ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
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Aerospace Nuclear Science & Technology
Organized to promote the advancement of knowledge in the use of nuclear science and technologies in the aerospace application. Specialized nuclear-based technologies and applications are needed to advance the state-of-the-art in aerospace design, engineering and operations to explore planetary bodies in our solar system and beyond, plus enhance the safety of air travel, especially high speed air travel. Areas of interest will include but are not limited to the creation of nuclear-based power and propulsion systems, multifunctional materials to protect humans and electronic components from atmospheric, space, and nuclear power system radiation, human factor strategies for the safety and reliable operation of nuclear power and propulsion plants by non-specialized personnel and more.
Materials in Nuclear Energy Systems (MiNES 2023)
December 10–14, 2023
New Orleans, LA|New Orleans Marriott
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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New U.K. report: Young people want to know more about nuclear
Almost two-thirds of 14- to 18-year-olds in the United Kingdom would consider a career in nuclear if they knew more about it, according to a new report, Nuclear Energy: Young People’s Views on Nuclear Energy and Careers in the Nuclear Sector, from the British Science Association (BSA).
About the report: The report was conducted as part of the BSA’s Future Forum program and was funded by Urenco, an international supplier of uranium enrichment services and fuel cycle products, as part of its commitment to education and skills development.
The report centered around an initial survey of 1,000 14- to 18-year-olds in England, Scotland, and Wales, with two follow-up workshops that were attended by 39 young people, providing the opportunity for more detailed responses.
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