Time-dependent behavior of subcritical crack growth for rock plate: Experimental and numerical study | |
Wang, Yixian1,2,6; Chen, Wen3; Bian, Hanbing3; Liu, Yan4; Wang, Feng6; Yuan, Haiping5,6 | |
刊名 | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS |
2018-11-14 | |
卷号 | 14期号:11页码:10 |
关键词 | Double-torsion test subcritical crack growth stress corrosion time-dependent Particle Flow Code in 3 Dimensions |
ISSN号 | 1550-1477 |
DOI | 10.1177/1550147718812019 |
英文摘要 | Time-dependent behavior of subcritical crack growth is one of the main characteristics in rocks. The double-torsion test is commonly used to study the slow crack growth behavior of brittle and quasi-brittle materials. However, double-torsion specimen is difficult to processing, the process of the laboratory test is irreversible, and the current numerical simulation is difficult to consider the time-dependent behavior, and so on. In view of all above problems, an idealized particle model was built, and the crack was identified in this article, based on the theory of particle flow. The numerical model was built using Particle Flow Code in 3 Dimensions, and the macromechanical and micromechanical parameters of the model were calibrated. The process of the macroscopic crack propagation and its evolution were analyzed. The intrinsic relations with the load, the displacement, and the time were established. The results show that the Particle Flow Code in 3 Dimensions can reproduce the time-dependent behavior of subcritical crack growth in double-torsion test. And, the peak and the law of the curves are in good agreement with the laboratory test results. Therefore, the Particle Flow Code in 3 Dimensions numerical simulation can be used as a new effective method to reveal the slow crack growth behavior, to get the relevant parameters such as V, K-I, and K-IC, and to build the relationship between V and K-I. The results of this article will have some reference value for the simulation and application of double-torsion test. |
资助项目 | National Natural Science Foundation of China[51874112] ; National Natural Science Foundation of China[51774107] ; State Engineering Laboratory of Highway Maintenance Technology, Changsha University of Science and Technology[kfj170108] ; Opening Project of State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology[KFJJ17-12M] ; Open Program of State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology[SKLGDUEK1406] ; Open Research Fund of State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences[Z013010] |
WOS研究方向 | Computer Science ; Telecommunications |
语种 | 英语 |
出版者 | SAGE PUBLICATIONS INC |
WOS记录号 | WOS:000450110000001 |
内容类型 | 期刊论文 |
源URL | [http://202.127.146.157/handle/2RYDP1HH/6081] |
专题 | 中国科学院武汉植物园 |
通讯作者 | Wang, Yixian |
作者单位 | 1.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan, Hubei, Peoples R China 2.China Univ Min & Technol, State Key Lab Coal Resources & Safety Min, Xuzhou, Jiangsu, Peoples R China 3.Univ Lorraine, CNRS, LEM3, Metz, France 4.Beijing Inst Technol, State Key Lab Explos Sci & Technol, Beijing, Peoples R China 5.Changsha Univ Sci & Technol, State Engn Lab Highway Maintenance Technol, Changsha, Hunan, Peoples R China 6.Hefei Univ Technol, Sch Civil Engn, Hefei 230009, Anhui, Peoples R China |
推荐引用方式 GB/T 7714 | Wang, Yixian,Chen, Wen,Bian, Hanbing,et al. Time-dependent behavior of subcritical crack growth for rock plate: Experimental and numerical study[J]. INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS,2018,14(11):10. |
APA | Wang, Yixian,Chen, Wen,Bian, Hanbing,Liu, Yan,Wang, Feng,&Yuan, Haiping.(2018).Time-dependent behavior of subcritical crack growth for rock plate: Experimental and numerical study.INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS,14(11),10. |
MLA | Wang, Yixian,et al."Time-dependent behavior of subcritical crack growth for rock plate: Experimental and numerical study".INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 14.11(2018):10. |
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