On the Well-Posedness of Green's Function Reconstruction via the Kirchhoff-Helmholtz Equation for One-Speed Neutron Diffusion

TL;DR

This paper develops a mathematical framework using the Kirchhoff-Helmholtz equation to reliably reconstruct neutron flux distributions in nuclear reactors from boundary measurements, ensuring the well-posedness of the inverse problem.

Contribution

It formulates the Green's function reconstruction as a well-posed inverse problem for the neutron diffusion model, including symmetry properties and numerical approaches for complex geometries.

Findings

Demonstrates existence and uniqueness of the Green's function from sampled data.

Provides a methodology for interpreting sensor data for flux reconstruction.

Ensures the reliability of neutron flux predictions in complex domains.

Abstract

This work presents a methodology for reconstructing the spatial distribution of the neutron flux in a nuclear reactor, leveraging real-time measurements obtained from ex-core detectors. The Kirchhoff-Helmholtz (K-H) equation inherently defines the problem of estimating a scalar field within a domain based on boundary data, making it a natural mathematical framework for this task. The main challenge lies in deriving the Green's function specific to the domain and the neutron diffusion process. While analytical solutions for Green's functions exist for simplified geometries, their derivation of complex, heterogeneous domains-such as a nuclear reactor-requires a numerical approach. The objective of this work is to demonstrate the well-posedness of the data-driven Green's function approximation by formulating and solving the K-H equation as an inverse problem. After establishing the symmetry properties that the Green's function must satisfy, the K-H equation is derived from the one-speed neutron diffusion model. This is followed by a comprehensive description of the procedure for interpreting sensor readings and implementing the neutron flux reconstruction algorithm. Finally, the existence and uniqueness of the Green's function inferred from the sampled data are demonstrated, ensuring the reliability of the proposed method and its predictions.