Core monitoring and diagnostics in SMRs using reactor neutron noise and machine learning

Research Project, 2023 –

A technique is investigated for core monitoring and diagnostics applicable in future Small Modular Reactors (SMRs). The technique will rely on the analysis of reactor neutron noise, i.e., the small, stationary fluctuations of the neutron flux in the reactor core. These fluctuations are always present and are related to different types of physical phenomena. Following the evolution of neutron noise in time allows to identify and correct promptly possible perturbations that might negatively impact the operation and safety of the reactor. The technique makes use of computational tools to characterize the neutron noise response of the system to perturbations and a machine learning algorithm for the inverse problem that determines the perturbations from the measured system response. The outcome of this project will ultimately support the design and instrumentation of SMRs, before their construction and exploitation. This research project is conducted within ANItA – Academic-industrial Nuclear technology Initiative to Achieve a sustainable energy future, which is coordinated by Uppsala University and is financed by Swedish academia, the Swedish nuclear industry and the Swedish Energy Agency.