For many aggregate mining facilities, the stockpile is the preferred method of storing rock. In many aggregate mines, as well as other mines using stockpiling techniques, understanding the timing and flow of particles through a stockpile is important for correctly timing samples, making proper process adjustments and overall stockpile safety. Because much of the research of today lacks important information regarding actual interior particle movement within a stockpile, a series of Real Time Distribution (RTD) analyses and stockpile flow models have been prepared and analyzed for this study in order to better understand the flow characteristics of a stockpile.

A series of three RTD analyses performed on three separate stockpiles provides information leading to the assumption that stockpiles tend to operate similar to a plug flow system. While conveyor loading techniques may lead to separation of rocks prior to traveling through the stockpile, the majority of the rock particles entering the pile remain near the point of entry, or within the "action" area, and will travel through the pile in a plug flow, rather than a mixed flow, manner. High Peclet number results for each analysis prove this assumption to be accurate.

A series of models on three separate stockpiles have been created using PFC3d. Mainly, the simulations prove PFC3d is capable of showing how stockpile particles move in three dimensions while monitoring specific particles within the pile. In addition, these models provide simulation results similar to the results obtained within the RTD analyses. Results show that particles located directly above the discharge point, or "action" area, travel through the pile at a faster rate than particles surrounding this area. Velocity results obtained from the simulations show particles accelerating as they get closer to the discharge points while also providing evidence of "arching" during the simulation process. These findings provide a better understanding of internal flow within the stockpile and ways to possibly predict future stockpile flow issues that may be encountered.