Advanced computational modelling of composite materials

被引:10
|
作者
Cheng, Zheng-Qiang [1 ]
Liu, Hu [1 ]
Tan, Wei [2 ]
机构
[1] Southwest Jiaotong Univ, Sch Mech & Aerosp Engn, Appl Mech & Struct Safety Key Lab Sichuan Prov, Chengdu 610031, Peoples R China
[2] Queen Mary Univ London, Sch Engn & Mat Sci, Mile End Rd, London E1 4NS, England
基金
中国国家自然科学基金; 英国工程与自然科学研究理事会;
关键词
A. Polymer-matrix composites (PMCs); B. Mechanical properties; C. Computational modelling; D. Numerical analysis; FINITE-ELEMENT-ANALYSIS; PROGRESSIVE FAILURE ANALYSIS; FIBER-REINFORCED COMPOSITES; DYNAMIC CRACK-PROPAGATION; VELOCITY IMPACT DAMAGE; PHASE-FIELD MODELS; HIGH-STRAIN RATE; COHESIVE-ZONE; MECHANICAL-BEHAVIOR; BRITTLE-FRACTURE;
D O I
10.1016/j.engfracmech.2024.110120
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
This review paper presents an overview of computational methods for modelling the failure of composite materials, with a focus on fracture modelling. The paper begins by discussing the principles and concepts of continuum damage mechanics (CDM), phase field method (PFM), cohesive zone model (CZM) and discrete element method (DEM), highlighting their ability to predict crack initiation, propagation, and coalescence. The paper also includes case studies and examples that demonstrate the effectiveness and limitations of each method in simulating fracture behaviour in different composite materials. We then review existing methods for modelling the deformation and fracture behaviour of composite material under dynamic loading. Additionally, the significance of multiscale modelling, multi -physics modelling and data -driven methods in composite failure analysis is discussed. Multiscale models provide a comprehensive understanding of deformation and fracture across various length scales, while multi -physics modelling can provide valuable insights into failure mechanisms when multiple physical phenomena are coupled, such as hygrothermal degradation of composite materials. On the other hand, datadriven methods enhance fracture and multiscale modelling through machine learning and statistical techniques. Current challenges and recommendations for future work have also been articulated.
引用
收藏
页数:46
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