Projects per year
Abstract
Degradation of the iron ore during handling and transportation results predominantly from impact from drops, such as the ship loading process. The lump ore degradation is directly related to the particle energy dissipation during impacting with wall or particles. In this work, graphical processing units (GPU) and message passing interface (MPI)-based discrete element method (DEM) is developed for the large-scale iron ore ship loading process to analysis the particle impact and energy dissipation. The effect of particle properties such as size distribution and shape, belt speed and dropping height on energy dissipation are studied. The results illustrate that Young's modulus has little effect on the energy dissipation under the same loading condition. Degradation varies with particle size, with coarser particles suffering a greater energy dissipation than finer ones. Particles with a size distribution provide a significant cushion effect on particle degradation, as demonstrated by an obvious smaller value in energy dissipation. This is explained by the inter-particle contact during the dynamic loading process. Belt speed has negligible effect on impact energy dissipation within the range considered. Dropping height, as expected, is the most significant factors affecting the impact energy dissipation. When the dropping height reduces from 10 m to 5 m, the dissipated energy by particle-particle impacts reduces more than half. Vogel and Peukert [1] particle breakage model is used to study the individual particle breakage probability under specific material properties. For the same energy input, smaller particles have lower breakage probability, indicating that larger particles are easier to break than smaller ones.
Original language | English |
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Pages (from-to) | 476-484 |
Number of pages | 9 |
Journal | Powder Technology |
Volume | 354 |
DOIs | |
Publication status | Published - 1 Sept 2019 |
Keywords
- Degradation
- Energy dissipation
- GPU-DEM
- Particle breakage
- Ship loading
Projects
- 1 Finished
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ARC Research Hub for Computational Particle Technology
Yu, A., Zhao, D., Rudman, M., Jiang, X., Selomulya, C., Zou, R., Yan, W., Zhou, Z., Guo, B., Shen, Y., Kuang, S., Chu, K., Yang, R., Zhu, H., Zeng, Q., Dong, K., Strezov, V., Wang, G., Zhao, B., Song, S., Evans, T. J., Mao, X., Zhu, J., Hu, D., Pan, R., Li, J., Williams, S. R. O., Luding, S., Liu, Q., Zhang, J., Huang, H., Jiang, Y., Qiu, T., Hapgood, K. & Chen, W.
Australian Research Council (ARC), Jiangxi University of Science and Technology, Jiangsu Industrial Technology Research Institute, Fujian Longking Co Ltd, Baosteel Group Corporation, Hamersley Iron Pty Limited, Monash University, University of New South Wales (UNSW), University of Queensland , Western Sydney University (WSU), Macquarie University
31/12/16 → 30/12/21
Project: Research