Subjects: Physics >> Nuclear Physics submitted time 2024-01-02
Abstract： 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.
Subjects: Physics >> Nuclear Physics Subjects: Mathematics >> Computational Mathematics. submitted time 2023-09-29
Abstract： Machine learning-based modeling of reactor physics problems has attracted increasing interest in recent years.
Despite some progress in one-dimensional problems, there is still a paucity of benchmark studies that are easy
to solve using traditional numerical methods albeit still challenging using neural networks for a wide range
of practical problems. We present two networks, namely the Generalized Inverse Power Method Neural Net#2;
work (GIPMNN) and Physics-Constrained GIPMNN (PC-GIPIMNN) to solve K-eigenvalue problems in neu#2;
tron diffusion theory. GIPMNN follows the main idea of the inverse power method and determines the lowest
eigenvalue using an iterative method. The PC-GIPMNN additionally enforces conservative interface condi#2;
tions for the neutron flux. Meanwhile, Deep Ritz Method (DRM) directly solves the smallest eigenvalue by
minimizing the eigenvalue in Rayleigh quotient form. A comprehensive study was conducted using GIPMNN,
PC-GIPMNN, and DRM to solve problems of complex spatial geometry with variant material domains from
the field of nuclear reactor physics. The methods were compared with the standard finite element method. The
applicability and accuracy of the methods are reported and indicate that PC-GIPMNN outperforms GIPMNN
and DRM.
Hits
YES
Hits
Downloads
Comment