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Robotics & Remote Systems
The Mission of the Robotics and Remote Systems Division is to promote the development and application of immersive simulation, robotics, and remote systems for hazardous environments for the purpose of reducing hazardous exposure to individuals, reducing environmental hazards and reducing the cost of performing work.
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2024 ANS Winter Conference and Expo
November 17–21, 2024
Orlando, FL|Renaissance Orlando at SeaWorld
Standards Program
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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New laws offer nuclear industry incentives for existing power plant uprates
This year, the U.S. nuclear industry received a much-needed economic boost that could help preserve operating nuclear power plants and incentivize upgrades that extend their lifespan and power output.
Signed into law in 2022, the Inflation Reduction Act offers production tax credits (PTCs) for existing nuclear power plants and either PTCs or investment tax credits (ITCs) for new carbon-free generation. These credits could make power uprates—increasing the maximum power level at which a commercial plant may operate—a much more appealing option for utilities.
D. Rochman, A. J. Koning
Nuclear Science and Engineering | Volume 169 | Number 1 | September 2011 | Pages 68-80
Technical Paper | doi.org/10.13182/NSE10-66
Articles are hosted by Taylor and Francis Online.
This paper presents a novel approach to combine Monte Carlo optimization and nuclear data to produce an optimal adjusted nuclear data file. We first introduce the methodology, which is based on the so-called “Total Monte Carlo” and the TALYS system. As an original procedure, not only a single nuclear data file is produced for a given isotope but virtually an infinite number, defining probability distributions for each nuclear quantity. Then, each of these random nuclear data libraries is used in a series of benchmark calculations. With a goodness-of-fit estimator, a best evaluation for that benchmark set can be selected. To apply the proposed method, the neutron-induced reactions on 239Pu are chosen. More than 600 random files of 239Pu are presented, and each of them is tested with 120 criticality benchmarks. From this, the best performing random file is chosen and proposed as the optimum choice among the studied random set.