Real-Time Canister Welding Health Monitoring and Prediction System for Spent Fuel Dry Storage

Storage of spent nuclear fuel (SNF) is occurring for longer periods than initially intended. It is important to assess the lifetime extension of dry storage canisters (DSC) with the ability to accurately monitor and predict material degradation so that corrective maintenance actions can be taken. The possibility of chloride-induced stress corrosion cracking (CISCC) in welded stainless-steel DSC for SNF has been identified as a potential concern regarding long- term performance of the canister’s containment boundary. Improved understanding of the risk of SCC to interim storage canisters has been identified as a critical data gap in analyses by the DOE, nuclear waste technical review board (NWTRB), and electric power research institute (EPRI). The welding procedure introduces high tensile residual stress and sensitization in the heat-affected zone (HAZ) during the canister fabrication. The HAZ might render the welds susceptible to the incubation of pitting and transition to crack initiation and growth when exposed to aggressive environment. Analysis of canister surface samples from in-service DSCs at three near-marine ISFSI sites have demonstrated the presence of chloride-rich salts on the outer canister surfaces. DOE is funding development work to address the NDE and characterization of CISCC for stainless steel interim storage canisters within their overpacks. There is a critical need for advanced monitoring system and analysis tools to appropriately capture and process massive amount of measurement data and extract timely, actionable information in order to provide better canister health awareness. Intelligent Automations, Inc. (IAI), along with Oak Ridge National Laboratory (ORNL) and Orano TN, proposes to develop and test an innovative multi-sensor network with Machine Learning (ML) system called CanisterMonitorTM for in-situ DSC CISCC diagnosis and prognosis. The system incorporates acoustic emission, temperature, humidity, eddy current, and visual camera sensors to real-time monitor the canister health and a machine learning tool for predictive detection and interpretation of pits and cracks on canister. IAI will work closely with ORNL to address the HAZ residue stress and corrosion environment for canister repaired weld. We will collect representative AE, eddy current and visual imaging data for training and validation of the ML tool in an accelerated corrosion test on repaired weld specimens. IAI and the team of collaborators will work closely with DOE to define the specific requirements, scenarios, and performance matrices for building the system architecture. We will design and develop CanisterMonitorTM system components include sensor network, overall platform development, control software and database. We will Perform accelerated environmental corrosion tests and collect damage precursors on representative weld repaired samples and experiments and implement prototype systems for crack signal collection during environmental exposure. We will implement the signal processing and machine learning algorithm to predict and interpret the CISCC and demonstrate the CanisterMonitorTM system with lab test, study ROI/BCA. We will work with DoE and Orano TN to develop the plans for the Phase II program and transition of the monitoring system.The NRC is also seriously considering the implementation of monitoring system for nuclear waste management. The CanisterMonitorTM technology is applicable to multiple programs across the DoE, for example, office of environmental management, office of nuclear energy. The market for spent fuel and nuclear waste management system market is estimated at $30B at 2024 and growing rapidly including new installation, maintenance, and repair. Both nuclear plant owners and storage manufactures will be benefit from the product. This enables various commercial applications in Government, industry, and academic sectors.