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Division Spotlight
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.
Meeting Spotlight
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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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Latest News
Kentucky legislature sends nuclear bills to governor
Kentucky’s Republican-majority legislature passed a bill this past week that could bring nuclear energy to the “coal-is-king” state as lawmakers broadly seek solutions to reduce carbon emissions. The bill went to Democratic Gov. Andrew Beshear on Monday for final approval.
Seung-Hwan Seong, Heui-Youn Park, Dong-Hoon Kim, Yong-Suk Suh, Seop Hur, In-Soo Koo, Un-Chul Lee, Jin-Wook Jang, Yong-Chul Shin
Nuclear Science and Engineering | Volume 141 | Number 1 | May 2002 | Pages 66-77
Technical Paper | doi.org/10.13182/NSE02-A2267
Articles are hosted by Taylor and Francis Online.
A new fast-running analytic model has been developed for analyzing the load follow operation. The new model was based on the neural network theory, which has the capability of modeling the input/output relationships of a nonlinear system. The new model is made up of two error back-propagation neural networks and procedures to calculate core parameters, such as the distributions and density of xenon in a quasi-steady-state core like load follow operation. One neural network is designed to retrieve the axial offset of power distribution, and the other is for reactivity corresponding to a given core condition. The training data sets for learning the neural networks in the new model are generated with a three-dimensional nodal code and, also, the measured data of the first-day test of load follow operation. Using the new model, the simulation results of the 5-day load follow test in a pressurized water reactor show a good agreement between the simulation data and the actual measured data. Required computing time for simulating a load follow operation is comparable to that of a fast-running lumped model. Moreover, the new model does not require additional engineering factors to compensate for the difference between the actual measurements and analysis results because the neural network has the inherent learning capability of neural networks to new situations.