DEM Simulation to Determine the Influence on the Experimental Results of Tests of Iron Pellets When the Dimensions of the Test Device Are Varied — Oak Academic Publishing
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DEM Simulation to Determine the Influence on the Experimental Results of Tests of Iron Pellets When the Dimensions of the Test Device Are Varied
The current study is based on the DEM computer simulation of three experimental test devices with different dimensions to determine the difference in the results of the formation of shear and repose angles that the particles experience when grouped under the action of the gravitational force. In this respect, the experimental test devices with different height, width, and depth were geometrically modeled with iron pellet particles using morphology and a granulometric variation from 6 mm to 9 mm of equivalent diameter in its spherical shape. Depending on the results obtained, a reliable size of the experimental test device will be available to obtain the necessary data for a correct adjustment of the calibration parameters for the DEM simulation of mining-metallurgical processes that use granulated material of iron pellet.
KeywordsDiscrete Elements MethodCalibrationTest DeviceDEM Parameters
Cundall, P.A. and Strack, O.D. (1979) A Discrete Numerical Model for Granular Assemblies. Geotechnique, 29, 47-65. https://doi.org/10.1680/geot.1979.29.1.47
Coetzee, C.J. and Els, D.N.J. (2009) Calibration of Granular Material Parameters for DEM Modelling and Numerical Verification by Blade-Granular Material Interaction. Journal of Terramechanics, 46, 15-26. https://doi.org/10.1016/j.jterra.2008.12.004
Gustafsson, G., Häggblad, H.Å., Jonsén, P. and Marklund, P. (2013) Determination of Bulk Properties and Fracture Data for Iron Ore Pellets Using Instrumented Confined Compression Experiments. Powder Technology, 241, 19-27. https://doi.org/10.1016/j.powtec.2013.02.030
Qi, J., Li, K.C., Jiang, H., Zhou, Q. and Yang, L. (2015) GPU-Accelerated DEM Implementation with CUDA. International Journal of Computational Science and Engineering, 11, 330-337. https://doi.org/10.1504/IJCSE.2015.072653
Peters, B. (2013) The Extended Discrete Element Method (XDEM) for Multi-Physics Applications. Scholarly Journal of Engineering Research, 2, 1-20.
Zhao, J. and Shan, T. (2013) Coupled CFD-DEM Simulation of Fluid-Particle Interaction in Geomechanics. Powder Technology, 239, 248-258. https://doi.org/10.1016/j.powtec.2013.02.003
Ng, A.H.M., Chang, H.C., Ge, L., Rizos, C. and Omura, M. (2009) Assessment of Radar Interferometry Performance for Ground Subsidence Monitoring Due to Underground Mining. Earth, Planets and Space, 61, 733-745. https://doi.org/10.1186/BF03353180
Xu, N., Zhang, J., Tian, H., Mei, G. and Ge, Q. (2016) Discrete Element Modeling of Strata and surface Movement Induced by Mining under Open-Pit Final Slope. International Journal of Rock Mechanics and Mining Sciences, 88, 61-76. https://doi.org/10.1016/j.ijrmms.2016.07.006
Jylhä, J. (2018) CFD-DEM Simulation of Two-Phase Flows in the Flash Smelting Settler.
Lei, X., Liao, Y. and Liao, Q. (2016) Simulation of Seed Motion in Seed Feeding Device with DEM-CFD Coupling Approach for Rapeseed and Wheat. Computers and Electronics in Agriculture, 131, 29-39. https://doi.org/10.1016/j.compag.2016.11.006
Suzzi, D., Toschkoff, G., Radl, S., Machold, D., Fraser, S.D., Glasser, B.J. and Khinast, J.G. (2012) DEM Simulation of Continuous Tablet Coating: Effects of Tablet Shape and Fill Level on Inter-Tablet Coating Variability. Chemical Engineering Science, 69, 107-121. https://doi.org/10.1016/j.ces.2011.10.009
Richter, C., Rößler, T., Kunze, G., Katterfeld, A. and Will, F. (2020) Development of a Standard Calibration Procedure for the DEM Parameters of Cohesionless Bulk Materials Part II: Efficient Optimization-Based Calibration. Powder Technology, 360, 967-976. https://doi.org/10.1016/j.powtec.2019.10.052
Gustafsson, G., Häggblad, H.Å. and Jonsén, P. (2013) Multi-Particle Finite Element Modelling of the Compression of Iron Ore Pellets with Statistically Distributed Geometric and Material Data. Powder Technology, 239, 231-238. https://doi.org/10.1016/j.powtec.2013.02.005
Rackl, M. and Hanley, K.J. (2017) A Methodical Calibration Procedure for Discrete Element Models. Powder Technology, 307, 73-83. https://doi.org/10.1016/j.powtec.2016.11.048
Roessler, T. and Katterfeld, A. (2016) Scalability of Angle of Repose Tests for the Calibration of DEM Parameters. 12th International Conference on Bulk Materials Storage, Handling and Transportation (ICBMH 2016), Darwin, 11-14 July 2016, 201.
Do, H.Q., Aragón, A.M. and Schott, D.L. (2018) A Calibration Framework for Discrete Element Model Parameters Using Genetic Algorithms. Advanced Powder Technology, 29, 1393-1403. https://doi.org/10.1016/j.apt.2018.03.001
Yoon, J. (2007) Application of Experimental Design and Optimization to PFC Model Calibration in Uniaxial Compression Simulation. International Journal of Rock Mechanics and Mining Sciences, 44, 871-889. https://doi.org/10.1016/j.ijrmms.2007.01.004
Benvenuti, L., Kloss, C. and Pirker, S. (2016) Identification of DEM Simulation Parameters by Artificial Neural Networks and Bulk Experiments. Powder Technology, 291, 456-465. https://doi.org/10.1016/j.powtec.2016.01.003
Luding, S. (2008) Introduction to Discrete Element Methods: Basic of Contact Force Models and How to Perform the Micro-Macro Transition to Continuum Theory. European Journal of Environmental and Civil Engineering, 12, 785-826. https://doi.org/10.1080/19648189.2008.9693050
Zhu, H.P., Zhou, Z.Y., Yang, R.Y. and Yu, A.B. (2007) Discrete Particle Simulation of Particulate Systems: Theoretical Developments. Chemical Engineering Science, 62, 3378-3396. https://doi.org/10.1016/j.ces.2006.12.089
González, C.A.L. (2012) Advances in the Development of the Discrete Element Method for Excavation Processes. Doctoral Dissertation, Universitat Politècnica de Catalunya (UPC).
Oñate, E., Labra, C., Zárate, F., Rojek, J. and Miquel, J. (2005) Avances en el desarrollo de los Métodos de Elementos Discretos y de Elementos Finitos para el análisis de problemas de fractura. Anales de Mecánica de la Fractura, 22, 27-34.
Taylor, L.M. and Preece, D.S. (1992) Simulation of Blasting Induced Rock Motion Using Spherical Element Models. Engineering Computations, 9, 243-252. https://doi.org/10.1108/eb023863
Katterfeld, A., Coetzee, C., Donohue, T., Fottner, A.J., Grima, A., Gomez, A.R., Schott, D., et al. (2019) Calibration of DEM Parameters for Cohesionless Bulk Materials under Rapid Flow Conditions and Low Consolidation.
Rackl, M., Grötsch, F.E., Rusch, M. and Fottner, J. (2017) Qualitative and Quantitative Assessment of 3D-Scanned Bulk Solid Heap Data. Powder Technology, 321, 105-118. https://doi.org/10.1016/j.powtec.2017.08.009
Barrios, G.K., de Carvalho, R.M., Kwade, A. and Tavares, L.M. (2013) Contact Parameter Estimation for DEM Simulation of Iron Ore Pellet Handling. Powder Technology, 248, 84-93. https://doi.org/10.1016/j.powtec.2013.01.063
Guya, S.R. (2018) Calibration of Discrete Element Modelling Parameters for Bulk Materials Handling Applications (Doctoral Dissertation).
Chen, G., Lodewijks, G. and Schott, D.L. (2018) Numerical Prediction on Abrasive Wear Reduction of Bulk Solids Handling Equipment Using Bionic Design. Particulate Science and Technology, 37, 964-973. https://doi.org/10.1080/02726351.2018.1480547
Schott, D., Vreeburg, W., Molhoek, C. and Lodewijks, G. (2016) Granular Flow to a Blast Iron Ore Furnace: Influence of Particle Size Distribution on Segregation of a Mixture. In: Traffic and Granular Flow’15, Springer, Cham, 621-628. https://doi.org/10.1007/978-3-319-33482-0_78
Coetzee, C.J. (2016) Calibration of the Discrete Element Method and the Effect of Particle Shape. Powder Technology, 297, 50-70. https://doi.org/10.1016/j.powtec.2016.04.003