Development and evaluation of an immersive VR-CFD-Based tool for dust exposure mitigation in underground tunnelling operations

Publication Name

Tunnelling and Underground Space Technology

Abstract

Long-term exposure to respirable coal and silica dust during underground tunnelling operations has gained increasing attention in recent years. The solution to effective mitigation of dust exposure depends not only on higher-order engineering controls, but also on administrative controls for frontline workers. However, there is a disconnect between knowledge gathered in the field of dust exposure monitoring and the frontline worker, resulting in important learnings being overlooked in underground tunnelling operations. To remedy this discrepancy, an immersive educational tool was developed to visualise computational fluid dynamics (CFD) modelling datasets of ventilation and respirable dust flow characteristics in the tunnel face in a virtual reality (VR) environment. An algorithm was developed for processing the large and complex CFD datasets into a form that can be processed and visualised using standalone VR headsets with limited processing power. The VR-CFD system was assessed by an industry expert and via many industry showcases, where regular feedback was received for making significant improvements in this education tool. This tool has been developed as a training platform to allow frontline workers to better understand the results of decisions made during tunnelling operations and the best practices for dust controls. A number of key technological achievements were made that can be used in the future to quickly translate real-world particulate readings collected from underground space into a VR visualiser. This VR-CFD digital technology can readily be extended to mining, construction and other tunnelling operations in the underground space, thus improving health and safety.

Open Access Status

This publication may be available as open access

Volume

143

Article Number

105496

Share

COinS
 

Link to publisher version (DOI)

http://dx.doi.org/10.1016/j.tust.2023.105496