Digital twin-based hybrid framework for steam generator clogging prognostics

TL;DR

This paper introduces a hybrid digital twin framework that integrates physics-based modeling, observational data, and uncertainty quantification to predict steam generator clogging and remaining useful life in nuclear reactors.

Contribution

It presents a novel hybrid prognostic framework combining physics simulation, data, and uncertainty methods for steam generator maintenance planning.

Findings

Effective estimation of steam generator remaining useful life.

Robust clogging prognosis using heterogeneous observational data.

Framework compatible with digital twin platforms for nuclear plant maintenance.

Abstract

We present a hybrid framework to support prognostics of the clogging degradation phenomenon in tube support plates for digital twins of steam generators in pressurized water reactors. The proposed approach combines a physics-based simulation code, heterogeneous and sparse observational data, and several uncertainty quantification techniques to obtain a robust estimate of the steam generator remaining useful life associated with the clogging rate. The proposed framework is compatible with a digital twin platform to assist maintenance planning of EDF steam generators.