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Division Spotlight
Fuel Cycle & Waste Management
Devoted to all aspects of the nuclear fuel cycle including waste management, worldwide. Division specific areas of interest and involvement include uranium conversion and enrichment; fuel fabrication, management (in-core and ex-core) and recycle; transportation; safeguards; high-level, low-level and mixed waste management and disposal; public policy and program management; decontamination and decommissioning environmental restoration; and excess weapons materials disposition.
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
College students help develop waste-measuring device at Hanford
A partnership between Washington River Protection Solutions (WRPS) and Washington State University has resulted in the development of a device to measure radioactive and chemical tank waste at the Hanford Site. WRPS is the contractor at Hanford for the Department of Energy’s Office of Environmental Management.
M. J. Fleming, L. W. G. Morgan, E. Shwageraus
Nuclear Science and Engineering | Volume 183 | Number 2 | June 2016 | Pages 173-184
Technical Paper | doi.org/10.13182/NSE15-55
Articles are hosted by Taylor and Francis Online.
Modeling of nuclide densities as a function of time within magnetic confinement fusion devices such as the JET, ITER, and proposed DEMO tokamaks is performed using Monte Carlo transport codes coupled with a Bateman equation solver. The generation of reaction rates occurs through either pointwise interpolation of energy-dependent tracked particle data with nuclear data or multigroup (MG) convolution of binned fluxes with binned cross sections. The MG approach benefits from decreased computational expense and data portability, but introduces errors through effects such as self-shielding. Depending on the MG structure and nuclear data used, this method can introduce unacceptable errors without warning. We present a MG optimization method that utilizes a modified particle swarm algorithm to generate seed solutions for a nonstochastic string-tightening algorithm. This procedure has been used with a semihomogenized one-dimensional DEMO-like reactor design to produce an optimized energy group structure for tritium breeding. In this example, the errors introduced by the Vitamin-J 175 MG are reduced by two orders of magnitude in the optimized group structure.