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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.
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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.
Jerome Spanier
Nuclear Science and Engineering | Volume 133 | Number 1 | September 1999 | Pages 73-79
Technical Paper | doi.org/10.13182/NSE99-A2073
Articles are hosted by Taylor and Francis Online.
Adaptive Monte Carlo methods, based on the use of either correlated sampling or importance sampling, to obtain global solutions to certain transport problems have recently been described. The resulting "learning algorithms" are capable of achieving geometric convergence when applied to the estimation of a finite number of coefficients in a flux expansion representation of the global solution. However, because of the nonphysical nature of the random walk simulations needed to perform importance sampling, conventional transport estimators and source sampling techniques require modification to be used successfully in conjunction with such flux expansion methods. It is shown how these problems can be overcome.First, the traditional path length estimators in wide use in particle transport simulations are generalized to include rather general "detector" functions (which, in this application, are the individual basis functions chosen for the flux expansion). Second, it is shown how to sample from the signed probabilities that arise as source density functions in these applications, without destroying the zero variance property needed to ensure geometric convergence to zero error.