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Division Spotlight
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.
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
Digital control system installed at China’s Linglong One
Earlier this month, the first digital control system was put in place at Linglong One, a small modular reactor demonstration project being built at the Changjiang nuclear power plant in Hainan Province. This is the world’s first land-based commercial SMR and is controlled by China National Nuclear Power Co. Ltd., a subsidiary of the China National Nuclear Corporation (CNNC).
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.