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Division Spotlight
Human Factors, Instrumentation & Controls
Improving task performance, system reliability, system and personnel safety, efficiency, and effectiveness are the division's main objectives. Its major areas of interest include task design, procedures, training, instrument and control layout and placement, stress control, anthropometrics, psychological input, and motivation.
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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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Latest News
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.
Han Gon Kim, Soon Heung Chang, Byung Ho Lee
Nuclear Science and Engineering | Volume 113 | Number 1 | January 1993 | Pages 70-76
Technical Paper | doi.org/10.13182/NSE93-A23994
Articles are hosted by Taylor and Francis Online.
In pressurized water reactors, the fuel reloading problem has significant meaning in terms of both safety and economics. The local power peaking factor must be kept lower than a predetermined value during a cycle, and the effective multiplication factor must be maximized to extract the maximum energy. If these core parameters could be obtained in a very short time, the optimal fuel reloading patterns would be found more effectively and quickly. A very fast core parameter prediction system is developed using the backpropagation neural network. This system predicts the core parameters several hundred times as fast as the reference numerical code, within an error of a few percent. The effects of the variation of the training rate coefficients, the momentum, and the hidden layer units are also discussed.