The simulation and analysis of neutronics parameters in Molten Salt Reactors (MSRs) is fundamental for the design of the reactor core. However, high-fidelity neutron transport calculations of the MSR are time-consuming and require significant computational resources. Artificial neural networks (ANNs) have been used in various industries, and in recent years are increasingly introduced in the nuclear industry. Back-Propagation neural network (BPNN) is one type of ANN. A surrogate model based on BP neural network is developed to quickly predict two key neutronics parameters in reactor core: the effective multiplication factor ($k_{\mathrm{eff}}$) and the three-dimensional channel-by-channel neutron flux distribution. The dataset samples are generated by modeling and simulating different operation states of the Molten Salt Reactor Experiment (MSRE) using the Monte Carlo code. Hyper-parameters optimization is performed to obtain the optimal surrogate model. The numerical results on the test dataset show good agreement between the surrogate model and the Monte Carlo code. Additionally, the surrogate model significantly reduces computation time compared to the Monte Carlo code and greatly enhances efficiency. The feasibility and advantages of the proposed surrogate model is demonstrated, which has important significance for real-time prediction and design optimization of the reactor core.

The transmutation of transuranic (TRU) elements produced by pressurized water reactors (PWRs) can effectively reduce their radioactive hazards. The molten chloride salt fast reactor (MCSFR) is a type of liquid-fueled molten salt reactor (MSR) using fuel in the form of molten chloride salts. The MCSFR utilizing a fast neutron spectrum and high actinide fraction is considered to be a potential reactor type for TRU transmutation. An online refueling and reprocessing scenario is the unique feature of liquid-fueled MSRs. On account of this characteristic, a new fuel management code named ThorNEMFM with a nodal expansion method (NEM) was developed and validated with the molten salt breeder reactor (MSBR) and the molten salt fast reactor (MSFR) benchmarks. Then, the transmutation and breeding performances of the MCSFR were simulated and analyzed with the ThorNEMFM code. The MCSFR adopts TRU elements as initial fissile loads and online feeding fissile materials. The results show that the transmutation ratio of TRU elements in the MCSFR can reach 50%, and the breeding ratio can reach 1.359. Moreover, the MCSFR has low radiotoxicity due to lower buildup of fission products (FPs).