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
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
DOE issues RFQ for clean-energy projects at WIPP
The Department of Energy has issued a request for qualifications (RFQ) for interested parties that are looking to establish carbon pollution–free electricity (CFE) projects at its Waste Isolation Pilot Plant site in New Mexico.
Denise Neudecker, Rudolf Frühwirth, Helmut Leeb
Nuclear Science and Engineering | Volume 170 | Number 1 | January 2012 | Pages 54-60
Technical Paper | doi.org/10.13182/NSE11-20
Articles are hosted by Taylor and Francis Online.
The occurrence of unexpected mean values in statistical analyses of experimental data, known as Peelle's pertinent puzzle in nuclear data evaluation, is revisited. It is shown in terms of Bayesian statistics, it is not caused exclusively by nonlinearities but is due to improper estimates of covariance matrices of experiments. Applying the correct covariance matrix leads to the exact posterior expectation value and variance for an arbitrary number of uncorrelated measurement points that are normalized with the same quantity.