Detection and localisation of multiple in-core perturbations with neutron noise-based self-supervised domain adaptation
Paper i proceeding, 2021

The use of non-intrusive techniques for monitoring nuclear reactors is becoming more vital as western fleets age. As a consequence, the necessity to detect more frequently occurring operational anomalies is of upmost interest. Here, noise diagnostics — the analysis of small stationary deviations of local neutron flux around its time-averaged value — is employed aiming to unfold from detector readings the nature and location of driving perturbations. Given that in-core instrumentation of western-type light-water reactors are scarce in number of detectors, rendering formal inversion of the reactor transfer function impossible, we propose to utilise advancements in Machine Learning and Deep Learning for the task of unfolding. This work presents an approach to such a task doing so in the presence of multiple and simultaneously occurring perturbations or anomalies. A voxel-wise semantic segmentation network is proposed to determine the nature and source
location of multiple and simultaneously occurring perturbations in the frequency domain. A diffusion-based core simulation tool has been employed to provide simulated training data for two reactors. Additionally, we work towards the application of the aforementioned approach to real measurements, introducing a self-supervised domain adaptation procedure to align the representation distributions of simulated and real plant measurements.

neutron noise

core monitoring

core diagnostics

machine learning

Författare

A. Durrant

University of Lincoln

G. Leontidis

University of Lincoln

S. Kollias

University of Lincoln

L.A. Torres

Universidad Politecnica de Madrid

C. Montalvo

Universidad Politecnica de Madrid

Antonios Mylonakis

Chalmers, Fysik, Subatomär, högenergi- och plasmafysik

Christophe Demaziere

Chalmers, Fysik, Subatomär, högenergi- och plasmafysik

Paolo Vinai

Chalmers, Fysik, Subatomär, högenergi- och plasmafysik

Proc. Int. Conf. Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C2021)

Int. Conf. Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C2021)
Online, USA,

Core monitoring techniques and experimental validation and demonstration (CORTEX)

Europeiska kommissionen (EU) (754316CORTEX), 2017-09-01 -- 2021-08-31.

Styrkeområden

Energi

Ämneskategorier

Annan fysik

DOI

10.13182/M&C21-33650

Mer information

Skapat

2021-10-16