A framework to develop a hybrid methodology for modeling of diesel particulate matter concentration in underground mine ventilation systems

Abstract

The purpose of this research is to develop a hybrid methodology for diesel particulate matter (DPM) modeling for underground mine ventilation systems. The hybrid methodology, which is an enhanced and complementary tool, is proposed to provide improved diesel input to a ventilation network solver by using a computational fluid dynamics (CFD) solver. The hybrid methodology uses the DPM results from a calibrated CFD model to update those from a network model at shared locations. A CFD approach for simulating DPM over an extended period has been proposed to make the hybrid methodology more adaptable to large ventilation systems. With the proposed CFD modeling approach, contaminants like DPM can be modeled accurately in a timely manner over an extended period of time. The results from this approach are then used to update the network model results at the shared outlets. The workflow to update DPM results from the network model using the calibrated CFD model is presented. Two field studies were conducted in an underground mine in the western United States. Due to the low quality airflow and DPM data collected from the field, this study should be viewed as a qualitative study rather than a quantitative study. Corresponding CFD models, ventilation network models, and updated ventilation models were also established, and results from these models were compared with the experimental data. The limitations for both the instruments and the methodology were defined as well.