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Division Spotlight
Nuclear Criticality Safety
NCSD provides communication among nuclear criticality safety professionals through the development of standards, the evolution of training methods and materials, the presentation of technical data and procedures, and the creation of specialty publications. In these ways, the division furthers the exchange of technical information on nuclear criticality safety with the ultimate goal of promoting the safe handling of fissionable materials outside reactors.
Meeting Spotlight
Conference on Nuclear Training and Education: A Biennial International Forum (CONTE 2023)
February 6–9, 2023
Amelia Island, FL|Omni Amelia Island Resort
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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Nuclear Science and Engineering
February 2023
Nuclear Technology
Fusion Science and Technology
January 2023
Latest News
Nuclear energy: enabling production of food, fiber, hydrocarbon biofuels, and negative carbon emissions
In the 1960s, Alvin Weinberg at Oak Ridge National Laboratory initiated a series of studies on nuclear agro-industrial complexes1 to address the needs of the world’s growing population. Agriculture was a central component of these studies, as it must be. Much of the emphasis was on desalination of seawater to provide fresh water for irrigation of crops. Remarkable advances have lowered the cost of desalination to make that option viable in countries like Israel. Later studies2 asked the question, are there sufficient minerals (potassium, phosphorous, copper, nickel, etc.) to enable a prosperous global society assuming sufficient nuclear energy? The answer was a qualified “yes,” with the caveat that mineral resources will limit some technological options. These studies were defined by the characteristic of looking across agricultural and industrial sectors to address multiple challenges using nuclear energy.
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.