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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 Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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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
NWMO to select Canadian repository site this year
Canada’s Nuclear Waste Management Organization, a not-for-profit organization responsible for the long-term management of the country’s intermediate- and high-level radioactive waste, is set to select a site for a deep geologic repository by the end of the year.
Chi-Szu Lee, Chaung Lin
Nuclear Technology | Volume 159 | Number 3 | September 2007 | Pages 256-266
Technical Paper | Fission Reactors | doi.org/10.13182/NT07-A3874
Articles are hosted by Taylor and Francis Online.
A method that includes a genetic algorithm (GA), principal component analysis (PCA), and an artificial neural network (ANN) is adopted in the search for the power ascension path of a boiling water reactor that used to rely solely on an operator's experiences. The power ascension path is formulated as an optimization problem with thermal limits, e.g., minimum critical power ratio, maximum linear heat generation rate, and maximum average planar linear heat generation rate, and with the stability requirement serving as a constraint. The Simulate-3 code is used to calculate the reactor core status, while the optimization problem is solved through the use of the GA. Since the search domain of the GA is relatively large, the ANN, which models the power ascension path, is developed in order to quickly select the candidate solutions for further Simulate-3 calculations, permitting the algorithm to converge effectively. Meanwhile, PCA is used to reduce the ANN input vector's dimension, which improves the ANN training efficiency and pattern recognition capability. The results show that this method efficiently obtains the proper power ascension path required for meeting all constraints at different fuel exposure levels.