正交各向异性材料应力球-偏拉压分解的断裂相场模型

Phase-field fracture model of anisotropic materials based on stress volumetric-deviatoric split

断裂相场法被广泛应用于各向同性和复合材料的断裂分析之中。目前,针对各向异性材料和复合材料的断裂问题仍然是断裂相场法的研究重点。本文基于应力拉压的球偏分解方式对弹性应变能进行分解,排除了压缩体积应变能造成的裂纹扩展,并考虑材料拉、压本构关系的非对称情况,建立了一个针对正交各向异性材料断裂问题的相场分析模型。为了验证模型的可靠性,分别对各向同性材料、正交各向异性材料的单边开口板的拉伸和剪切问题进行了分析。对单向纤维夹杂的复合材料板,应用Hashin准则对损伤裂纹驱动力进行了修正,并对不同碳纤维铺设方向复合材料板的拉伸问题进行了模拟。本文建立的模型能够模拟各向异性材料和单向纤维夹杂复合材料的裂纹扩展问题。对于各向同性材料和正交各向异性材料,模拟得到的裂纹扩展路径与现有模型一致,复合材料中裂纹扩展方向和纤维铺设方向平行,预测结果和实验结果吻合良好。

The phase field method,recognized for its effectiveness in fracture analysis,particularly of isotropic and composite materials,remains a key research focus when considering the intricacies of fractures in anisotropic materials and their composites.In this study,a refined approach to decomposing elastic strain energy was presented.This method excludes the compressive volumetric strain energy influence on crack propagation and accounts for asymmetries in material constitutive relationships under tension and compression.Leveraging this,a tailored phase field analysis model for orthotropic material fractures was constructed.To validate the model's robustness and applicability,this work conducted analyses on unidirectional opening plates made of isotropic and orthotropic materials,focusing on tensile and shear boundary conditions.For composite plates with unidirectional fiber reinforcement,the Hashin criterion was incorporated to refine the quantification of damage-induced crack driving forces.This facilitated accurate simulations of tensile behavior in composite plates with varying carbon fiber orientations.Our findings demonstrate the profound capability of our theoretical framework in simulating crack propagation in anisotropic materials and unidirectional fiber-reinforced composites.The predicted crack propagation paths align with those from established models for both isotropic and orthotropic materials,attesting to the model's fidelity.Within composites,we observed a strong alignment between crack propagation and fiber orientation,highlighting a robust correlation between our predictions and experiment results.