Impact of physical model error on State Estimation for neutronics applications

TL;DR

This paper examines how inaccuracies in physical models affect the accuracy of neutron flux state estimation in nuclear power plants, using a PDE-based approach to quantify the impact of model errors.

Contribution

It introduces an analysis of model error effects in neutron flux estimation using the Parametrized Background Data Weak method with diffusion models.

Findings

Model errors significantly impact power field reconstruction accuracy.

Diffusion models may not fully capture neutron transport physics, leading to estimation inaccuracies.

Quantitative assessment of model error influence on state estimation.

Abstract

In this paper, we consider the inverse problem of state estimation of nuclear power fields in a power plant from a limited number of observations of the neutron flux. For this, we use the Parametrized Background Data Weak approach. The method combines the observations with a parametrized PDE model for the behavior of the neutron flux. Since, in general, even the most sophisticated models cannot perfectly capture reality, an inevitable model error is made. We investigate the impact of the model error in the power reconstruction when we use a diffusion model for the neutron flux, and assume that the true physics are governed by a neutron transport model.