SUPR
Core monitoring and diagnostics in SMRs using neutron noise and machine learning
Dnr:

NAISS 2024/22-246

Type:

NAISS Small Compute

Principal Investigator:

Salma Hussein

Affiliation:

Chalmers tekniska högskola

Start Date:

2024-03-01

End Date:

2025-03-01

Primary Classification:

10399: Other Physics Topics

Webpage:

Allocation

Abstract

A technique is investigated for core monitoring and diagnostics applicable in future nuclear 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 nuclear 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 will make 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. The project is conducted within ANItA – Academic-industrial Nuclear technology Initiative to Achieve a sustainable energy future, which is financed by Swedish academia, the Swedish nuclear industry and the Swedish Energy Agency. https://research.chalmers.se/project/11339 https://www.chalmers.se/en/departments/physics/research/subatomic-high-energy-and-plasma-physics/reactor-physics-modeling-and-safety/