ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Fuel Cycle & Waste Management
Devoted to all aspects of the nuclear fuel cycle including waste management, worldwide. Division specific areas of interest and involvement include uranium conversion and enrichment; fuel fabrication, management (in-core and ex-core) and recycle; transportation; safeguards; high-level, low-level and mixed waste management and disposal; public policy and program management; decontamination and decommissioning environmental restoration; and excess weapons materials disposition.
Nuclear and Emerging Technologies for Space (NETS 2023)
May 7–11, 2023
Idaho Falls, ID|Snake River Event Center
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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DOE makes efforts to develop an inclusive STEM workforce
More than 300 employees from the Department of Energy's Office of Environmental Management (EM) have recently retired, resulting in a large amount of job vacancies across the cleanup program, according to the DOE.
EM’s Workforce Management Office is implementing recruitment efforts to fill the vacancies with college graduates, early career professionals, mid-career candidates, and seasoned veterans.
According to the DOE, "The open positions offer opportunities across many different disciplines, including engineering, science, business, management, safety and information technology."
Advances in Thermal Hydraulics (ATH 2022)
Wednesday, June 15, 2022|3:15–5:00PM PDT|San Simeon B
Xingang Zhao (ORNL)
Yang Liu (ANL)
In the past few years, reactor thermal-hydraulic (T-H) study has advanced with the support of machine learning (ML) in many aspects, including automated experimental data analysis, data-driven prediction for important reactor thermal-fluid phenomena, and surrogate modeling and uncertainty quantification for reactor system codes. ML also showed promising potential to expand reactor T-H to a wider range of applications to better support advanced reactor deployment, such as integrated multi-physics modeling and digital twin. On the other hand, ML in T-H study has its unique challenges, from data availability and quality to model transparency and interpretability.
In this panel session, experts from different institutes with a diverse background will share their experience and perspectives on ML for T-H study, including recent progresses, existing challenges and potential solutions, and future opportunities.
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