ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Division Spotlight
Robotics & Remote Systems
The Mission of the Robotics and Remote Systems Division is to promote the development and application of immersive simulation, robotics, and remote systems for hazardous environments for the purpose of reducing hazardous exposure to individuals, reducing environmental hazards and reducing the cost of performing work.
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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Latest News
Four million nuclear jobs by 2050: Who will do them?
Industry leaders from around the globe met this month to discuss the talent development that will be necessary for the long-term success of the nuclear industry.
The International Conference on Nuclear Knowledge Management and Human Resources Development, hosted by the International Atomic Energy Agency, was held in Vienna earlier this month. Discussed there was the agency’s forecast for nuclear capacity to more than double—or hopefully triple—by 2050 and the requirement of more than four million professionals to support the industry.
Nathan Siu, Ali Mosleh
Nuclear Technology | Volume 84 | Number 3 | March 1989 | Pages 265-281
Technical Paper | Probabilistic Safety Assessment and Risk Management / Nuclear Safety | doi.org/10.13182/NT89-A34210
Articles are hosted by Taylor and Francis Online.
Uncertainties in the estimation of parameters for common-cause failure models arise not only because of the small number of common-cause failure events but also because recorded events may not be relevant to the analysis of a particular plant. The data base for a plant-specific analysis may therefore be uncertain. A Bayesian methodology for treating data base uncertainties in the estimation of common-cause failure model parameters is developed and applied to a three-pump auxiliary feedwater system. Sensitivity analyses show that the results are not strongly sensitive to assumptions concerning prior distribution type and shape, but do depend somewhat on the degree of state-of-knowledge dependence between uncertain events. These analyses also show that ignoring the uncertainties in the data can lead to significant estimation errors. Finally, an approximate methodology for treating uncertain data is examined; this method provides reasonable estimates of the mean values of the common-cause failure model parameters, but underpredicts the uncertainty in these parameters.