Numerical Study of Diesel Particulate Matter Distribution in an Underground Mine Isolated Zone
The increased use of diesel engines in underground mines, together with increased mine depth, cause challenges in maintaining diesel particulate matter (DPM) at acceptable levels in underground environments. In 2012, the International Agency for Research on Cancer (IARC) classified DPM as carcinogenic to humans. To control the DPM exposure, it is important to understand DPM distribution and dispersion characteristics. In this study, an isolated zone in an underground mine in the US was taken as the physical model and the computational fluid dynamics (CFD) method was used to study the DPM distribution for two operational scenarios. The simulation results were compared with existing validation data. Compared to studies that treat DPM as a continuous phase, a better agreement with the experimental data is achieved in this study which uses the discrete phase to represent DPM. High DPM concentrations were identified in the two scenarios. This information can be potentially used to optimise auxiliary ventilation designs.
G. Xu et al., "Numerical Study of Diesel Particulate Matter Distribution in an Underground Mine Isolated Zone," Powder Technology, vol. 339, pp. 947 - 957, Elsevier, Nov 2018.
The definitive version is available at https://doi.org/10.1016/j.powtec.2018.08.075
Keywords and Phrases
Diesel engines; International cooperation; Ventilation; Auxiliary ventilation; Computational fluid dynamics methods; Diesel particulate matters; Dispersion characteristics; International agency for research on cancers; Operational scenario; Underground environment; Underground mines; Computational fluid dynamics; Article; Assisted ventilation; Computational fluid dynamics; Controlled study; Human; Particulate matter; Physical model; Simulation; Validation process
International Standard Serial Number (ISSN)
Article - Journal
© 2018 Elsevier, All rights reserved.
01 Nov 2018
This research project is supported by the Independent Research Projects of State Key Laboratory of Coal Resources and Safe Mining , CUMT (SKLCRSM15KF01); the Minerals Research Institute of Western Australia (M495); the Mining Education Australia Collaborative Research Grant Scheme (2018); and the computation resources provided by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia.