EDN: EHLYZA
Authors & Affiliations
Darin N.A., Uvakin M.A., Antipov M.V., Nikolaev A.L., Makhin I.V.
Russian Federal Nuclear Center – Zababakhin All-Russia Research Institute of Technical Physics, Snezhinsk, Russia
Darin N.A. – Engineer. Contacts: 21, Ordzhonikidze st., Podolsk, Moscow region, 142103. Tel.: +7 (4967) 65-26-25; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it..
Uvakin M.A. – Deputy Head of Department, Cand. Sci. (Phys.-Math.).
Antipov M.V. – Engineer.
Nikolaev A.L. – Lead Engineer.
Makhin I.V. – Head of Group.
Abstract
The article presents the first part of a large research regarding the possibility of implementation the option of calculating macroscopic sections using previously prepared functional expansions in polynomial series into the KORSAR/GP code. The relevance of the work is due to the increasing complexity of the models used for calculations, and consequently, the increasing resource intensity of the calculations performed. Considered methods make it possible to significantly reduce the amount of computer memory, which is used both when performing calculations and when storing results, which helps to increase productivity.
To find the best approximation of macro cross-sections, in addition to the traditional approach, the possibility of using machine learning and deep learning models is considered. The fundamental possibility of approximation is shown. Its accuracy is checked both by a direct method based on the known values of the macro cross-sections used to find the expansion coefficients, and on the basis of those values that were not used in this procedure.
The results obtained demonstrate the fundamental possibility of using the methods under consideration. Based on the results of the work, a number of practically significant conclusions regarding the prospects for introducing the approach under consideration into the KORSAR/GP code without additional restrictions on the scope of application are made.
Keywords
KORSAR/GP code, library of macro cross-sections, polynomial interpolation, linear regression, neural network
Article Text (PDF, in Russian)
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UDC 621.039.51
Problems of Atomic Science and Technology. Series: Nuclear and Reactor Constants, 2025, no. 1, 1:2
