Hybrid CFD-Network Methodology for Improved DPM Modelling

"A hybrid methodology is proposed to improve the accuracy of Diesel Particulate Matter (DPM) concentrations simulated in a ventilation network model by using Computational Fluid Dynamics (CFD). DPM, as a primary concern for underground hard-rock mines, has been studied in two dominant numerical models, ventilation network solvers and computational fluid dynamics. Ventilation network solvers are efficient, but they are not able to provide three-dimensional DPM concentration contours and only accept area-average DPM data as input. CFD simulations are time-consuming, but generating DPM concentration results with high accuracy. Besides, both monitoring points and planes can be created in a CFD model, which can take both point data and area-average DPM data as input. The hybrid methodology is intended to combine the advantages of the two by using the CFD results to update the network model results at key locations. A field study was conducted in a dead-end heading in an underground gold mine in the United States. Real-time DPM monitors were used to collect data. A CFD model and a network model were built for the experiment area. The CFD model was calibrated to the experimental data. In the study presented here, results from a calibrated CFD model are converted to equivalent diesel emission rates in the ventilation network model at a shared outlet. The hybrid methodology is used to provide better input for the network model. INTRODUCTION Mine ventilation system is one of the most important parts for an underground mine. It not only provides fresh air to the personnel but also helps clear the contaminants generated from the diesel-powered equipment. For most underground metal and non-metal mines, diesel particulate matter is the main contaminant. It is also the focus of this paper."