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Division Spotlight
Accelerator Applications
The division was organized to promote the advancement of knowledge of the use of particle accelerator technologies for nuclear and other applications. It focuses on production of neutrons and other particles, utilization of these particles for scientific or industrial purposes, such as the production or destruction of radionuclides significant to energy, medicine, defense or other endeavors, as well as imaging and diagnostics.
Meeting Spotlight
International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 2025)
April 27–30, 2025
Denver, CO|The Westin Denver Downtown
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
Argonne’s METL gears up to test more sodium fast reactor components
Argonne National Laboratory has successfully swapped out an aging cold trap in the sodium test loop called METL (Mechanisms Engineering Test Loop), the Department of Energy announced April 23. The upgrade is the first of its kind in the United States in more than 30 years, according to the DOE, and will help test components and operations for the sodium-cooled fast reactors being developed now.
Andreas Szeless, Lawrence Ruby
Nuclear Science and Engineering | Volume 45 | Number 1 | July 1971 | Pages 7-13
Technical Paper | doi.org/10.13182/NSE71-A20340
Articles are hosted by Taylor and Francis Online.
A method has been devised to calculate exactly the probability distribution of reactor neutron noise. The distribution is calculated from a complicated generating function which has been known for some time. The method depends on the success achieved in obtaining a closed-form expression for the n'th derivative of a differentiable r-fold composite function. As an application of the technique, exact probability distributions are calculated for a variety of parameters. The resultant distributions are compared with the approximative negative binomial distribution. In some cases, rather similar variances are found, where the negative binomial is not expected to be a good approximation to the exact distribution. The explanation lies in an interlacing of the exact and approximative distributions. A procedure is described for fitting an experimental distribution to the exact distribution, thereby obtaining the best values of the parameters α1 and Y1 ∞. When the negative binomial is a good approximation to the exact distribution, only the product α1 Y1 ∞ can be obtained by the fitting procedure. In such cases, a Feynman-variance experiment can be performed to determine the parameters separately.