Archives
December 2024
Categories
All
|
Back to Blog
Nonlinear Dynamic Analysis Framework for Slender Structures Using the Modal Rotation Method12/10/2024 Yoshitaka Shizuno, Shuonan Dong, Ryo Kuzuno, Taiki Okada, Shugo Kawashima, Kanjuro Makihara, and Keisuke Otsuka
J. Comput. Nonlinear Dynam. March 2025, 20(2): 021002. https://doi.org/10.1115/1.4067201 Owing to their low induced drag, high-aspect-ratio wings are often applied to aircraft, particularly high-altitude long-endurance (HALE) aircraft. An analytical method that considers geometrical nonlinearity is necessary for the analysis of high-aspect-ratio wings as they tend to undergo large deformations. Nonlinear shell/plate or solid finite element methods are widely used for the static analysis of wing strength. However, an increase in the number of elements drastically increases the computational costs owing to the complexity of wing shapes. The modal rotation method (MRM) can avoid this additional expense by analyzing large deformations based on modes and stiffness matrices obtained from any linear or linearized model. However, MRM has only been formulated as a static analysis method. In this study, a novel modal-based dynamic analysis framework, referred to as dynamic MRM (DMRM), is developed to analyze slender cantilever structures. This paper proposes a method to discretize dynamics by capitalizing on the fact that MRM considers geometrical nonlinearity based on deformed shapes. The proposed method targets slender structures with small strains and large displacements and considers geometrical nonlinearity, but not material nonlinearity. Additionally, a formulation method for the work performed by a follower force is proposed. The energy stored in the structure agreed with the work performed by an external force in each performed simulation. DMRM achieved a 95% reduction in the calculation time compared with a nonlinear plate finite element method in a performed simulation.
0 Comments
Back to Blog
Jamal-Odysseas Maaita and Dimitrios Prousalis
J. Comput. Nonlinear Dynam. January 2025, 20(1): 011008. https://doi.org/10.1115/1.4067010 A nonregular oscillation is not enough to define a system as chaotic. A more in-depth investigation is required to prove the existence of chaotic behavior, which is challenging. Although many scientists use the Lyapunov Characteristic Exponents to detect chaos, it is not the only method. Several scientists have introduced different methods that utilize various properties of dynamical systems. Hidden Attractors may be chaotic or regular. The fact that they have small basins of attraction introduces difficulties in locating and characterizing them. The paper presents four different chaotic indicators based on the evolution of the deviation vectors: the maximal Lyapunov Exponent, the Lyapunov Characteristic Exponents, the Fast Lyapunov Index (FLI), and the Small Alignment Index. It includes their properties and the advantages and disadvantages of each method. Also, it includes the algorithms to calculate them and their implementation in Python. The paper closes with a comparison between the four indices applied to a system with hidden attractors. |
RSS Feed
JOURNAL OF COMPUTATIONAL and
NONLINEAR DYNAMICS COMPANION |
QUICK LINKS |
|
Copyright © 2021 Journal of Computational and Nonlinear Dynamics