Abstract
During a Spring 2019 refueling outage, Plant A, a pressurized water reactor (PWR), experienced difficulty removing the reactor vessel upper internals because of degraded thermal shield (TS) support block bolts (TSSBBs), TS flexures, and baffle-former bolts (BFBs). None of the degradation was expected based on prior industry operating experience. Dynamic finite element analyses (FEA) and neutron noise monitoring were performed to diagnose the degraded condition. The FEA simulated the indications from the 2019 outage to assess the effect that degradation would have on the dynamic response of the lower reactor vessel internals. In parallel, neutron noise data was collected and evaluated to characterize the vibratory signature of the Plant A lower internals. The results of the FEA and neutron noise monitoring were coupled to compare degradation scenarios with the measured data, and it was thus predicted that multiple non-functional (fully failed) flexures existed at Plant A. Those predictions were subsequently validated during the Fall 2020 outage.
This use of neutron noise to detect and diagnose degradation represents the practice of proactive structural health monitoring (SHM) for reactor vessel internals. This remote condition monitoring and diagnostic computational framework is currently being enhanced by quantifying the sensitivity of the structural dynamics to different degradation scenarios. This methodology leverages benchmarked FEA models and machine learning methods to enhance interpretability of neutron noise results and proactively diagnose different degradation scenarios, thus enabling prognostic asset management for reactor structures.
