NEURAL NETWORK MODEL OF IRON DIFFUSION IN AUSTENITIC STEELS
https://doi.org/10.17073/0368-0797-2017-11-891-896
Abstract
Speed of iron diffusion in austenitic steels is one of the major factors determining swelling of a constructional material of units and details of an active zone of a fast nuclear reactor. The factor of iron diffusion essentially depends on a chemical composition of steels and alloys on the basis of iron. Therefore the problem of its modeling is rather actual. The file of experimental data on diffusion in steels and alloys containing various combinations of C, Si, Cr, Ni, Mn, Mo, Nb, Ti, V, W, Al, Pb, Bi, Sn and Sb in a range of temperatures of 1023 ÷ 1666 K has been generated on the basis of literary data. For correct approximation of iron diffusion the artificial neural network was used in the form of 4-layer-perceptron with number of units on layers as 49:10:17:1. Developed neural network model was tested on independent experimental data and has shown satisfactory statistical characteristics that shows its adequacy. Calculations of coefficient of iron diffusion in pure iron and both steels of grades ChS68 and EK164 show higher factor of diffusion in steels, than in pure iron. On the basis of the developed model the diffusion coefficients of shells of two fuel elements of EK164-ID c.d. steel have been calculated. Fuel elements were irradiated in a reactor on fast neutrons BN-600 at various temperatures and damaging dozes at their maximal and minimal alloying. Calculations have shown that in the steel containing a minimum quantity of alloying elements, speed of diffusion is below, than in steel with maximal contents of alloying elements, and swelling in inverse proportion to coefficient of iron diffusion in EK164-ID c.d. steel. On the basis of these data it has been received linear (in logarithms) regression equation of steel swelling S depending on temperature T, damaging doze D and coefficient of iron diffusion DFe : lnS = –340,511 + 0,036 lnD + 42,64lnT – 1,558lnDFe , where D is measured in dpa; Т – in degrees of Celsius; DFe – in m2 /s; S – in %. A range of change of temperature was 435 – 515 °С, damaging doze was 57 ÷ 76 dpa.
About the Authors
M. Yu. Belomyttsev
National University of Science and Technology “MISIS” (MISIS)
Russian Federation
Russian Federation
Dr. Sci. (Eng.), Professor of the Chair “Metallography and Physics of Strength”,
Moscow
S. M. Obraztsov
“State Scientific Centre of the Russian Federation – Institute for Physics and Power Engineering named after A. I. Leypunsky” (FSUE “SSC RF – IPPE”)
Russian Federation
Russian Federation
Dr. Sci. (Phys.–Math.),
Obninsk
V. A. Solov’ev
“State Scientific Centre of the Russian Federation – Institute for Physics and Power Engineering named after A. I. Leypunsky” (FSUE “SSC RF – IPPE”)
Russian Federation
Russian Federation
Senior Researcher,
Obninsk
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Review
For citations:
Belomyttsev M.Yu., Obraztsov S.M., Solov’ev V.A. NEURAL NETWORK MODEL OF IRON DIFFUSION IN AUSTENITIC STEELS. Izvestiya. Ferrous Metallurgy. 2017;60(11):891-896. (In Russ.) https://doi.org/10.17073/0368-0797-2017-11-891-896
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