Volume 14, Issue 2 (2-2024)
IJOCE 2024, 14(2): 295-318 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Karimi A, Bazrafshan Moghaddam A. A META-HEURISTIC FRAMEWORK TO IMPROVE NONLINEAR FINITE ELEMENT ANALYSIS. IJOCE 2024; 14 (2) :295-318
URL: http://ijoce.iust.ac.ir/article-1-588-en.html
URL: http://ijoce.iust.ac.ir/article-1-588-en.html
1- Civil Engineering Department, Faculty of Engineering, Shahrood University of Technology, Shahrood, Iran
Abstract: (6591 Views)
Most industrial-practical projects deal with nonlinearity phenomena. Therefore, it is vital to implement a nonlinear method to analyze their behavior. The Finite Element Method (FEM) is one of the most powerful and popular numerical methods for either linear or nonlinear analysis. Although this method is absolutely robust, it suffers from some drawbacks. One of them is convergency issues, especially in large deformation problems. Prevalent iterative methods such as the Newton-Raphson algorithm and its various modified versions cannot converge in certain problems including some cases such as snap-back or through-back. There are some appropriate methods to overcome this issue such as the arc-length method. However, these methods are difficult to implement. In this paper, a computational framework is presented based on meta-heuristic algorithms to improve nonlinear finite element analysis, especially in large deformation problems. The proposed method is verified via different benchmark problems solved by commercial software. Finally, the robustness of the proposed algorithm is discussed compared to the classic methods.
Keywords: Nonlinear Analysis, Finite Element Method, Meta-Heuristic Algorithm, Continuum Mechanics, Enriched Firefly Algorithm, Large Deformation, Optimization
Type of Study: Research |
Subject:
Applications
Received: 2024/02/20 | Accepted: 2024/05/10 | Published: 2024/05/28
Received: 2024/02/20 | Accepted: 2024/05/10 | Published: 2024/05/28
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
