Many molten salt reactor concepts rely on so-called continuous salt management, which consists of continually inserting/removing fuel salt into/from the core to compensate for the loss of reactivity due to fuel depletion. In this PhD thesis, we propose to reconsider the molten salt reactor conceptual design in a so-called batch fuel management strategy that involves taking and loading a fraction of the volume of the core after a certain irradiation time and during a reactor maintenance. Contrary to a continuous strategy, the aim of this batch refueling strategy is to take into account technological constraints external to the reactor. The implications of a batch-fueled molten salt reactor with regard to core performances, cycle constraints and salt chemistry constitutes a largely unexplored area of research.

The doctoral student will first focus on evaluating the neutronic impacts of batch management with sensitivity studies (minor actinides burning performances, fuel regeneration performances, cycle time, volume/mass supply). The doctoral student will then focus on the cycle aspects of a molten salt reactor integrated into a given nuclear fleet, including the fresh fuel salt fabrication (actinides solubility) and the used salt reprocessing (process, cooling time, process time) with nuclear scenario calculations. The approach will evaluate the relevance of a batch-fueled molten salt reactor and will be applied to different reactors (burner, breeder) in comparison to continuous salt management.

The thesis will allow the candidate to develop skills in the conceptual design of a fourth generation reactor. He/she will be part of the scientific community working on such complex systems, which opens the door to a job in a R&D lab.

Multiphysics modeling is a powerful tool for analyzing nuclear reactors, but the uncertainty propagation between disciplines is often disregarded. This PhD thesis proposes innovative approaches to improve the accuracy of multiphysics modeling by accounting for these uncertainties. The primary goal is to propose optimal modeling approaches tailored to diverse accuracy requirements. This information is of prime interest to researchers and industry professionals involved in the development and utilization of multiphysics models. Specifically, the thesis will assess various uncertainty propagation techniques applicable to multiphysics simulations. This involves exploring surrogate modeling through avenues like reduced-order modeling and polynomial chaos expansion. The goal is to identify and categorize input parameters with the most significant impact on system outputs, irrespective of their physical domain. Subsequently, uncertainty propagation will be executed using two core modeling types: a ‘high-fidelity’ model based on the CEA's reference simulation tools and a ‘best-estimate’ model accounting for the "industrial" objective of the calculations). The similarities and differences between these approaches will be analyzed to assess model biases. These uncertainty evaluations employing the above methods will be tested on an extensive set of experiments performed in SEFOR, a sodium-cooled fast reactor, representing a diverse range of experimental data for various reactor conditions.