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Decommissioning & Environmental Sciences
The mission of the Decommissioning and Environmental Sciences (DES) Division is to promote the development and use of those skills and technologies associated with the use of nuclear energy and the optimal management and stewardship of the environment, sustainable development, decommissioning, remediation, reutilization, and long-term surveillance and maintenance of nuclear-related installations, and sites. The target audience for this effort is the membership of the Division, the Society, and the public at large.
2020 ANS Virtual Winter Meeting
November 16–19, 2020
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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Fusion Science and Technology
Advanced nuclear to be a focus of reopened Arctic Energy Office
The Department of Energy has announced the reestablishment of the Arctic Energy Office (AEO), to be located on the campus of the University of Alaska Fairbanks. The AEO was originally established in 2001 but failed to receive sufficient funding. Energy Secretary Dan Brouillette had pledged to reopen the office by the close of the current federal fiscal year.
The focus of the AEO, according to the DOE, will include international cooperation on Arctic issues, research on methane hydrates, and the development of advanced microgrids and nuclear power systems, such as small modular reactors.
Technical Session|Sponsored by HFICD
Wednesday, November 18, 2020|3:50–5:30PM (4:50–6:30PM EST)
The session will be available to join at 3:20PM (4:20PM EST).
Brent D. Shumaker
Katrina M. Groth
Jamie B. Coble
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Integrated Operations for Nuclear - ION
Kenneth D. Thomas (Idaho National Laboratory), S. J. Remer (Remer Consulting)
On the Formalization of Development and Assessment Process for Digital Twins in the Nearly Autonomous Management and Control System
Linyu Lin (North Carolina State University), Han Bao (Idaho National Laboratory), Nam T. Dinh (North Carolina State University)
Development and Assessment of Physics-guided Machine Learning for Prognosis System
Anil Gurgen (North Carolina State University), Linyu Lin (North Carolina State University), Nam T. Dinh (North Carolina State University)
Quantitative Reasoning and Risk Assessment with Dynamic Bayesian Network
Junyung Kim (RPI), Hyun Gook Kang (Rensselaer Polytechnic Institute)
Equipping Machine Learning with Uncertainty Quantification to Update Probabilistic Risk Assessment of Nuclear Power Plants using NRC Licensee Event Reports
Jaemin Yang (University of Illinois at Urbana-Champaign), Pegah Farshadmanesh (University of Illinois at Urbana-Champaign), Tatsuya Sakurahara (University of Illinois at Urbana-Champaign), Justin Pence (University of Illinois at Urbana-Champaign), Seyed Reihani (University of Illinois at Urbana-Champaign), Catherine Blake (University of Illinois at Urbana-Champaign), Zahra Mohaghegh (University of Illinois at Urbana-Champaign)
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