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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.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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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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Latest News
ARG-US Remote Monitoring Systems: Use Cases and Applications in Nuclear Facilities and During Transportation
As highlighted in the Spring 2024 issue of Radwaste Solutions, researchers at the Department of Energy’s Argonne National Laboratory are developing and deploying ARG-US—meaning “Watchful Guardian”—remote monitoring systems technologies to enhance the safety, security, and safeguards (3S) of packages of nuclear and other radioactive material during storage, transportation, and disposal.
Amit Thakur, Umasankari Kannan
Nuclear Science and Engineering | Volume 193 | Number 10 | October 2019 | Pages 1160-1171
Technical Paper | doi.org/10.1080/00295639.2019.1599607
Articles are hosted by Taylor and Francis Online.
Evolutionary algorithms play an important role for solving various optimization problems related to fuel management in reactor physics like core loading pattern optimization (LPO) or refueling. In general, all algorithms make a sample of solution candidates and evaluate the fitness of all candidates, and then the candidates with better fitness value are used to generate the next sample of solution candidates. In optimization algorithms, internal parameters [like population size, weighting factor in estimation of distribution algorithm (EDA) and population size, cross-over rate, etc., in Genetic Algorithm (GA)] have a stiffness problem as the value of these parameters is fixed at the first generation and is not being changed subsequently. However, the flexibility of changing the value of even one internal parameter during the generations will make the algorithm more efficient. In this paper we propose that fuzzy logics can be used in an innovative way to eliminate the stiffness problem related to internal parameters in evolutionary algorithms. As a test case, EDA for initial core LPO of the advanced heavy water reactor is chosen, and the use of fuzzy logics has shown a significant improvement in the algorithm’s performance. The appropriate value of weighting factor α in EDA has been predicted using fuzzy logics in each generation, and this has resulted in efficiency improvement of the algorithm. The improved methodology is expected to give better performance with other optimization algorithms, such as the GA or Ant Colony Optimization Algorithm, etc.