ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
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Division Spotlight
Operations & Power
Members focus on the dissemination of knowledge and information in the area of power reactors with particular application to the production of electric power and process heat. The division sponsors meetings on the coverage of applied nuclear science and engineering as related to power plants, non-power reactors, and other nuclear facilities. It encourages and assists with the dissemination of knowledge pertinent to the safe and efficient operation of nuclear facilities through professional staff development, information exchange, and supporting the generation of viable solutions to current issues.
Meeting Spotlight
Utility Working Conference and Vendor Technology Expo (UWC 2024)
August 4–7, 2024
Marco Island, FL|JW Marriott Marco Island
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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Nuclear Science and Engineering
August 2024
Nuclear Technology
Fusion Science and Technology
Latest News
BWXT will scout potential TRISO fuel production sites in Wyoming
BWX Technologies Inc. announced today that its Advanced Technologies subsidiary has signed a cooperation agreement with the state of Wyoming to evaluate locations and requirements for siting a potential new TRISO nuclear fuel fabrication facility in the state.
A. I. Mogilner, A. O. Skomorokhov, D. M. Shvetsov
Nuclear Technology | Volume 53 | Number 1 | April 1981 | Pages 8-18
Technical Paper | Fission Reactor | doi.org/10.13182/NT81-A17051
Articles are hosted by Taylor and Francis Online.
The problem of nuclear power plant noise diagnostics was formulated as a problem of the pattern recognition theory. The use of the entropy criterion, the difference of the conditional probability density criterion, and the Karhunen-Loeve expansion for feature extraction were discussed. The Bayes’ learning was applied to decision rule development. The non-parametric K nearest neighbor method was used for the probability density estimate. These methods were applied to a boiling type and a burnout identification with the help of an acoustic noise. The acoustic noise information about the heat exchange processes was presented in the dimensionality reduced space. The Bayes’ decision rule for the burnout identification was developed. The experiments on the Universal Combined Model and the Reactor Channel Model plants have demonstrated a high efficiency of the pattern recognition theory application to the reactor noise diagnosis.