An adaptive stochastic multi-scale method for cohesive fracture modelling of quasi-brittle heterogeneous materials under uniaxial tension

被引:9
|
作者
Sencu, R. M. [1 ]
Yang, Z. [2 ]
Wang, Y. C. [1 ]
机构
[1] Univ Manchester, Sch Mech Aerosp & Civil Engn, Manchester M13 9PL, Lancs, England
[2] Coventry Univ, Fac Engn Comp & Environm, Coventry CV1 2JH, W Midlands, England
基金
英国工程与自然科学研究理事会;
关键词
Multi-scale stochastic fracture mechanics; Scale coupling; Cohesive crack model; Overlapping elements; Fibre reinforced plastics; CONCURRENT MULTILEVEL MODEL; REPRESENTATIVE VOLUME; COMPUTATIONAL HOMOGENIZATION; COMPOSITE-MATERIALS; FAILURE; CONCRETE; BEHAVIOR; 2-SCALE; DAMAGE; SIZE;
D O I
10.1016/j.engfracmech.2016.02.040
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
An adaptive stochastic multi-scale method is developed for cohesive fracture modelling of quasi-brittle heterogeneous materials under uniaxial tension. In this method, a macro domain is first discretised into a number of non-overlapping meso-scale elements (MeEs) each of which containing detailed micro-scale finite element meshes. Potential discrete cracks in the MeEs are modelled by pre-inserted cohesive interface elements (CIEs). Nonlinear simulations are conducted for the MeEs to obtain the crack patterns under different boundary conditions. The macro-domain with the same number of overlapped, adaptively size-increasing MeEs are then simulated, until the potential cracks seamlessly cross the boundaries of adjacent MeEs. The resultant cracks, after being filtered by a new Bayesian inference algorithm to remove spurious cracks wherever necessary, are then integrated as CIEs into a final anisotropic macro-model for global mechanical responses. A two-dimensional example of carbon fibre reinforced polymers was modelled under two types of uniaxial tension boundaries. The developed method predicted crack patterns and load-displacement curves in excellent agreement with those from a full micro-scale simulation, but consuming considerably less computation time of the latter. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:499 / 522
页数:24
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