Digital twin-based hybrid framework for steam generator clogging prognostics

2026-04-21

AI Summary

This study addresses the challenge of predicting the remaining useful life (RUL) of pressurized water reactor steam generator tubes degraded by sludge accumulation on tube support plates. To tackle this problem, the authors propose a hybrid digital twin framework that integrates high-fidelity physics-based simulations, multi-source sparse observational data, and advanced uncertainty quantification techniques within a unified platform. This approach enables robust modeling of the sludge-induced degradation process and delivers reliable RUL estimates by synergistically combining physical insights with heterogeneous, sparsely sampled field data. The framework significantly enhances prediction robustness and has been successfully deployed to support maintenance decision-making for Électricité de France (EDF) steam generators, offering a novel paradigm for precision operation and maintenance of critical nuclear power plant components.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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.

Problem

• digital twin
• steam generator
• clogging prognostics
• remaining useful life
• pressurized water reactor

Innovation

• digital twin
• clogging prognostics
• physics-based simulation
• uncertainty quantification
• remaining useful life