Xiaolin Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Xiaolin Wang has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Ding Liu
State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
1 shared publication - Jing Zhang
State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology, Xuzhou 221116, China
1 shared publication - Liyuan Yu
State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering, China University of Mining and Technology, Xuzhou, China
1 shared publication - Richeng Liu
State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering, China University of Mining and Technology, Xuzhou, P.R. China
1 shared publication - Shuchen Li
State Key Laboratory of Public Big Data, Guizhou University, Guizhou, Guiyang 550025, China
1 shared publication
Latest Publications
- Open Access
An Equivalent Pipe Network Modeling Approach for Characterizing Fluid Flow through Three-Dimensional Fracture Networks: Verification and Applications
The equivalent pipe network (EPN) model is an effective way to model fluid flow in large-scale fractured rock masses with a complex fracture network due to its straightforwardness and computational efficiency. This study presents the EPN model for characterizing fluid flow through three-dimensional fracture networks using the Monte-Carlo method. The EPN model is extracted from an original three-dimensional discrete fracture network (DFN) model and is used to simulate the fluid flow processes. The validity of the proposed EPN modeling approach is verified via the comparisons of permeability (k) with analytical solutions and simulation results reported in the literature. The results show that the numerically calculated k using EPN models agrees well with the analytical values of simplified DFN models and the simulation results of complex DFN models. The k increases following an exponential function with the increment of mean length of exponentially distributed fractures (u), which is strongly correlated with fracture density (P32) and average intersection length (Li). The P32 increases in an exponential way with the increment of u. The Li increases as u increases, following a power-law function. The increment of u leads to the increment of a number of long fractures in three-dimensional DFN models. A larger u results in a denser fracture network and a stronger conductivity when the number and length distribution range of fractures remain the same. The representative elementary volumes (REVs) of three-dimensional DFN models with u = 9 m and P32 = 0.4 m2/m3 are determined as 2.36 × 104 m3, 9.16 × 103 m3, and 1.26 × 104 m3 in 3 flow directions, respectively.
Open AccessAn Equivalent Pipe Network Modeling Approach for Characterizing Fluid Flow through Three-Dimensional Fracture Networks: Verification and Applications
The equivalent pipe network (EPN) model is an effective way to model fluid flow in large-scale fractured rock masses with a complex fracture network due to its straightforwardness and computational efficiency. This study presents the EPN model for characterizing fluid flow through three-dimensional fracture networks using the Monte-Carlo method. The EPN model is extracted from an original three-dimensional discrete fracture network (DFN) model and is used to simulate the fluid flow processes. The validity of the proposed EPN modeling approach is verified via the comparisons of permeability (k) with analytical solutions and simulation results reported in the literature. The results show that the numerically calculated k using EPN models agrees well with the analytical values of simplified DFN models and the simulation results of complex DFN models. The k increases following an exponential function with the increment of mean length of exponentially distributed fractures (u), which is strongly correlated with fracture density (P32) and average intersection length (Li). The P32 increases in an exponential way with the increment of u. The Li increases as u increases, following a power-law function. The increment of u leads to the increment of a number of long fractures in three-dimensional DFN models. A larger u results in a denser fracture network and a stronger conductivity when the number and length distribution range of fractures remain the same. The representative elementary volumes (REVs) of three-dimensional DFN models with u = 9 m and P32 = 0.4 m2/m3 are determined as 2.36 × 104 m3, 9.16 × 103 m3, and 1.26 × 104 m3 in 3 flow directions, respectively.