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水-力耦合下类岩材料相邻裂纹时效扩展和节理岩体本构模型、多物理场耦合岩石变形近场动力学模型

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关键词:应力强度因子、相邻裂纹、水-力耦合、近场动力学、裂纹扩展、损伤本构模型、力学特性  
TAFMTime-dependent propagation and interaction behavior of adjacent cracks in rock-like material under hydro-mechanical coupling  

-力耦合作用下类岩石材料中相邻裂纹的时效扩展及相互作用  

摘要内容  

在渗流压力和地应力的长期耦合作用下,岩体中裂纹的相互作用和扩展呈现出典型的时效特征,这种特征直接控制着岩体的破坏模式,本研究以含相邻预制裂纹的水泥砂浆试件为研究对象,开展了长期水-力耦合室内试验,利用低频核磁共振技术分析了试件中微裂纹的演化规律,进行了相应的数值模拟,并研究了相邻裂纹的扩展和相互作用以及岩石蠕变破坏的时效机制。研究结果表明:含相邻裂隙岩体的蠕变破坏呈现拉剪复合破坏模式,宏观断裂是由单条裂纹扩展形成的,与相邻裂纹不连通;由于裂纹间的相互作用效应,试件内部形成了大量微裂纹,相邻预制裂纹的扩展不均匀,主裂纹的上下翼裂纹扩展不对称;在裂纹萌生阶段,裂纹扩展受剪切断裂模式的控制,相互作用表现为对相邻裂纹的抑制;在扩展到一定程度后,主裂纹扩展受拉伸断裂模式的控制,其扩展速率增加,加速了岩体的破坏。  

含相邻预制裂纹水泥砂浆试件的制作过程  

含相邻裂纹试件的数值模型: (a) 网格划分; (b) 积分域

相邻裂纹随时间的扩展和相互作用模式: (a) 基本扩展模式; (b) 扩展过程

断裂参数沿相邻裂纹前缘的分布和变化: (a) KI; (b) KII; (c) KIII; (d) Ke

文献信息  

J. Mei, X. Sheng, L. Yang, Y. Zhang, H. Yu, W. Zhang. Time-dependent propagation and interaction behavior of adjacent cracks in rock-like material under hydro-mechanical coupling. Theoretical and Applied Fracture Mechanics, 2022, 122: 103618. DOI: 10.1016/j.tafmec.2022.103618.

https://www.sciencedirect.com/science/article/pii/S0167844222003627  

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TAFMPeridynamic model of deformation and failure for rock material under the coupling effect of multi-physical fields

多物理场耦合作用下岩石材料变形与破坏的近场动力学模型  

摘要内容  

本文提出了一种热--力耦合的近场动力学(PD)模型,用于模拟岩石材料在多物理场耦合作用下的变形和破坏以及流动和温度扩散,该研究利用近场动力学最小二乘(PD-LSM)方法,将热-力耦合、水-力耦合、热--力耦合作用下的热传导和流动扩散方程细化为PD形式,并在基于键的PD模型框架下建立了热--力耦合的运动方程。通过对比稳态导热、热变形、瞬态流动、水力压裂、热固结和考虑热效应的水力压裂等问题的解析解和数值结果,本文验证了耦合PD模型的有效性,对比结果表明,本文所提出的热--力耦合PD模型能较好地反映岩石材料在多物理场耦合作用下的变形破坏过程。  

岩石材料中的三个区域  

2 PDFEM的温度对比

3 (a) THM--力耦合, (b) HM-力耦合, (c) TM-力耦合 的裂纹扩展

文献信息  

Y. Zhang, S. Yu, H. Deng.Peridynamic model of deformation and failure for rock material under the coupling effect of multi-physical fields.  Theoretical and Applied Fracture Mechanics, 2023, 125: 103912. DOI: 10.1016/j.tafmec.2023.103912.

https://www.sciencedirect.com/science/article/pii/S0167844223001751  


TAFMDamage constitutive model and mechanical properties of jointed rock mass under hydro-mechanical coupling

-力耦合作用下节理岩体的损伤本构模型及力学特性  

摘要内容  

-力耦合作用下节理岩体的损伤演化规律和力学性能弱化机制是深部岩体开挖中的重要科学问题,本文分析了岩石试件的矿物成分,研究了花岗岩在不同轴向应力下的核磁共振T2谱分布规律,研究了节理岩样在三轴应力和渗透压力作用下的力学特性,本文通过对节理尖端应力场的分析建立了节理岩体的损伤模型,并对损伤变量张量进行了分析,计算了渗透压力作用下的应力强度因子。渗流压力促进了节理岩体试件的损伤演化过程,加速了节理岩体试件的强度破坏,本文建立并验证了渗透压力作用下节理岩体的损伤本构模型,基于渗流压力弱化规律,本文定制开发了水-力耦合本构模型,并以Mohr-Coulomb本构模型计算函数为编译模板将其导入FLAC3D中,本文还建立了地下岩体工程三维数值模拟模型,分析了不同节理倾角下岩体的稳定性。  

不同倾角的非贯通节理岩体试件  

不同节理倾角下不同试件的力学特性

破碎围岩巷道计算模型

节理岩体中巷道的主应力

文献信息  

B. Yan, H. Kang, X. Li, Q. Qi, B. Zhang, J. Liu. Damage constitutive model and mechanical properties of jointed rock mass under hydro-mechanical coupling. Theoretical and Applied Fracture Mechanics, 2023, 123: 103735. DOI: 10.1016/j.tafmec.2022.103735.  

https://www.sciencedirect.com/science/article/pii/S0167844222004840  



Journal of Computational Physics期刊最新一期论文

2023,492卷)  

Variance reduced particle solution of the Fokker-Planck equation with application to rarefied gas and plasma dynamics

Mohsen Sadr, Nicolas G. Hadjiconstantinou

Department of Mechanical Engineering, MIT, Cambridge, MA 02139, USA  

https://www.sciencedirect.com/science/article/pii/S0021999123004977    


Enforcing continuous symmetries in physics-informed neural network for solving forward and inverse problems of partial differential equations    

Zhi-Yong Zhang a, Hui Zhang a, Li-Sheng Zhang b, Lei-Lei Guo b  

a College of Science, Minzu University of China, Beijing 100081, PR China  

b College of Science, North China University of Technology, Beijing 100144, PR China  

https://www.sciencedirect.com/science/article/pii/S0021999123005107    


Coupling-strength criteria for sequential implicit formulations    

J. Franc a, O. Møyner b, H.A. Tchelepi a  

a Energy Science and Engineering, Stanford University, United States of America  

b SINTEF Digital, Norway  

https://www.sciencedirect.com/science/article/pii/S0021999123005089    


Quasiperiodic perturbations of Stokes waves: Secondary bifurcations and stability    

Sergey A. Dyachenko a, Anastassiya Semenova b  

a Department of Mathematics, University at Buffalo, 244 Mathematics Building, Buffalo, 14260, NY, USA  

b Department of Applied Mathematics, University of Washington, Lewis Hall 201, Seattle, 98195, WA, USA  

https://www.sciencedirect.com/science/article/pii/S0021999123005065    


A family of structure-preserving exponential time differencing Runge–Kutta schemes for the viscous Cahn–Hilliard equation    

Jingwei Sun, Hong Zhang, Xu Qian, Songhe Song  

Department of Mathematics, National University of Defense Technology, Changsha 410073, PR China  

https://www.sciencedirect.com/science/article/pii/S0021999123005090    


Discrete-time nonlinear feedback linearization via physics-informed machine learning    

Hector Vargas Alvarez a, Gianluca Fabiani a d, Nikolaos Kazantzis b, Constantinos Siettos c, Ioannis G. Kevrekidis d e f  

a Scuola Superiore Meridionale, Naples, Italy  

b Department of Chemical Engineering, Worcester Polytechnic Institute, Worcester, USA  

c Dipartimento di Matematica e Applicazioni “Renato Caccioppoli”, Università degli Studi di Napoli “Federico II”, Naples, Italy  

d Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, USA  

e Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, USA  

f Medical School, Department of Urology, Johns Hopkins University, Baltimore, USA  

https://www.sciencedirect.com/science/article/pii/S002199912300503X    


A Cartesian-octree adaptive front-tracking solver for immersed biological capsules in large complex domains    

Damien P. Huet a, Anthony Wachs a b  

a Department of Mathematics, University of British Columbia, 1984 Mathematics Road, BC V6T 1Z2, Vancouver, Canada  

b Department of Chemical & Biological Engineering, University of British Columbia, 2360 E Mall, BC V6T 1Z3, Vancouver, Canada  

https://www.sciencedirect.com/science/article/pii/S0021999123005193    


Analysis of the immersed boundary method for turbulent fluid-structure interaction with Lattice Boltzmann method    

Isabelle Cheylan, Tom Fringand, Jérôme Jacob, Julien Favier  

Aix Marseille Univ, CNRS, Centrale Marseille, M2P2 UMR 7340, 13451 Marseille cedex 13, France  

https://www.sciencedirect.com/science/article/pii/S0021999123005132    


Efficient Bayesian inference with latent Hamiltonian neural networks in No-U-Turn Sampling    

Somayajulu L.N. Dhulipala a, Yifeng Che a, Michael D. Shields b  

a Computational Mechanics and Materials, Idaho National Laboratory, Idaho Falls, ID 83415, USA  

b Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218, USA  

https://www.sciencedirect.com/science/article/pii/S002199912300520X    


Data-driven probability density forecast for stochastic dynamical systems    

Meng Zhao, Lijian Jiang  

School of Mathematical Sciences, Tongji University, Shanghai 200092, China  

https://www.sciencedirect.com/science/article/pii/S002199912300517X    


A comparative study of scalable multilevel preconditioners for cardiac mechanics    

Nicolás A. Barnafi a, Luca F. Pavarino b, Simone Scacchi c  

a Instituto de Ingeniería Biológica y Médica, Pontificia Universidad Católica de Chile, Chile  

b Department of Mathematics, Università degli Studi di Pavia, Italy  

c Department of Mathematics, Università degli Studi di Milano, Italy  

https://www.sciencedirect.com/science/article/pii/S0021999123005168    


Neural-network-augmented projection-based model order reduction for mitigating the Kolmogorov barrier to reducibility    

Joshua Barnett a, Charbel Farhat a b c, Yvon Maday d  

a Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, United States of America  

b Department of Aeronautics and Astronautics, Stanford University, Stanford, CA 94305, United States of America  

c Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, United States of America  

d Laboratoire Jacques-Louis Lions (LJLL), Sorbonne Université and Université de Paris Cité, CNRS, F-75005 Paris, France  

https://www.sciencedirect.com/science/article/pii/S0021999123005156    


Convex optimization-based structure-preserving filter for multidimensional finite element simulations    

Vidhi Zala a b, Akil Narayan a c, Robert M. Kirby a b  

a Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, United States of America  

b School of Computing, University of Utah, Salt Lake City, UT 84112, United States of America  

c Department of Mathematics, University of Utah, Salt Lake City, UT 84112, United States of America  

https://www.sciencedirect.com/science/article/pii/S002199912300459X    


Structure-preserving and helicity-conserving finite element approximations and preconditioning for the Hall MHD equations    

Fabian Laakmann, Kaibo Hu, Patrick E. Farrell  

Mathematical Institute, University of Oxford, Oxford, UK  

https://www.sciencedirect.com/science/article/pii/S0021999123005053  


Integral equation methods for the Morse-Ingard equations    

Xiaoyu Wei a, Andreas Klöckner a, Robert C. Kirby b  

a Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, United States  

b Department of Mathematics, Baylor University, Waco, TX, United States  

https://www.sciencedirect.com/science/article/pii/S0021999123005119    


Analysis on physical-constraint-preserving high-order discontinuous Galerkin method for solving Kapila's five-equation model    

Fan Zhang a, Jian Cheng b  

a School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, PR China  

b Institute of Applied Physics and Computational Mathematics, Beijing 100088, PR China  

https://www.sciencedirect.com/science/article/pii/S0021999123005120  


A hybrid deep neural operator/finite element method for ice-sheet modeling    

QiZhi He a, Mauro Perego b, Amanda A. Howard c, George Em Karniadakis c d, Panos Stinis c  

a Department of Civil, Environmental, and Geo- Engineering, University of Minnesota, 500 Pillsbury Drive S.E., Minneapolis, MN 55455, United States of America  

b Center for Computing Research, Sandia National Laboratories, P.O. Box 5800, Albuquerque, NM 87185, United States of America  

c Advanced Computing, Mathematics and Data Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States of America  

d Division of Applied Mathematics and School of Engineering, Brown University, 182 George Street, Providence, RI 02912, United States of America  

https://www.sciencedirect.com/science/article/pii/S0021999123005235  


An efficient energy conserving semi-Lagrangian kinetic scheme for the Vlasov-Ampère system    

Hongtao Liu a b, Xiaofeng Cai c d, Yong Cao b, Giovanni Lapenta a  

a Center for Mathematical Plasma Astrophysics, Department of Mathematics, KU Leuven, Leuven, 3001, Belgium  

b School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China  

c Research Center of Mathematics, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, 519087, China  

d Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, 519087, China  

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Data-driven stochastic particle scheme for collisional plasma simulations    

K. Chung a, F. Fei b, M.H. Gorji c, P. Jenny a  

a Institute of Fluid Dynamics, Swiss Federal Institute of Technology, Sonneggstrasse 3, 8092 Zurich, Switzerland  

b School of Aerospace Engineering, Huazhong University of Science and Technology, 430074 Wuhan, China  

c Laboratory of Multiscale Studies in Building Physics, Empa, Dübendorf, Switzerland  

https://www.sciencedirect.com/science/article/pii/S0021999123004953    


Dimensionality reduction for regularization of sparse data-driven RANS simulations    

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Institute of Fluid Dynamics, Sonneggstrasse 3, ETH Zurich, Zurich CH-8092, Switzerland  

https://www.sciencedirect.com/science/article/pii/S0021999123004990


来源:现代石油人
ACTMechanicalSystemDeform断裂UG裂纹化机材料控制试验
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首次发布时间:2024-05-08
最近编辑:6月前
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