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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
Digital control system installed at China’s Linglong One
Earlier this month, the first digital control system was put in place at Linglong One, a small modular reactor demonstration project being built at the Changjiang nuclear power plant in Hainan Province. This is the world’s first land-based commercial SMR and is controlled by China National Nuclear Power Co. Ltd., a subsidiary of the China National Nuclear Corporation (CNNC).
Tom Burr, Brian Williams, Stephen Croft, Morgan White, Ken Hanson
Nuclear Science and Engineering | Volume 173 | Number 1 | January 2013 | Pages 15-27
Technical Paper | doi.org/10.13182/NSE11-112
Articles are hosted by Taylor and Francis Online.
Meta-analysis aims to combine results from multiple experiments. For example, a neutron reaction rate or cross section is typically measured in multiple experiments, and a single estimate and its uncertainty are provided for users of the estimated reaction rate. It is often difficult to combine estimates from multiple laboratories because there can be important differences in experimental protocols among laboratories and because laboratories do not always provide all the information needed to assess the estimate's uncertainty, particularly if total uncertainty (random and systematic) is required. The paper illustrates that explicit measurement error models are essential for understanding measurement processes and for guiding how to combine multiple measurements, whether the measurements are consistent or not. We emphasize that both the consensus estimate and its estimated uncertainty depend on the assumed measurement error model, and we investigate measurement error model selection options for two examples.