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Division Spotlight
Education, Training & Workforce Development
The Education, Training & Workforce Development Division provides communication among the academic, industrial, and governmental communities through the exchange of views and information on matters related to education, training and workforce development in nuclear and radiological science, engineering, and technology. Industry leaders, education and training professionals, and interested students work together through Society-sponsored meetings and publications, to enrich their professional development, to educate the general public, and to advance nuclear and radiological science and engineering.
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
BWXT announces nuclear manufacturing plant expansion
BWX Technologies announced today plans to expand and add advanced manufacturing equipment to its manufacturing plant in Cambridge, Ontario, Canada.
A $36.3 million USD ($50M CAD) expansion will increase the plant’s size by 25 percent—to 280,000 square feet—and another $21.7 million USD ($30M CAD) will be spent on new equipment to increase and accelerate its output of large nuclear components. The investment will increase capacity and create more than 200 long-term jobs for skilled workers, engineers, and support staff, according to the company.
A. Petruzzi
Nuclear Technology | Volume 205 | Number 12 | December 2019 | Pages 1554-1566
Technical Paper | doi.org/10.1080/00295450.2019.1632092
Articles are hosted by Taylor and Francis Online.
Predictive Modeling Methodology constitutes an innovative approach to perform uncertainty analysis (UA) that reduces the subjective and user-defined ways to manage experimental data and derive uncertainty of input parameters that characterize the Propagation of Input Uncertainties (PIU) and/or Propagation of Output Accuracies (POA) methods.
The Code with the capability of Adjoint Sensitivity and Uncertainty AnaLysis by Internal Data ADjustment and assimilation (CASUALIDAD) method can be developed as a fully deterministic method based on advanced mathematical tools to internally perform in the thermal-hydraulic system code the sensitivity analysis (SA) and the UA. The method is based upon powerful mathematical tools to perform the SA and upon the Data Adjustment and Assimilation methodology by which experimental observations are combined with code predictions and their respective errors through the application of the Bayes theorem and of the Principle of Maximum Likelihood to provide an improved estimate of the system state and of the associated uncertainty considering all input parameters that affect any prediction.
The methodology has been structured in two main steps. The first step generates the database of improved estimations (IEs) starting from the available set of experimental data and related qualified calculations. The second step deals with the use of the selected (from the obtained database) set of IEs for the uncertainty evaluation of the predicted nuclear power plant transient scenario.
The proposed methodology clearly interrelates in a consistent and robust framework the code validation issue with the evaluation of the uncertainty of code responses passing through the quantification of input uncertainty parameters of code models, thus constituting a step forward with respect to the subjectivity of the current methods based on PIU and/or POA.