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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.
2021 ANS Winter Meeting and Technology Expo
November 30–December 3, 2021
Washington, DC|Washington Hilton
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Results of advanced nuclear survey released
The U.S. Nuclear Industry Council has released the results of its 2021 Advanced Nuclear Survey.
The results, issued late last month, include information from 17 USNIC-member advanced nuclear developers on topics such as federal and state policies, types of reactors in development, U.S./Canadian licensing, need for control room operators, Nuclear Regulatory Commission fees, Department of Energy programs, and high-assay low-enriched uranium (HALEU). In addition, 24 companies (the 17 member firms plus seven nonmember firms) provided their perspectives on the NRC’s planned 10 CFR Part 53 regulation of advanced reactors.
Wednesday, October 6, 2021|9:40–11:20AM (10:40AM–12:20PM EDT)|Salon A
Zeyun Wu (Virginia Commonwealth Univ.)
Ryan G. McClarren (University of Notre Dame)
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On the Use of Machine Learning (Neural Networks) to Deepen Numerical Validation of Neutronic Calculations: Basics and Application to an APOLLO3-SFR Calculation Scheme
J.-M. Palau (CEA/DES), G. Rimpault (CEA/DES)
Acceleration of Multi-Objective Optimisation Calculations for Nuclear Burnup Studies Using Intra-Optimisation Objective Expansion
D.J. Brennan (Univ. of Cambridge), G. T. Parks (Univ. of Cambridge)
Application of Reinforcement Learning Optimisation Methodology to BWR Assemblies
Majdi I. Radaideh (MIT), Benoit Forget (MIT), Koroush Shirvan (MIT)
Estimation of the Axial Neutron Flux Profiles in the SAFARI-1 Core Using Artificial Neural Networks
L. E. Moloko (South African Nuclear Energy Corp.), P. M. Bokov (South African Nuclear Energy Corp.), K. N. Ivanov (NC State Univ.)
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