Hybrid CFD-network methodology for improved DPM modelling


Hongbin Zhang, Laurentian University

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 accurate DPM concentration results. CFD simulations are time-consuming, but generating DPM concentration results with higher accuracy. 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 several headings 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 power input in the ventilation network model at a shared outlet. The updated network model is expected to be more accurate than the original ventilation network model. The benefits of the hybrid methodology will be more noticeable when DPM data from multiple outlets are updated in the network model.