Presentation Open Access
Machine learning techniques for anomaly detection and for the alignment of perturbation simulations with power plant measurements
G. Alexandridis
Main Objectives
Detect anomalies in nuclear reactors using non‐intrusive methodologies
Anomalies
• Excessive vibrations of core internals
• Flow blockage
• Coolant inlet perturbations
• Combination of the above
• …
Non‐intrusiveness
• Measure the inherent fluctuations in neutron flux recorded by in‐core and ex‐core
detectors
• No external perturbation of the system is required
More information can be found in the presentation file
Files
(4.0 MB)
| Name | Size | |
|---|---|---|
|
2021_Alexandridis_SAINT_presentation_V1.pdf
md5:256170ae010345a9f352a805ae95bc5c |
4.0 MB | Download |
110
35
views
downloads
| All versions | This version | |
|---|---|---|
| Views | 110 | 110 |
| Downloads | 35 | 35 |
| Data volume | 141.7 MB | 141.7 MB |
| Unique views | 82 | 82 |
| Unique downloads | 34 | 34 |
Indexed in
- Publication date:
- January 13, 2021
- DOI:
-
- Grants:
-
European Commission:
- CORTEX - Core monitoring techniques and experimental validation and demonstration (754316)
- Meeting:
- SAINT Workshop AI/ML, 13-14 January 2021
- License (for files):
- Creative Commons Attribution 4.0 International