Experimental and Numerical Studies of Water–Sand Flow in Fractured Porous Media

Wei Li,Yu Liu,Shuncai Li,Liqiang Ma, Lei Yue, Jintao Wang

Rock Mechanics and Rock Engineering(2024)

Cited 0|Views8
No score
Abstract
This study aims to investigate the flow characteristics of water–sand mixture in fractured porous media through a series of laboratory experiments. The experiments focus on examining the transport characteristics of water–sand mixture with varying grain sizes, glass bead gradations, sand mass fraction and outlet conditions. The results show that an increase in grain size causes a more pronounced nonlinear variation in large pressure gradient, resulting in a predominantly longitudinal flow pattern with a 'multi-arc' flow state. Water–sand mixture with various glass bead gradations exhibits aggregation effects in porous media. The discrete element method–computational fluid dynamics (DEM–CFD) two-way coupling method is used to simulate water–sand flow in fractured porous media using the three-dimensional particle flow code (PFC3D). The simulation shows that the fracture has a more significant constraint effect than the pore due to the significant variation in internal contact force. A response surface optimization method is utilized to establish multi-factor response models. The correlation between flow, fracture inlet pressure, pore inlet pressure and water–sand mass is analyzed to predict accurately the variation of water–sand outflow. Based on the least square method, multivariate regression fitting equations are developed using sand grain size, gradation and sand mass fraction to predict the dynamics of fluidity I and non-Darcy factor β. The equations are found to be effective in forecasting the dynamics of these factors and provides valuable insights for assessing. These results can be used to predict the movement behavior of fluid in fracture-pore composite media and provide theoretical guidance for preventing disasters such as water–sand inrush.
More
Translated text
Key words
Fractured porous media,Multi-factor response models,Multivariate regression fitting equations,Water–sand inrush
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined