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Inverse parameter identification of n-segmented multilinear cohesive laws using parametric finite element modeling

机译:使用参数有限元建模的N分段多线性凝聚法的逆参数识别

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摘要

Delamination in laminated composites are efficiently modelled with the cohesive zone model (CZM). The shape of the cohesive law becomes important when simulating delamination in material systems experiencing large scale fiber bridging, as several competing damage mechanisms occurs in the fracture process zone at multiple length scales. For this purpose a multilinear cohesive law has recently been developed in [1], which readily can be adapted to a variety of shapes. However, a key challenge in applying such cohesive laws is their model calibration, i.e. identification of parameters that define the shape of the cohesive law. In this work, a new methodology for experimental characterization of multilinear cohesive laws is proposed. The methodology is an inverse approach, which identifies cohesive laws by iteratively varying cohesive zone parameters using a gradient-based optimization scheme to minimize the error in structural response between a fracture mechanical experiment and a parametric finite element model. The method is demonstrated on a moment loaded double cantilever beam (DCB) specimen made of unidirectional glass-epoxy showing large-scale fiber bridging. Multilinear cohesive laws are characterized which result in an excellent agreement between the finite element simulation and the experiment. The results and sensitivity studies demonstrate the accuracy and robustness of the proposed methodology, even for a large number of design variables in the optimization problem.
机译:层压复合材料中的分层用粘性区域模型(CZM)有效地建模。在经历大规模纤维桥接的材料系统中模拟分层时,粘性定律的形状变得重要,因为在多个长度尺度的断裂过程区域中发生了几种竞争损伤机制。为此目的,最近在[1]中开发了一种多线性粘性法,该方法很容易适应各种形状。然而,应用这种凝聚力定律的关键挑战是它们的模型校准,即确定凝聚凝聚律形状的参数。在这项工作中,提出了一种新的多线性凝聚法实验表征的新方法。该方法是一种逆方法,其通过使用基于梯度的优化方案迭代地改变凝聚区域参数来识别粘性定律,以最小化裂缝机械实验与参数化有限元模型之间的结构响应中的误差。该方法在加载的双悬臂梁(DCB)样本上进行了说明,由单向玻璃环氧树脂制成,显示出大规模纤维桥接。多线性凝聚性法律的特点是有限元模拟与实验之间的优异一致性。结果和敏感性研究表明了所提出的方法的准确性和稳健性,即使在优化问题中的大量设计变量也是如此。

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