, LiF (50% Li) and FLiBe (66% LiF and 33% BeF 2 ), to predict the thermodynamic and transport properties, such as density, diffusion coefficients, thermal conductivity, electrical conductivity, and shear viscosity. Due to the large possibilities of atomic environments, we employ training using Deep Potential Smooth Edition (DPSE) neural networks to learn from large datasets of 141,278 structures with 70 atoms for LiF and 238,610 structures with 91 atoms for FLiBe molten salts. These networks are then deployed in fast molecular dynamics to predict the thermodynamic and transport properties that are only accessible at longer time scales and are otherwise difficult to calculate with classical potentials, ab initio molecular dynamics, or experiments. The prospect of this work is to provide guidance for future works to develop general MLPs for high-throughput thermophysical database generation for a wide spectrum of molten salts.