Cite this article as:

Kim R. P., Romanchuk S. P., Terin D. V., Korchagin S. A. The Use of a Genetic Algorithm in Modeling the Electrophysical Properties of a Layered Nanocomposite. Izv. Saratov Univ. (N. S.), Ser. Math. Mech. Inform., 2019, vol. 19, iss. 2, pp. 217-225. DOI: https://doi.org/10.18500/1816-9791-2019-19-2-217-225


Published online: 
28.05.2019
Language: 
English
Heading: 
UDC: 
501.1

The Use of a Genetic Algorithm in Modeling the Electrophysical Properties of a Layered Nanocomposite

Abstract: 

The research proposes an approach to solving the problem of selecting layered nanocomposite components with given electrical properties. The known methods for modeling the nanocomposites electrical characteristics are based on a preliminary analysis of such characteristics as the dielectric constant and electrical conductivity of the materials that make up a nanocomposite. The study proposes an algorithm for the selection of components of a layered nanocomposite using a genetic algorithm. Mathematical modeling of nanocomposite electrical properties is carried out using an effective medium model. We consider composite materials based on nanoporous silicon and partially oxidized porous silicon as an example. We have analyzed the frequency dependences of the dielectric constant and nanocomposite electrical conductivity when interacting with electromagnetic radiation. We have also studied efficiency of the proposed method depending on the rate of convergence and various parameters (mutation coefficient, population size, etc.). We developed a software package for modeling the electrical properties of a nanocomposite using a genetic algorithm. The results of the research can reduce the time and cost of creating new functional materials.

References

1. Msekh M. A., Cuong N. H., Zi G., Areias P., Zhuang X., Rabczuk T. Fracture properties prediction of clay/epoxy nanocomposites with interphase zones using a phase field model. Engineering Fracture Mechanics, 2018, vol. 188, pp. 287–299. DOI: https:doi.org/10.1016/j.engfracmech.2017.08.002
2. Vu-Bac N., Silani M., Lahmer T., Zhuang X., Rabczuk T. A unified frame- work for stochastic predictions of mechanical properties of polymeric nanocomposites. Computational Materials Science, 2015, vol. 96, pt. B, pp. 520–535. DOI: https://doi.org/10.1016/j.commatsci.2014.04.066
3. Zare Y., Rhee K. Y., Hui D. Influences of nanoparticles aggregation/agglomeration on the interfacial/interphase and tensile properties of nanocom- posites. Composites Part B: Engineering, 2017, vol. 122, pp. 41–46. DOI: https://doi.org/10.1016/j.compositesb.2017.04.008
4. Korchagin S. A., Terin D. V., Klinaev Yu. V. Simulation of a fractal composite and study of its electrical characteristics. Mat. Mod. Chisl. Met., 2017, iss. 13, pp. 22–31 (in Russian). DOI: https://doi.org/10.18698/2309-3684-2017-1-2231
5. Balagurov B. Ya. Conduction of the three-dimensional model of a composite with structural anisotropy. Journal of Experimental and Theoretical Physics, 2016, vol. 123, iss. 2, pp. 348–356. DOI: https://doi.org/10.1134/S1063776116060017
6. Zarubin V. S., Kuvirkin G. N., Savelieva I. Yu. Evaluation of dielectric permittivity of composite with dispersed inclusions. Herald of the Bauman Moscow State Technical University. Ser. Instrument Engineering, 2015, no. 3(102). pp. 50–64 (in Russian). DOI: https://doi.org/10.18698/0236-3933-2015-3-50-64
7. Zarubin V. S., Sergeeva E. S. Application of mathematical modeling to determine the thermoelastic characteristics of nano-reinforced composites. Math. Models Comput. Simul., 2017, vol. 29, no. 10, pp. 288–298. DOI: https://doi.org/10.1134/S2070048218030134

8. Aberth O. Iteration methods for finding all zeros of a polynomial simultaneously. Mathematics of Computation, 1973, vol. 27, no. 122, pp. 339–344.
9. Kerner I. O. Ein Gesamtschrittverfahren zur Berechnung der Nullstellen von Polynomen. Numerische Mathematik, 1966, vol. 8, pp. 290–294.
10. Korchagin S. A., Terin D. V., Romanchuk S. P. Synergetics of mathematical models for analysis of composite materials. Izvestiya VUZ. Applied nonlinear dynamics, 2015, vol. 23, no. 3, pp. 55–64 (in Russian). DOI: https://doi.org/10.18500/0869-6632-2015-23-3-55-64
11. Aleksandrov Y. M., Yatsishen V. V. Calculation of the elements of the complex dielectric tensor for anisotropic materials. Physics of Wave Processes and Radio Systems, 2015, vol. 18, no. 1, pp. 23–27 (in Russian).
12. Kasumova R. D., Amirov Sh. Sh., Shamilova Sh. A. Parametric interaction of optical waves in metamaterials under low-frequency pumping. Quantum Electronics, 2017, vol. 47, no. 7, pp. 655–660. DOI: http://dx.doi.org/10.1070/QEL16395
13. Toader G., Rusen E., Teodorescu M., Diacon A., Stanescu P. O., Damian C., Rotariu T., Rotariu A. New polyurea MWCNTs nanocomposite films with enhanced mechanical properties. J. Appl. Polym. Sci., 2017, vol. 134, iss. 28, p. 45061. DOI: https://doi.org/10.1002/app.45061
14. Kramer O. Genetic algorithm essentials. Springer, 2017. 94 p. DOI: https://doi.org/10.1007/978-3-319-52156-5
15. Huang Yu., Du L., Liu K., Yao X., Risacher Sh. L., Guo L.. Saykin A. J., Shen L. A Fast SCCA Algorithm for Big Data Analysis in Brain Imaging Genetics. Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics. GRAIL 2017, MICGen 2017, MFCA 2017. Lecture Notes in Computer Science, vol. 10551. Springer, Cham, 2017. pp. 210–219. DOI: http://dx.doi.org/10.1007/978-3-319-67675-3_19
16. Handbook of optical constants of solids : in 5 vols. / ed. by E. D. Palik. San Diego, Academic Press, 1997. Vol. 3. 999 p.
17. Romanchuk S. P., Korchagin S. A., Terin D. V. Simulation of the characteristics of a nanocomposite material with spherical inclusions using the genetic algorithm. Mathematical Modeling and Computational Methods, 2018, no. 2, pp. 21–31 (in Russian). DOI: https://doi.org/10.18698/2309-3684-2018-2-2131

Short text (in English): 
Full text:
175