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

机译：基于参数有限元建模的n段多线性内聚律反参数识别

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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：使用内聚区模型（CZM）有效地对层压复合材料中的分层进行建模。当在经历大规模纤维桥接的材料系统中模拟分层时，内聚规律的形状变得很重要，因为在断裂过程区域中，在多个长度尺度上会发生多种竞争性损坏机制。为此，最近在[1]中开发了一种多线性内聚定律，该定律可以很容易地适应各种形状。然而，应用这种内聚规律的关键挑战是它们的模型校准，即，识别定义内聚规律形状的参数。在这项工作中，提出了一种用于表征多线性内聚规律的新方法。该方法是一种反向方法，该方法使用基于梯度的优化方案通过迭代地更改内聚区参数来识别内聚规律，以最大程度地降低断裂力学实验和参数化有限元模型之间的结构响应误差。该方法在由单向玻璃环氧树脂制成的矩加载双悬臂梁（DCB）样品上得到了证明，该样品显示出大规模的光纤桥接。表征了多线性凝聚力定律，从而使有限元模拟和实验之间达到了极好的一致性。结果和灵敏度研究证明了所提出方法的准确性和鲁棒性，即使对于优化问题中的大量设计变量也是如此。

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