ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
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The division provides a forum for focused technical dialogue on thermal hydraulic technology in the nuclear industry. Specifically, this will include heat transfer and fluid mechanics involved in the utilization of nuclear energy. It is intended to attract the highest quality of theoretical and experimental work to ANS, including research on basic phenomena and application to nuclear system design.
Utility Working Conference and Vendor Technology Expo (UWC 2022)
August 7–10, 2022
Marco Island, FL|JW Marriott Marco Island
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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Carbon value: Lifetime extensions of nuclear reactors could save billions in climate mitigation costs
On the road to achieving net-zero by midcentury, low- or no-carbon energy sources that slash carbon dioxide emissions are critical weapons. Nevertheless, the role of nuclear energy—the single largest source of carbon-free electricity—remains uncertain.
Nuclear energy, which provides 20 percent of the electricity in the United States, has been a constant, reliable, carbon-free source for nearly 50 years. But our fleet of nuclear reactors is aging, with more than half of the 92 operating reactors across 29 states at or over 40 years old—the length of the original operating licenses issued to the power plants. While some reactors have been retired prematurely, there are two options for those that remain: retire them or renew their license.
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