Mechanics Informed Neutron Noise Monitoring to Perform Remote Condition Assessment for Reactor Vessel Internals

Abstract

Use of neutron noise analysis in pressurized water reactors to detect and diagnose degradation represents the practice of proactive structural health monitoring for reactor vessel internals. Recent enhancements to this remote condition monitoring and diagnostic computational framework quantify the sensitivity of the structural dynamics to different degradation scenarios. This methodology leverages benchmarked computational structural mechanics models and machine learning methods to enhance interpretability of neutron noise measurement results. The novelty of the methodology lies not in the particular technologies and algorithms but in our amalgamation into a holistic computational framework for structural health monitoring. Recent experience revealed successful deployment of this methodology to proactively diagnose different degradation scenarios, thus enabling prognostic asset management for reactor structures.