ML24276A001
| ML24276A001 | |
| Person / Time | |
|---|---|
| Issue date: | 10/02/2024 |
| From: | Raj Iyengar NRC/RES/DE |
| To: | |
| Raj Iyengar 301-415-0770 | |
| References | |
| Download: ML24276A001 (11) | |
Text
Regulatory Implications of Advanced Technologies for Component Condition Monitoring in Nuclear Energy Systems Raj Iyengar Office of Nuclear Regulatory Research U.S. Nuclear Regulatory Commission The views expressed are those of the authors and do not reflect the views of the U.S. Nuclear Regulatory Commission. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for public release; distribution is unlimited.
OBJECTIVES
- Demonstrate the application of digital twins (DT) for mechanical system condition monitoring in nuclear power plants (NPPs)
- Gain knowledge and confidence
- Modeling and simulation for digital twins
- Obtain insights
- Support future regulatory application reviews and develop guidance, as needed
DIGITAL TWIN SYSTEM - NPP SCENARIO
- Four main elements to an NPP-DT system
- 1. An NPP
- 2. A Digital Twin (DT)
- 3. Sensor and performance data flowing from NPP to DT
- 4. Actions and recommendations flowing from DT to NPP
DIGITAL TWIN CHARACTERISTICS & CAPABILITIES
ANOMALY DETECTION CASE STUDY: Simulator synthetic data
- Full scope BWR: Represents the full set of plant components and operations
- Physics-based model: Parameters are calculated using first principles
- Real-time data: Operates in real time just like an actual plant
- Initial conditions: Can set the initial conditions of the plant
- Malfunctions: Variety of malfunctions that can be inserted at a specified time and severity
- Output: Records time-series data of selected plant parameters and malfunctions Probabilistic Safety Assessment and Management (PSAM) 2023 Topical Conference on Artificial Intelligence (AI) and Risk Analysis, October 2325, 2023.
https://www.ideals.illinois.edu/items/128985
ANOMALY DETECTION CASE STUDY: Trained LSTM autoencoder Probabilistic Safety Assessment and Management (PSAM) 2023 Topical Conference on Artificial Intelligence (AI) and Risk Analysis, October 2325, 2023.
https://www.ideals.illinois.edu/items/128985
ANOMALY DETECTION CASE STUDY: Identified inserted anomalies Probabilistic Safety Assessment and Management (PSAM) 2023 Topical Conference on Artificial Intelligence (AI) and Risk Analysis, October 2325, 2023.
https://www.ideals.illinois.edu/items/128985 Malfunctions Modeled Recirc Pump A Runaway Recirc Pump B Runaway Recirc Pump A Seal #1 Failure Recirc Pump B Seal #1 Failure Recirc Pump A Seal #2 Failure Recirc Pump B Seal #2 Failure RBCCW Heat Exchanger Tube Leak
IST AND CONDITION MONITORING CASE STUDIES
- Case studies for condition monitoring of mechanical systems and components
- Reactor coolant pump (RCP)
- Heat Pipe
- AI/ML explainability
- Influence of input features to model output INL/EXT-20-60782 INL/EXT-17-43212 Insights from these case studies will help prepare NRC staff to evaluate applications of condition monitoring to meet IST requirements
DIGITAL TWINS PROJECT ACCOMPLISHMENTS 8
6
- 6 Technical Letter Reports
- 2 Public workshops
- Ongoing research Future Focused Research Project ML22192A046
Technical Letter Reports ML23058A085 ML22235A643 ML21361A261
ML21160A074
ML2108A132 Information Letters Follow-on Report ML23271A055
ML24065A049
INNOVATION AT THE NRC:
Digital Twins for Condition Monitoring
THANK YOU https://www.nrc.gov/reactors/power/digital-twins.html