Determination of the statistical distribution of drag and lift coefficients of refuse derived fuel by computer vision
Streier, Robin; Wirtz, Siegmar; Aleksandrov, Krasimir 1; Gehrmann, Hans-Joachim 1; Stapf, Dieter 1; Zhang, Miao 2; Vogelbacher, Markus
2; Matthes, Jörg 2; Scherer, Viktor
1 Institut für Technische Chemie (ITC), Karlsruher Institut für Technologie (KIT)
2 Institut für Automation und angewandte Informatik (IAI), Karlsruher Institut für Technologie (KIT)
2; Matthes, Jörg 2; Scherer, Viktor1 Institut für Technische Chemie (ITC), Karlsruher Institut für Technologie (KIT)
2 Institut für Automation und angewandte Informatik (IAI), Karlsruher Institut für Technologie (KIT)
Abstract (englisch):
This research investigates the flight behavior of refuse-derived fuel (RDF) in a drop shaft using Computer Vision
to obtain statistical data on the aerodynamic properties of the particles.
Methods to determine 3D geometry models of complex-shaped particles by photogrammetry and to obtain
time resolved particle positions and velocities are described. Furthermore, an approach to obtain the frequency
distribution of drag and lift coefficients from photogrammetric analysis and drop shaft experiments is presented.
The image evaluation is based on algorithms of the open-source libraries OpenCV, COLMAP as well as MeshLab
and Open3D. The precision of the system is validated employing model particles with known geometry. The 3D
particle models overestimate the particle surface area by 4.58 %, the position detection works with a mean
deviation of 2.73 %. The average sink rate is calculated with an accuracy of 4.87 % and the drag coefficient with
an accuracy of 2.08 %. Finally, the frequency distribution of four RDF fractions, namely, textiles, cardboard, 3D
plastic particles and 2D plastic foils are presented.
to obtain statistical data on the aerodynamic properties of the particles.
Methods to determine 3D geometry models of complex-shaped particles by photogrammetry and to obtain
time resolved particle positions and velocities are described. Furthermore, an approach to obtain the frequency
distribution of drag and lift coefficients from photogrammetric analysis and drop shaft experiments is presented.
The image evaluation is based on algorithms of the open-source libraries OpenCV, COLMAP as well as MeshLab
and Open3D. The precision of the system is validated employing model particles with known geometry. The 3D
particle models overestimate the particle surface area by 4.58 %, the position detection works with a mean
deviation of 2.73 %. The average sink rate is calculated with an accuracy of 4.87 % and the drag coefficient with
an accuracy of 2.08 %. Finally, the frequency distribution of four RDF fractions, namely, textiles, cardboard, 3D
plastic particles and 2D plastic foils are presented.
| Zugehörige Institution(en) am KIT | Institut für Automation und angewandte Informatik (IAI) Institut für Technische Chemie (ITC) |
| Publikationstyp | Zeitschriftenaufsatz |
| Publikationsdatum | 15.11.2023 |
| Sprache | Englisch |
| Identifikator | ISSN: 0016-2361, 1873-7153 KITopen-ID: 1000165129 |
| HGF-Programm | 38.05.01 (POF IV, LK 01) Anthropogenic Carbon Cycle |
| Weitere HGF-Programme | 37.12.01 (POF IV, LK 01) Digitalization & System Technology for Flexibility Solutions |
| Erschienen in | Fuel |
| Verlag | Elsevier |
| Band | 352 |
| Seiten | 128847 |
| Vorab online veröffentlicht am | 27.06.2023 |
| Schlagwörter | Drag and lift coefficients, Refuse derived fuel, Computer vision, Photogrammetry, Stereo vision |
| Nachgewiesen in | Dimensions |