具有局部粘弹性阻尼和摩擦阻尼的层叠式 Timoshenko 梁的稳定性

IF 1.6 2区 数学 Q2 MATHEMATICS, APPLIED
Yu-Ying Duan, Ti-Jun Xiao
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引用次数: 0

摘要

在本文中,我们考虑了一个具有界面滑移的双层梁系统,该系统仅通过作用于梁的一小部分的粘弹性阻尼与摩擦阻尼来稳定。我们表明,局部阻尼足以诱发整个耗散机制,并且仅在阻尼的基本条件下给出了一般和明确的能量衰减率。同时,当摩擦阻尼接近线性或多项式时,我们会获得最佳衰减率,并且通过量化每种阻尼的有效性,记忆核在无穷远处的行为要么是未量化的,要么是以相当普遍的方式量化的。为了解决阻尼局部特征带来的困难,我们设法找到了合适的加权函数来处理区域分割,并构建了适当的辅助函数。我们的结果在很大程度上改进并推广了系统的现有相关结果,甚至对于经典的季莫申科梁系统(无滑移)也是新颖的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stability of Laminated Timoshenko Beams with Local Viscoelastic Versus Frictional Damping

In this paper, we consider a two-layered beam system with an interfacial slip, stabilized only by one viscoelastic vs. frictional damping acting on a small portion of the beam. We show that the local damping is enough to induce the whole dissipation mechanism, and give a general and explicit energy decay rate only under basic conditions on the damping. Meanwhile, we obtain optimal decay rates, when the frictional damping is near linear or polynomial, and the behavior of the memory kernel at infinity is either unquantified or quantified in a quite general way, by means of quantifying the effectiveness of each type of the damping. In order to handle the difficulty caused by the local feature of the damping, we manage to find fitting weighted functions to process region segmentation, as well as to construct appropriate auxiliary functionals. Our results improve and generalize the existing related results for the system to a large extent, and they are novel even for the classical Timoshenko beam system (without slip).

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来源期刊
CiteScore
3.30
自引率
5.60%
发文量
103
审稿时长
>12 weeks
期刊介绍: The Applied Mathematics and Optimization Journal covers a broad range of mathematical methods in particular those that bridge with optimization and have some connection with applications. Core topics include calculus of variations, partial differential equations, stochastic control, optimization of deterministic or stochastic systems in discrete or continuous time, homogenization, control theory, mean field games, dynamic games and optimal transport. Algorithmic, data analytic, machine learning and numerical methods which support the modeling and analysis of optimization problems are encouraged. Of great interest are papers which show some novel idea in either the theory or model which include some connection with potential applications in science and engineering.
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