Joseph Hafen , Jon Teague , Brandon Fleming , Justin Ruthstrom , Murray Moore , Steven Lukow , Julio Suazo , David Grow , Samrat Choudhury , Jonathan Gigax
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引用次数: 0
Abstract
This work presents the preliminary design of an automated surveillance tool to assess the health of SAVY-4000 nuclear material storage containers. This tool is designed by training several machine learning (ML) regression models to predict maximum residual stress in plain dents on the container sidewall. The model is trained on an experimentally validated Finite Element Analysis (FEA) model built in Abaqus FEA. The accuracy of each ML model is compared. The potential for application as well as model shortcomings are assessed. Necessary FEA model improvements are outlined and the various ML models are proposed.
期刊介绍：
Nuclear Engineering and Design covers the wide range of disciplines involved in the engineering, design, safety and construction of nuclear fission reactors. The Editors welcome papers both on applied and innovative aspects and developments in nuclear science and technology.
Fundamentals of Reactor Design include:
• Thermal-Hydraulics and Core Physics
• Safety Analysis, Risk Assessment (PSA)
• Structural and Mechanical Engineering
• Materials Science
• Fuel Behavior and Design
• Structural Plant Design
• Engineering of Reactor Components
• Experiments
Aspects beyond fundamentals of Reactor Design covered:
• Accident Mitigation Measures
• Reactor Control Systems
• Licensing Issues
• Safeguard Engineering
• Economy of Plants
• Reprocessing / Waste Disposal
• Applications of Nuclear Energy
• Maintenance
• Decommissioning
Papers on new reactor ideas and developments (Generation IV reactors) such as inherently safe modular HTRs, High Performance LWRs/HWRs and LMFBs/GFR will be considered; Actinide Burners, Accelerator Driven Systems, Energy Amplifiers and other special designs of power and research reactors and their applications are also encouraged.
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