Identification of Network Physical Properties in Simulation of Flow Through Porous Media Using Network Model

Simulation of flow phenomena in porous media occur in many areas of sciences and engineering. It has wide applications in a variety of disciplines including water resources engineering, environmental and chemical engineering, petroleum engineering, and groundwater hydrology. Both theoretical and experimental studies conducted to further our understanding of flow and transport phenomena in porous media are based on Darcy and Forchheimer constitutive equations. In recent years, a few investigators considered converting original porous media into a 2-D and/or 3-D networks to address challenging and complex issues in porous media. Such a network can be conceptualized as consisting of a series of pore bodies and throats. Pore body comprises the void between grains and throat represents the channel connecting two pore bodies. The basic question is how to choose throat’s length and size. In this study, two porous media of uniform size (i.e., spherical balls) but different diameters were constructed in the hydraulic laboratory of school of engineering, Shiraz University. A methodology is developed to convert each porous media into an equivalent network and the resulting network is subjected to rigorous computer simulation. Validity of such conversion is achieved via triggering and monitoring the two actual porous media in the laboratory. For this purpose, the equivalent network of first porous media is calibrated Preprint submitted to Journal of Water and Wastewater Engineering 5 May 2008 for pipe roughness using three different resistance equations. As both porous media have the same surface roughness characteristics, the equivalent network of second porous media is solved in a forward manner with different upstream heads using roughness coefficient obtained from the first porous media. Observed and simulated water surface profiles and outflow discharges from the second porous media are compared and contrasted to each other. Results show a good agreement between predicted values of the network model and experimental data obtained in the laboratory