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A two-way loose coupling procedure for investigating the buckling and damage behaviour of stiffened composite panels

机译:一种双向松耦合程序,用于研究加劲复合板的屈曲和损坏行为

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摘要

This paper is concerned with the development of a novel FE-based two-way loose coupling approach for the analysis of stiffened composite panels. The aim of this numerical strategy is to investigate the global postbuckling behaviour as well as the local damage progression of composite structures using separated FE models with different levels of fidelity: (i) a relatively simple global model of the complete structure, and (ii) more complex local models of certain details that incorporate damage capabilities to simulate damage events. In the coupling process, information is exchanged between these diverse models to simulate the overall structural behaviour including geometrical as well as material nonlinearities. The two-way loose coupling character of the methodology allows, firstly, a direct interaction between the global and the local levels along the solution process and, secondly, a highly versatile adaption with regard to the definition of the local models. In addition, the separation of the models and analyses in the approach enables the use of standard FE software without complex implementations or modifications of the source code. The developed coupling procedure is assessed through two applications: (i) an academic composite stiffened panel, and (ii) a real stiffened panel taken from literature. The results of the proposed coupling approach are compared with the numerical and experimental reference data, exhibiting a satisfactory level of accuracy. (C) 2015 Elsevier Ltd. All rights reserved.
机译:本文涉及一种新型的基于有限元的双向松耦合方法,用于分析加筋的复合板。此数值策略的目的是使用具有不同保真度的分离有限元模型来研究复合结构的整体后屈曲行为以及局部破坏进程:(i)相对简单的完整结构的整体模型,以及(ii)具有某些细节的更复杂的局部模型,其中包含了破坏功能以模拟破坏事件。在耦合过程中,在这些不同的模型之间交换信息以模拟总体结构行为,包括几何以及材料非线性。该方法的双向松散耦合特性首先允许在解决方案过程中全局级别和局部级别之间进行直接交互,其次允许对本地模型的定义进行高度通用的调整。此外,该方法中模型和分析的分离使得无需使用复杂的实现或修改源代码即可使用标准的有限元软件。通过两个应用程序评估开发的耦合程序:(i)学术复合材料加劲板,以及(ii)取自文献的真实加劲板。所提出的耦合方法的结果与数值和实验参考数据进行了比较,显示出令人满意的精度水平。 (C)2015 Elsevier Ltd.保留所有权利。

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