基于双向随机微力学模型的复合材料内聚强度预测

doi:10.19936/j.cnki.2096-8000.20250528.005

摘要/Abstract

摘要： 针对复合材料损伤实效机理复杂的问题,本文从细观力学机理角度,通过微观代表性体积单元(RVE),建立复合材料双向随机微力学模型来表征结构内聚层,用于探究复合材料三种分层模式下的内聚强度。选取扩展的线性Drucker-Prager屈服准则,参数化定义随机微观结构,预测裂纹在基体中的萌生现象。预测结果表明,内聚强度在很大程度上取决于由最小垂直纤维间距、最小横向纤维间距、纤维垂直角以及纤维铺层角参数描述的随机微观结构形态。从物理机制的角度出发,提出了内聚强度计算框架,本研究在预测复合材料层合板脱层行为方面具有重要的研究意义,可为工程实践中的结构设计和性能优化提供重要依据。

关键词: 复合材料, 分层, 代表性体积单元, 内聚强度

Abstract: To address the complexity of the damage effectiveness mechanism in composites, this paper presents a bidirectional stochastic micromechanical model to characterize the structural cohesive layers using microscopic representative volume units (RVEs) from a micromechanical perspective. This model is used to investigate the cohesive strength of composites under three delamination modes. An extended linear Drucker-Prager yield criterion is selected to parametrically define the stochastic microstructure and predict the initiation of cracks in the matrix. The prediction results demonstrate that the cohesive strength is strongly influenced by the random microstructure morphology, specifically the minimum vertical fiber spacing, minimum transverse fiber spacing, fiber vertical angle, and fiber lay-up angles parameters. By proposing a cohesive strength calculation framework based on the physical mechanism, this study has significant research implications in predicting delamination behavior in composite laminates. Furthermore, it provides an important basis for structural design and performance optimization in engineering applications.

Key words: composites, delamination, representative volume unit, cohesive strength

中图分类号:

TB332

许明朝, 田阿利, 王千一, 罗怡. 基于双向随机微力学模型的复合材料内聚强度预测[J]. 复合材料科学与工程, 2025, 0(5): 38-44.

XU Mingchao, TIAN Ali, WANG Qianyi, LUO Yi. Prediction of cohesive strength of composites based on a bidirectional stochastic micromechanical model[J]. COMPOSITES SCIENCE AND ENGINEERING, 2025, 0(5): 38-44.

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