Reactor field reconstruction from sparse and movable sensors using Voronoi tessellation-assisted convolutional neural networks

摘要: The aging of operational reactors leads increased mechanical vibrations of reactor internals. The vibration of the in-core sensors near their nominal locations is a new issue for the neutronic fields reconstruction. Current field reconstruction methods fail to handle spatially moving sensors. In this work, we proposed a Voronoi tessellation techinque in combination with convolutional neural networks (V-CNN) to handle this challenge. The observations from movable in-core sensors are projected to the same global field structure, this projection is achieved with Voronoi tessellation, holding the magnitude and location information of sensors. The general convolutional neural networks were used to learn the map from observations to the global field. The proposed method is able to reconstruct the multi-physics fields (e.g., the fast flux, thermal flux and power rate) using observations from single field (e.g., thermal flux). Numerical tests based on IAEA benchmark proved its potential for real engineering usage, particularly, within an amplitude of 5 cm around nominal locations, the field reconstruction leads to average relative errors below 5 % and 10 % in $L_2$ norm and $L_{ infty}$ norm, respectively.