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Division Spotlight
Human Factors, Instrumentation & Controls
Improving task performance, system reliability, system and personnel safety, efficiency, and effectiveness are the division's main objectives. Its major areas of interest include task design, procedures, training, instrument and control layout and placement, stress control, anthropometrics, psychological input, and motivation.
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
DOE awards $59.7 million for university nuclear R&D in 2024; $1 billion in 15 years
The Office of Nuclear Energy is awarding $59.7 million to 25 U.S. colleges and universities, two national laboratories, and one industry organization to support nuclear energy research and development and provide access to world-class research facilities, the Department of Energy announced on April 15.
Jeffrey Willert, H. Park, William Taitano
Nuclear Science and Engineering | Volume 181 | Number 3 | November 2015 | Pages 351-360
Technical Paper | doi.org/10.13182/NSE14-131
Articles are hosted by Taylor and Francis Online.
High-order/low-order (or moment-based acceleration) algorithms have been used to significantly accelerate the solution to the neutron transport k-eigenvalue problem over the past several years. Recently, the nonlinear diffusion acceleration algorithm has been extended to solve fixed-source problems with anisotropic scattering sources. In this paper, we demonstrate that we can extend this algorithm to k-eigenvalue problems in which the scattering source is anisotropic and a significant acceleration can be achieved. Furthermore, we demonstrate that the low-order, diffusion-like eigenvalue problem can be solved efficiently using a technique known as nonlinear elimination.