祝贺李辉的论文被期刊International Journal of Solids and Structures(IJSS)接受发表!
论文链接：https://www.sciencedirect.com/science/article/abs/pii/S0020768322004139
3D meso-scale fracture modelling of concrete with random aggregates using a phase-field regularized cohesive zone model
基于相场正则化粘聚裂缝模型的含随机骨料混凝土三维细观断裂模拟
Hui Li, Yujie Huang, Zhenjun Yang*, Kelai Yu, Q.M. Li
Abstract：This study develops a mesoscale fracture modelling method combining the phase-field regularized cohesive zone model (PF-CZM) and random aggregate models for simulating complicated 3D meso-scale damage and fracture in concrete-like quasi-brittle materials. In this method, the aggregates in the mortar are built using a random generating and packing algorithm, and their surrounding mortar-aggregate interfaces are obtained from an aggregate scaling algorithm. The PF-CZM with cohesive softening laws and an intrinsic length scale is used to model multi-crack initiation and propagation in the mortar and interfaces without remeshing. The method was first validated by a concrete cube under uniaxial tension, with the effects of the mesh size, the length scale, the tensile strength, and the fracture energy investigated in detail. Three concrete beam examples, one under mode-I fracture and two under mixed-mode fracture, were then modelled for further validation and demonstration for practical uses. It is found that the method can effectively simulate 3D stochastic fracture processes and accurately calculate load-carrying capacities with little mesh-dependence.
摘要：文中将相场正则化粘聚裂缝模型（PF-CZM）与随机骨料模型相结合，提出了一种混凝土类准脆性材料的三维细观损伤和断裂模拟方法。方法中利用随机骨料生成算法建立了混凝土砂浆中的骨料模型，通过开发骨料缩放算法得到了骨料与砂浆之间的界面。利用PF-CZM模拟了砂浆和界面的多缝起裂和扩展，无需引入网格重划分技术。文中模拟了混凝土立方体的单轴拉伸试验，进行了网格尺寸、相场长度尺寸、抗拉强度和断裂能等参数分析。另外，分别模拟了I型破坏、混合型破坏、以及扭转破坏等3个混凝土梁结构的宏/细观断裂过程。结果表明该方法可以模拟复杂三维随机断裂过程和准确预测混凝土结构承载力，并且计算结果对网格不敏感。
图1. 混凝土单轴拉伸破坏过程
图3. 不同骨料分布下模拟的梁三点弯I型破坏裂缝形态
图4. 混凝土梁受扭破坏过程
图5. 混凝土梁受扭破坏结果与试验对比
该项研究工作得到了国家自然科学基金（No. 51974202 和No. 52173300）、中德合作交流基金（No. M-0172）、湖北省重点研发项目（No. 2020BAB052）、武汉大学-曼彻斯特大学先进材料与结构多尺度研究联合科研平台（No. WHUZZJJ202208）等资助。
This study is funded by National Natural Science Foundation of China (No. 51974202 and No. 52173300), Sino-German Center for Research Promotion (Mobility Programme No. M-0172), Key Research and Development Programme of Hubei Province (No. 2020BAB052), and Key Seed Fund Program for Sino-Foreign Joint Scientific Research Platform of Wuhan University (No. WHUZZJJ202208).