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Modelling and Data Analysis
2025. Vol. 15, no. 2, 127–138
doi:10.17759/mda.2025150207
ISSN: 2219-3758 / 2311-9454 (online)

Modern approaches to modeling of elastic-strength properties of polymer composites

 
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Abstract

Context and relevance. Improvement of approaches to predicting the elastic-strength characteristics of composites is an urgent task in the mechanics of deformable solids, since polymer composite materials are widely used in modern technology, and the speed and accuracy of modeling the characteristics of such structures play an important role in the design and operation of structures made of composite. Objective. Having considered the existing approaches to PCM modeling, to identify the most promising ones. Results. The article provides analysis of existing approaches to modeling materials with heterogeneous structures: from early phenomenological and semi-empirical to modern multiscale ones that take into account the mutual influence of processes occurring in composites at the micro-, meso-, and macroscale levels. Conclusions. Modern multiscale finite-element approaches to modeling the elastic-strength properties of a composite material are widely used, however, a detailed description of the processes occurring at the micro- and meso- levels of the PCM requires significant computing power, which makes such approaches inapplicable in practice. To solve this problem, the multiscale approach is complemented by machine learning methods using surrogate neural networks.

General Information

Keywords: polymer composites, computer modeling, effective properties, finite-element method, multiscale approach, surrogate neural network

Journal rubric: Numerical Methods

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2025150207

Received 08.04.2025

Accepted

Published

For citation: Zagordan, N.L., Mochalova, Yu.D., Abgaryan, K.K. (2025). Modern approaches to modeling of elastic-strength properties of polymer composites. Modelling and Data Analysis, 15(2), 127–138. (In Russ.). https://doi.org/10.17759/mda.2025150207

© Zagordan N.L., Mochalova Yu.D., Abgaryan K.K., 2025

License: CC BY-NC 4.0

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Information About the Authors

Nadezhda L. Zagordan, Candidate of Science (Physics and Matematics), scientist, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Moscow, Russian Federation, ORCID: https://orcid.org/0009-0000-9973-639X, e-mail: zagordann@gmail.com

Yuliya D. Mochalova, mathematician, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0003-0109-3213, e-mail: juliamochalova96@gmail.com

Karine K. Abgaryan, Doctor of Physics and Matematics, Chief Researcher, Head of Department, Federal research center "Information and Control" Russian Academy of Sciences, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0002-0059-0712, e-mail: kristal83@mail.ru

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