Neutronics Study for AI Controlled Fusion Driven Transmutator

Neutronics study to determine the validity and real world extendibility through Artificial Intelligence (AI) optimization of a transmutation concept with the aim of significantly reducing the radiotoxicity of Spent Nuclear Fuel (SNF) together with its required storage duration and volume. In this transmutation concept (the transmutator) utilizes a novel source providing laser-generated neutrons to transmute transuranic elements separated from SNF and dissolved in a molten salt within a subcritical core. The source neutrons are generated via beam-target fusion whereas the beam is created by laser irradiation of nanometric foils through the Coherent Acceleration of Ions by Laser (CAIL) process. This relatively low deuteron energy is catapulted by fusion and eventually by secondary fission processes. A significant factor in this study is that this can be accomplished using relatively cheap fiber lasers terminating onto small scale targets. Consequently, this makes possible the use of multiple tunable and distributable neutron sources. Such a source has not previously been considered and encourages an investigation with the aid of AI into new spatial arrangement and temporal control operation strategies as done here. This source is combined with a molten salt core whose liquid state allows and facilitates homogeneity by mixing, safety, laser irradiation, in-situ processing, and monitoring of chemical and physical properties. The combined use of molten salt and laser also allows for the introduction of rapid feedback or feedforward control of the system’s operation. This further extends the transmutator concept as this work demonstrates here by neutronics simulations showing: Efficient and continuous Minor Actinide (MA) waste only burning through AI optimized and balanced processing scheduling without the use of isotopic separation. As well as shaped thermal reactivity insertion for increased tank usage efficiency and active thermal management