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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.
Michael G. Lysenko, Hing-Ip Wong, G. Ivan Maldonado
Nuclear Science and Engineering | Volume 132 | Number 1 | May 1999 | Pages 78-89
Technical Paper | doi.org/10.13182/NSE99-A2050
Articles are hosted by Taylor and Francis Online.
Although artificial neural networks (ANNs) are powerful tools in terms of their high posttraining computational speed and their flexibility to construct complex nonlinear mappings from relatively few known data samples, a survey of past applications of ANNs to the area of core parameter prediction reveals drawbacks such as low prediction accuracy, lack of robust generalization, large network dimensionality, and typically high training requirements. This study provides a brief survey of past and recent applications of ANNs to direct core parameter predictions as well as an alternate hybrid approach that avoids the aforementioned shortcomings of ANNs by combining the mathematical rigor of generalized perturbation theory along with the strong qualities of ANNs in error prediction situations. The results presented focus exclusively on the neutron diffusion's fundamental mode eigenvalue (i.e., 1/keff) and demonstrate the viability of computationally inexpensive adaptive ANN error controllers for perturbation theory applications.