| Issue |
E3S Web Conf.
Volume 583, 2024
Innovative Technologies for Environmental Science and Energetics (ITESE-2024)
|
|
|---|---|---|
| Article Number | 05008 | |
| Number of page(s) | 14 | |
| Section | Tribology Solutions for Energy Efficiency | |
| DOI | https://doi.org/10.1051/e3sconf/202458305008 | |
| Published online | 25 October 2024 | |
Parametric identification of coefficients for fatigue stiffness degradation model of a composite material
1 Moscow Aviation Institute (National Research University), 4, Volokolamskoe Highway, Moscow, 125993, Russian Federation
2 Irkutsk National Research Technical University, 83, Lermontov St., Irkutsk, 664074, Russian Federation
* Corresponding author: avpanteleev@inbox.ru
The problem of finding the fatigue characteristics of a composite material based on test results is considered. As a mathematical model of stiffness degradation, a non-linear ordinary differential equation with five unknown parameters is used, reflecting characteristic changes in the properties of the material. It is required to find such parameter values that the solution of the differential equation describes the available test re- sults with sufficient accuracy. The solution procedure is reduced to the problem of optimizing the objective function, the value of which characterizes the achieved accuracy. The optimization methods used were a method that simulates the behavior of a flock of moths and a method of sequential reduction of the search set. A step-by-step algorithm for finding the unknown parameters of the model is proposed, and numerical results are presented for processing three versions of experimental data containing information about the change in the elastic modulus of the composite material during the application of load cycles.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.