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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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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Powering the future: How the DOE is fueling nuclear fuel cycle research and development
As global interest in nuclear energy surges, the United States must remain at the forefront of research and development to ensure national energy security, advance nuclear technologies, and promote international cooperation on safety and nonproliferation. A crucial step in achieving this is analyzing how funding and resources are allocated to better understand how to direct future research and development. The Department of Energy has spearheaded this effort by funding hundreds of research projects across the country through the Nuclear Energy University Program (NEUP). This initiative has empowered dozens of universities to collaborate toward a nuclear-friendly future.
Thomas E. Booth
Nuclear Science and Engineering | Volume 104 | Number 4 | April 1990 | Pages 374-384
Technical Paper | doi.org/10.13182/NSE90-A23735
Articles are hosted by Taylor and Francis Online.
The basic quasi-deterministic method provides an approximate importance function in arbitrary user-defined phase-space regions. The approximation is twofold. First, each region is averaged over and becomes a discrete state. Second, Monte Carlo methods estimate transport probabilities and scores between the discrete states. These two approximations lead to a set of linear equations for the state importances that can be deterministically solved. This new method is compared against the standard MCNP importance generator. A generalization of the method provides an importance function in the physical and random number spaces that may be useful for random number biasing techniques.