Determining the fuel assemblies bowing of the VVER-1000 reactor by using an artificial neural network and C-shape bowing assumption

FAs (Fuel Assembly) lateral deformation under mechanical, hydraulic, thermal, and radiation loads in the reactor core is called FA bowing. This phenomenon has different consequences for reactor safety and its operation. Bowing causes the local change in distance between the FAs, which leads to a neutron perturbation that affects the power distribution and its asymmetry. This research determines the bowing pattern of FAs in the VVER-1000 reactor core by using an artificial neural network and C-shape bowing assumption. That will be done based on power distribution caused by the presence of FA bowing. This issue will help the operator design the arrangement of FAs in the next cycle by knowing the bowing pattern in the reactor core. It will prevent the expansion of the bowing and its consequences in the next fuel cycles.