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.
Radiation Protection & Shielding
The Radiation Protection and Shielding Division is developing and promoting radiation protection and shielding aspects of nuclear science and technology — including interaction of nuclear radiation with materials and biological systems, instruments and techniques for the measurement of nuclear radiation fields, and radiation shield design and evaluation.
2023 ANS Winter Conference and Expo
November 12–15, 2023
Washington, D.C.|Washington Hilton
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
Westinghouse, Bechtel sign consortium agreement for first nuclear plant in Poland
Westinghouse Electric Company and engineering, construction, and project management firm Bechtel on September 20 announced the signing of a consortium agreement to partner on the design and construction of Poland’s first nuclear power plant.
Panel and Technical Session|Computational Methods, Artificial Intelligence, and Machine Learning
Friday, April 14, 2023|10:15–11:35AM EDT|Student Union 362A
Noah Walton (Univ. Tennessee, Knoxville)
Rida Rahman (Univ. Tennessee, Knoxville)
Lance M. Drouet (Univ. Tennessee, Knoxville)
This session is sponsored by the ANS Mathematics & Computation Division.
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Anomaly Detection in a Cold Trap Liquid Sodium Purification System through Multisensory Data Fusion with Deep Learning Autoencoders
Alexandra G. Akins (North Carolina State University, Argonne National Laboratory), Alexander Heifetz (ANL)
Machine Learning-Based Error Correction Model for Low-Fidelity BWR Colorset Simulation
Muhammad R. Oktavian (Purdue), Jonathan M. Nistor (Blue Wave AI Labs), John T. Gruenwald (Blue Wave AI Labs), Yunlin Xu (Purdue)
Simulating Photoelectric Radiation Transport in Cylindrical Gas Filled Photoemission Driven Cavities
Ravi Shastri (Missouri Univ. Science and Technology)
Can Advances in Artificial Intelligence Surpass Legacy Algorithms for PWR Core Optimization?
Paul R. Seurin (MIT), Koroush Shirvan (MIT)
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