This report summarizes accomplishments in the development and maintenance of modeling and simulation tools to support material accountancy of bulk nuclear facilities. In FY25, we added new capabilities to MAPIT (new statistical test, improved error handling), launched the open source F3M modeling library, and added three new facility models to the SSPM-L model library.
The SNL-developed F3M and MAPIT tools have the capability to analyze MC&A approaches for nuclear facilities via facility process flow simulation and statistical tests. Improvements on the application of F3M and MAPIT in simulating a generic TRISO fuel fabrication facility were successfully completed. This modeling framework can support the U.S. DOE and industry stakeholders in developing MC&A approaches for fuel fabrication facilities via demonstration of regulatory compliance.
Sandia National Laboratories (SNL) is in the process of creating Inspecta (International Nuclear Safeguards Personal Examination and Containment Tracking Assistant), an Artificial Intelligence (AI)-powered smart digital assistant (SDA) with robotic capabilities, aimed at enhancing the effectiveness, efficiency, and safety of international nuclear safeguards inspections. This innovative tool is designed to assist inspectors on-site by supporting or automating tasks that are typically mundane, hazardous, or susceptible to errors. In 2021, the development team established the specifications for Inspecta by analyzing International Atomic Energy Agency (IAEA) documents and consulting with former IAEA inspectors and subject matter experts. This process involved aligning in-field inspection tasks with existing commercial or open-source technologies to outline a roadmap for the initial prototype of Inspecta, while also identifying areas needing further research and development. From 2022 – 2024, the focus has shifted to integrating a critical inspection activity, the examination of seals, into an early version of Inspecta. This has involved developing both the software and hardware capabilities necessary for this task. This report outlines the ongoing advancements in Inspecta's functionalities, specifically those supporting the seal examination process.
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Annals of Nuclear Energy
Artificial intelligence (AI) and machine learning (ML) are near-ubiquitous in day-to-day life; from cars with automated driver-assistance, recommender systems, generative content platforms, and large language chatbots. Implementing AI as a tool for international safeguards could significantly decrease the burden on safeguards inspectors and nuclear facility operators. The use of AI would allow inspectors to complete their in-field activities quicker, while identifying patterns and anomalies and freeing inspectors to focus on the uniquely human component of inspections. Sandia National Laboratories has spent the past two and a half years developing on-device machine learning to develop both a digital and robotic assistant. This combined platform, which we term INSPECTA, has numerous on-device machine learning capabilities that have been demonstrated at the laboratory scale. This work describes early successes implementing AI/ML capabilities to reduce the burden of tedious inspector tasks such as seal examination, information recall, note taking, and more.
Solar Energy
Thermochemical air separation to produce high-purity N2 was demonstrated in a vertical tube reactor via a two-step reduction–oxidation cycle with an A-site substituted perovskite Ba0.15Sr0.85FeO3–δ (BSF1585). BSF1585 particles were synthesized and characterized in terms of their chemical, morphological, and thermophysical properties. A thermodynamic cycle model and sensitivity analysis using computational heat and mass transfer models of the reactor were used to select the system operating parameters for a concentrating solar thermal-driven process. Thermal reduction up to 800 °C in air and temperature-swing air separation from 800 °C to minimum temperatures between 400 and 600 °C were performed in the reactor containing a 35 g packed bed of BSF1585. The reactor was characterized for dispersion, and air separation was characterized via mass spectrometry. Gas measurements indicated that the reactor produced N2 with O2 impurity concentrations as low as 0.02 % for > 30 min of operation. A parametric study of air flow rates suggested that differences in observed and thermodynamically predicted O2 impurities were due to imperfect gas transport in the bed. Temperature swing reduction/oxidation cycling experiments between 800 and 400 °C in air were conducted with no statistically significant degradation in N2 purity over 50 cycles.