A method to accurately define arbitrary algorithmic damping character as viscous damping

IF 2.2 3区 工程技术 Q2 MECHANICS
Dániel Serfőző, Balázs Pere
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引用次数: 0

Abstract

Undesired oscillations often emerge in numerical solutions, especially in the case of dynamic problems. These are mainly spurious oscillations which must be eliminated or reduced at least to provide accurate results. Numerical methods with damping effect are especially useful to achieve this goal. However, the concrete shape of the damping characteristics has a great impact on the effectiveness. Dissipative numerical methods mostly have a specific damping character with very limited alteration possibility. In this article, a novel numerical method has been introduced where the dissipative effect is exerted via viscous damping. Using the proposed method, a great variety of damping curves can be defined accurately, straight through the determination of the algorithmic damping ratio. The newly developed technique is mainly useful for applications where the shape of the damping characteristics significantly affects the accuracy.

Abstract Image

一种将任意算法阻尼特性精确定义为粘性阻尼的方法
在数值解中经常出现不希望出现的振荡,特别是在动态问题的情况下。这些主要是伪振荡,必须消除或减少,至少提供准确的结果。考虑阻尼效应的数值方法对实现这一目标特别有用。然而,混凝土形状的阻尼特性对其有效性有很大影响。耗散数值方法大多具有特定的阻尼特性,改变的可能性非常有限。本文介绍了一种新的数值计算方法,其中耗散效应是通过粘性阻尼来发挥的。利用该方法,可以准确地定义各种阻尼曲线,直接通过算法阻尼比的确定。新开发的技术主要用于阻尼特性的形状显著影响精度的应用。
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来源期刊
CiteScore
4.40
自引率
10.70%
发文量
234
审稿时长
4-8 weeks
期刊介绍: Archive of Applied Mechanics serves as a platform to communicate original research of scholarly value in all branches of theoretical and applied mechanics, i.e., in solid and fluid mechanics, dynamics and vibrations. It focuses on continuum mechanics in general, structural mechanics, biomechanics, micro- and nano-mechanics as well as hydrodynamics. In particular, the following topics are emphasised: thermodynamics of materials, material modeling, multi-physics, mechanical properties of materials, homogenisation, phase transitions, fracture and damage mechanics, vibration, wave propagation experimental mechanics as well as machine learning techniques in the context of applied mechanics.
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