Mechanics & Industry
Volume 24, 2023
|Number of page(s)||18|
|Published online||03 May 2023|
Static reinforcement and vibration reduction of structures using topology optimization
Laboratoire de Mécanique des Structures et des Systèmes Couplés (LMSSC), Structural Mechanics and Coupled System Laboratory, Conservatoire National des Arts et Métiers (Cnam), 292 rue Saint-Martin, 75003 Paris, France
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Accepted: 13 January 2023
This paper presents a topology optimization formulation based on the Solid Isotropic Material with Penalization (SIMP) method and solved by the Modified Optimality Criteria (MOC) algorithm. It addresses mechanical design problems such as structural reinforcement adding elastic material or vibration reduction using viscoelastic layers. The aim is thus to attach on a pre-existing given structure a design domain in order to improve the behavior of this elastic structure, according to an objective function. This can be useful when one wants to use, for example, additive manufacturing to reinforce a pre-existing structure or to maximize structural damping. Two objective functions are tested in both linear statics and dynamics: a compliance based objective function and a displacement based one. In the dynamic case, written in the frequency domain, the two proposed objective functions include the viscoelastic material model (a Zener fractional derivative one) used to fill the design domain. The displacement criteria is developed using a general formula able to take into account as many degree of freedom as necessary. Finally, some applications based on beams and CubeSat-like structures are shown in this article. The proposed examples show that in both statics and dynamics, the optimization of a restrained design domain attached to an existing structure can improve its behavior: stiffness improvement or vibration reduction.
Key words: Static Reinforcement / Vibration Reduction / Topology optimization / Viscoelasticity
© S. Burri and A. Legay, Published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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