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.
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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Venue, date changed for ANS’s Annual Conference
The American Nuclear Society’s 2024 Annual Conference is moving the venue in part to accommodate a higher-than-expected number of submissions for the Annual Conference and embedded topical meetings—the most received for an annual meeting in over a decade! The conference venue was changed to Mandalay Bay at the beginning of the Las Vegas strip. However, the change in accommodation comes with a change in dates: The meeting has been moved one week later than originally scheduled, to June 16–19.
13th Nuclear Plant Instrumentation, Control & Human-Machine Interface Technologies (NPIC&HMIT 2023)
Tuesday, July 18, 2023|10:00–11:45AM EDT|301C
Daniel G. Cole
Brent D. Shumaker
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Knowledge-Informed Uncertainty-Aware Machine Learning for Time Series Forecasting of Dynamical Engineered Systems
Xingang Zhao (ORNL), Bryan Maldonado Puente (ORNL), Siyan Liu (ORNL), Seung-Hwan Lim (ORNL), William Gurecky (ORNL), Dan Lu (ORNL), Matthew Howell (ORNL), Frank Liu (ORNL), Wesley Williams (ORNL), Pradeep Ramuhalli (ORNL)
Quantifying Uncertainty of Deep Reinforcement Learning Based Decision Making for Operations and Maintenance of Nuclear Power Plant
Ryan M. Spangler (Univ. Pittsburgh), Daniel G. Cole (Univ. Pittsburgh)
Development of a Virtual Sensor for Leading Edge Flow Meter Measurements in Nuclear Power Plants
Brent Shumaker (Analysis and Measurement Services Corp.), Christopher Guillotte (Analysis and Measurement Services Corp.), Brian Moazen (Analysis and Measurement Services Corp.), Zachary Becker (Analysis and Measurement Services Corp.), Jamie Coble (Univ. Tennessee, Knoxville)
Presented by Ryan O’Hagan (Analysis and Measurement Services Corp.)
Using Machine Learning to Extract and Normalize Historic Maintenance Data from Work Descriptions
Nicholas Zwiryk (Westinghouse Electric Co.), Steven Yurkovich (Westinghouse Electric Co.)
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