摘要
从2.5维机织复合材料细观几何结构出发,讨论了织造及成型工艺对材料实际细观几何结构的影响,并分析了不同纱线截面及路径假设所建立代表性体积胞的优缺点。对考虑工艺因素的代表性体积胞是否具有统一性和体素单元法建模的合理性做出对比分析。在代表性体积胞的基础上,重点讨论了2.5维机织复合材料刚度的数值预测方法,并将其与刚度的理论预测方法作了简单对比。基于刚度的数值预测方法,分析了各种机织参数对刚度系数的影响,并且对比了不同代表性体积胞模型的应力场。此外,还讨论了2.5维机织复合材料叶片的建模分析技术与刚度设计方法。最后,指出了当前2.5维机织复合材料细观建模及刚度预测方法存在的问题与不足,并且对其力学性能研究的发展趋势进行了展望。
The effects of weaving and molding processes on the actual meso-geometry of the material are discussed in terms of the meso-geometry of 2.5D woven composites,and the advantages/disadvantages of repre⁃sentative volume cells established by different assumptions of yarn cross-section and path are analyzed.A com⁃parative analysis is made on whether the representative volume cells considering process factors are uniform and the rationality of the voxel cell method of modeling.Based on the representative volume cells,the numerical pre⁃diction method of the stiffness of 2.5D woven composites is focused on and is briefly compared with the theoretical prediction method.Based on the numerical prediction method of stiffness,the effects of various weaving parame⁃ters on the stiffness coefficients are analyzed,and the stress fields of different representative volume cell models are compared.In addition,the modeling analysis techniques and stiffness design methods for 2.5D woven compos⁃ite blades are discussed.Finally,the problems and shortcomings of the current meso-scale modeling and stiffness prediction methods for 2.5D woven composites are pointed out,and the development trend of their mechanical properties is prospected.
作者
崔海涛
郭俊华
张宏建
温卫东
CUI Hai-tao;GUO Jun-hua;ZHANG Hong-jian;WEN Wei-dong(Aero-Engine Thermal Environment and Structure Key Laboratory of Ministry of Industry and Information Technology,College of Energy and Power Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)
出处
《推进技术》
EI
CAS
CSCD
北大核心
2022年第8期1-20,共20页
Journal of Propulsion Technology
基金
国家科技重大专项(2017-Ⅳ-0007-0044)
国家自然科学基金(52175142)
关键词
复合材料
细观结构
几何模型
刚度模型
叶片结构
Composites
Mesostructure
Geometrical model
Stiffness prediction model
Blade struc⁃ture