SBIR/STTR Award attributes
Statement of the Problem: Monitoring the integrity of SNF dry-storage systems as well as fuel cladding failure and fuel assembly structural degradation becomes crucially important to DOE for public safety. For example, chloride- induced stress corrosion cracking (CISCC) in welds and heat-affected zone (HAZ) of a pressurized SNF- DSC has been identified as a potential safety concern. The high tensile residual stress and microstructure sensitization in the welds and of DSC may drive the initiation of pitting and/or the transition to SCC growth and a through-wall failure. To prevent such catastrophic failure, it is vitally important to continuously monitor the structural health or integrity of SNF-DSCs as well as the fuel cladding and fuel assembly during their long-term storage in confinements.Proposed Solution: To address this critical need, X-wave Innovations, Inc. (XII) with support from Sandia National Lab (SNL), Orano/TN-America and Dawnbreaker Inc. (DI), is developing a Non-contact, Intelligent Sensor Network (NCISN) system to continuously monitor the structural health or integrity of SNF-DSCs (such as CISCC) and fuel cladding and fuel assembly during their long-term storage.What was done in Phase I: In Phase I, we successfully prototyped a NCISN system and demonstrated its feasibility for long- term, intelligent, and remote monitoring the CISCC in SNF-DSC as well as the fuel cladding and assembly failure inside DSCs. We achieved remarkable results in FEA simulation of acoustic wave propagation, non-contact acoustic sensor design and fabrication, application software and machine learning algorithms development, NCISN prototyping and experimental setup, data analysis and feasibility demonstration.What is planned for the Phase II project: The main objective of this Phase II program is to extend our Phase I success and insights in NCISN prototyping/testing to develop a fully-functional NCISN system for continuous, intelligent monitoring the integrity of SNF-DSCs and fuel assemblies. We will refine the FEA simulation models, develop/incorporate new signal processing and machine learning algorithms, improve NCISN hardware and software, and optimize NCISN performance.Commercial Applications and Other Benefits Proposed NCISN system and technology will provide a highly efficient and reliable structural health monitoring of SNF-DSCs and fuel assemblies. This technology will help to reduce the overall cost associated with the inspection and maintenance of DSCs, which results in significantly improved reliability and minimizing the catastrophic failure of the nuclear waste storages. This new technology can also find many applications for nuclear waste management as well as nuclear reactors, including weld structures, pressure vessels and pipelines.
