Presentation Open Access
Detection and localisation of multiple in-core perturbations with neutron noise-based self-supervised domain adaptation
A. Durrant
Problem Case
• We aim to unfold reactor transfer function to provide core diagnostics.
• Derivation of core perturbation characteristics to classify and locate its origin.
• Yet this is challenging due to the limited number of neutron detectors in
western type reactors.
• We ask, can we use machine learning to successfully approximate the reactor
transfer function?
• However, to effectively train ML algorithms large quantities of data are
required.
More details can be found in the presentation file
Files
(4.8 MB)
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2021_Durrant_SAINT_presentation_V1.pdf
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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