Design of structured meshes of mining excavations based on variability trends of real point clouds from laser scanning for numerical airflow modeling

Abstract

Various technologies are used to acquire and process 3D data from mining excavations, such as Terrestrial Laser Scanning (TLS), photogrammetry, or Mobile Mapping Systems (MMS) supported by Simultaneous Localization and Mapping (SLAM) algorithms. Due to the often difficult measurement conditions, the data obtained are often incomplete or inaccurate. There are gaps in the point cloud due to objects obscuring the tunnel. Data processing itself is also time-consuming. Point clouds must be cleaned of unnecessary noise and elements. On the other hand, accurate modeling of airflows is an ongoing challenge for the scientific community. Considering the utilization of 3D data for the numerical analysis of airflow in mining excavations using Computational Fluid Dynamics (CFD) tools, this poses a considerable problem, especially the creation of a surface mesh model, which could be further utilized for this application. This paper proposes a method to create a synthetic model based on real data. 3D data from underground mining tunnels captured by a LiDAR sensor are processed employing feature extraction. A uniformly sampled tunnel of given dimensions, point cloud resolution, and cross-sectional shape is created for which obtained features are applied, e.g. general trajectory of the tunnel, shapes of walls, and additional valuable noise for obtaining surfaces of desired roughness. This allows to adjust parameters such as resolution, dimensions, or strengths of features to obtain the best possible representation of a real underground mining excavation geometry. From a perspective of Computational Fluid Dynamics (CFD) simulations of airflow, this approach has the potential to shorten geometry preparation, increase the quality of computational meshes, reduce discretization time, and increase the accuracy of the results obtained, which is of particular importance considering airflow modeling of extensive underground ventilation networks.