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Aerospace Nuclear Science & Technology
Organized to promote the advancement of knowledge in the use of nuclear science and technologies in the aerospace application. Specialized nuclear-based technologies and applications are needed to advance the state-of-the-art in aerospace design, engineering and operations to explore planetary bodies in our solar system and beyond, plus enhance the safety of air travel, especially high speed air travel. Areas of interest will include but are not limited to the creation of nuclear-based power and propulsion systems, multifunctional materials to protect humans and electronic components from atmospheric, space, and nuclear power system radiation, human factor strategies for the safety and reliable operation of nuclear power and propulsion plants by non-specialized personnel and more.
Meeting Spotlight
ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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
El Salvador: Looking to nuclear
In 2022, El Salvador’s leadership decided to expand its modest, mostly hydro- and geothermal-based electricity system, which is supported by expensive imported natural gas and diesel generation. They chose to use advanced nuclear reactors, preferably fueled by thorium-based fuels, to power their civilian efforts. The choice of thorium was made to inform the world that the reactor program was for civilian purposes only, and so they chose a fuel that was plentiful, easy to source and work with, and not a proliferation risk.
Jichong Lei, Zhenping Chen, Jiandong Zhou, Chao Yang, Changan Ren, Wei Li, Chao Xie, Zining Ni, Gan Huang, Leiming Li, Jinsen Xie, Tao Yu
Nuclear Technology | Volume 208 | Number 7 | July 2022 | Pages 1223-1232
Technical Note | doi.org/10.1080/00295450.2021.2018270
Articles are hosted by Taylor and Francis Online.
The reactor core design involves the search for and detailed calculation of a large number of schemes. Four different machine learning algorithms were used in this technical note: the C4.5 algorithm (an algorithm of decision trees), Support Vector Machine, Random Forest, and Multi-layer Perceptron, respectively. Uranium enrichment, the number of fuel rods containing burnable poison, and the concentration of burnable poison were taken as independent variables in the calculation. The k-eff unevenness coefficient, the radial power nonuniformity coefficient, the radial flux nonuniformity coefficient, and the core life were taken as the number of core parameters fulfilled (CPF). Machine learning models were constructed through learning the training data set, which consisted of a large number of assembly and core schemes whose nuclear design parameters were already known. Using the models, the CPF values for the unknown core data set (the test data set) were quickly predicted. The results show that the cross-validation accuracy of each algorithm was above 94% and that the C4.5 algorithm had the highest accuracy for the overall prediction of the CPF values. For the CPF value prediction of the test data set, the time for the training data set was within 10s, while the Random Forest algorithm has the highest prediction accuracy for CPF = 4 or CPF ≠ 4.