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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.
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2024 ANS Winter Conference and Expo
November 17–21, 2024
Orlando, FL|Renaissance Orlando at SeaWorld
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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New laws offer nuclear industry incentives for existing power plant uprates
This year, the U.S. nuclear industry received a much-needed economic boost that could help preserve operating nuclear power plants and incentivize upgrades that extend their lifespan and power output.
Signed into law in 2022, the Inflation Reduction Act offers production tax credits (PTCs) for existing nuclear power plants and either PTCs or investment tax credits (ITCs) for new carbon-free generation. These credits could make power uprates—increasing the maximum power level at which a commercial plant may operate—a much more appealing option for utilities.
F. Castiglia, E. Oliveri
Nuclear Science and Engineering | Volume 86 | Number 3 | March 1984 | Pages 297-301
Technical Paper | doi.org/10.13182/NSE84-A17558
Articles are hosted by Taylor and Francis Online.
In photon transport calculations by a Monte Carlo method, the solution of the inverse Klein-Nishina cumulative distribution function for random selection of a photon energy upon Compton scattering is not practical; moreover, Koblinger's procedure cannot be applied for incoming photon energies less than . In this case the sampling rejection methods (SRMs), especially the nonuniform ones, represent a good alternative. A number of SRMs for the Klein-Nishina probability density function are presented; some of the methods have very high selection efficiencies. These techniques are tested within the bounds of computer time required for a successful selection. At least one of them is appreciably advantageous over those previously proposed.