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Materials Science & Technology
The objectives of MSTD are: promote the advancement of materials science in Nuclear Science Technology; support the multidisciplines which constitute it; encourage research by providing a forum for the presentation, exchange, and documentation of relevant information; promote the interaction and communication among its members; and recognize and reward its members for significant contributions to the field of materials science in nuclear technology.
2023 ANS Annual Meeting
June 11–14, 2023
Indianapolis, IN|Marriott Indianapolis Downtown
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Nuclear Science and Engineering
Fusion Science and Technology
Route readiness elements in a large-scale spent nuclear fuel transportation system
The scale and duration of a national campaign to transport spent nuclear fuel (SNF) from commercial nuclear power plants around the United States would be unprecedented. A meticulous level of planning that considers many elements is needed to inspire public confidence and support.
Sunday, May 15, 2022|8:00AM–12:00PM EDT
Organizer: Xu Wu, North Carolina State University
Machine Learning (ML) is a subset of Artificial Intelligence (AI) which studies computer algorithms that can improve automatically through experience (data). Deep Learning (DL) is a subset of ML that uses multi-layered neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, language translation and others. Scientific Machine Learning (SciML), more specifically, consists of computational technologies that can be trained with scientific data to augment or automate human skills. ML has been very successful in areas such as computer vision, natural language processing, etc. But its application in scientific computing is relatively new, especially in Nuclear Engineering (NE). This workshop aims at augmenting the applications of AI/ML in scientific computing in nuclear computational science, and promoting ML-based transformative solutions across various DOE missions.
Recently, ML/DL have been applied in areas such as data-driven closure model development for nuclear thermal-hydraulics, data-driven material discovery and qualification, Digital Twins for integrated energy systems, small modular reactors (SMRs) and micro-reactors, AI-based autonomous operation and control for advanced nuclear reactors, AI-based diagnosis, prognosis and predictive maintenance, etc. In this workshop, we will have five presentations that cover a wide range of topics, including:
Active learning for computational simulations: Application to TRISO fuel failure analysis
Development of Neural Thermal Scattering (NeTS) Modules For Data Representation and Applications
Development of A Nearly Autonomous Management and Control System for Advanced Reactors
Applications of AI/ML from Nuclear Data to Reactor Design
Prediction of PWR Pin Powers using Convolutional Neutral Networks