CORE SIM+: A flexible diffusion-based solver for neutron noise simulations

Artikel i vetenskaplig tidskrift, 2021

The paper presents CORE SIM+, a tool developed for diffusion-based neutron noise simulations. The simulator is based on the 3-dimensional, two-energy group neutron diffusion equation in the frequency domain. The tool includes the necessary solvers to calculate the criticality problems associated with the system and then the response to a variety of perturbations such as absorbers of variable strength, perturbations travelling with the coolant flow and vibrations of core components. Numerical methods suitable for different types of problems have been implemented. A capability that allows to apply non-uniform computational meshes is available so that the discretization of the domain can be optimized with respect to the characteristics of the neutron noise sources. The simulator can generate neutron noise databases for nuclear power reactors via the Green’s function method in a fully automated manner. These databases can be useful when studying the neutron noise response in a reactor and when training machine learning algorithms for core monitoring and diagnostics. As part of the verification process, the CORE SIM+ response of a realistic reactor model to a given perturbation at various frequencies is used to estimate the corresponding point-kinetic component, compared to an exact analytical expression. In addition, the Green’s function generator is used to calculate the response of a system to a fuel cell vibration. The solution is then compared to the one of the direct solver of CORE SIM+. As part of the validation work, a neutron noise experiment with vibrations of fuel rods is simulated, showing a reasonable agreement with the measurements. A representative neutron noise database generated for a generic pressurized water reactor is described. From the database the simulations of fuel assembly and core barrel vibrations are chosen as illustrative examples for demonstrating the capabilities of the tool.