Document Type: Original Research Paper

Authors

1 Mining Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran

2 Kashigar Mineral Processing Research Centre, Shahid Bahonar University of Kerman, Kerman, Iran

10.22044/jme.2020.10208.1958

Abstract

The discrete element method (DEM) has been used as a popular simulation method in order to verify the designs by visualizing how materials flow through complex equipment geometries.  Although DEM simulation is a powerful design tool, finding a DEM model that includes all real material properties is not computationally feasible.  In order to obtain more realistic results, particle energy loss due to rolling friction has been highlighted by many researchers using various models to implement a reverse torque.  On account of the complexity of the problem, there is no unique model for all applications (i.e. dynamic and pseudo-static regimes).  In this research work, an in-house developed DEM software (KMPCDEM©) was used to assess the robustness of three models by comparing the repose angle obtained through the draw down test. The elastic–plastic spring dashpot model was then modified based on considering the individual parameters instead of the relative parameters of two contact entities.  The results showed that the modified model could produce a higher repose angle.  The modified model was used for the calibration of DEM input parameters in the simulation of repose angle of iron ore pellets in a laboratory setup of the draw down test.  Comparison of the calibrated DEM simulation (using 0.0007 and 0.75 for the rolling and sliding friction coefficients, respectively) with the laboratory results showed a good agreement between the predicted and measured angle of repose.  The non-calibrated DEM simulations are susceptible to error, and therefore, it is strongly recommended to use the laboratory experiments to characterize the materials before using the DEM simulation as a design tool of industrial equipment.

Keywords

[1]. Cundall, P.A. and Strack, D.L. (1979). A discrete numerical model for granular assemblies. Geotechnique. 29: 47-65.

[2]. Iwashita, K. and Oda, M. (2000). Micro-deformation mechanism of shear banding process based on modified distinct element method. Powder Technology. 109: 192-205.

[3]. Scharpf, D. (2008). DEM Applications: Simulation of Particulate Solids Handling and Processing Operations using the Discrete Element Method. Vision of Engineering Analysis and Simulation: NAFEMS Company, Developer of EDEM Software. 9-30.

[4]. Coetzee, C.J. and Els, D.N.J. (2009). Calibration of discrete element parameters and the modelling of silo discharge and bucket filling. Computers and Electronics in Agricultures. 65: 198-212.

[5]. Kondic, L. (1999). Dynamics of spherical particles on a surface: Collision-induced sliding and other effects. Physical review E. 60: 751-770.

[6]. Grima, A., Hastie1, D., Curry, D., Wypych, P. and LaRoche, R. (2011). The beginning of a new era in design: Calibrated discrete element modelling. Australian Bulk Handling Review. 14-21.

[7]. Hasankhoei, A.R., Maleki-Moghaddam, M., Haji-Zadeh, A., Barzgar, M.E. and Banisi, S. (2019). On dry SAG mills end liners: Physical modeling, DEM-based characterization and industrial outcomes of a new design. Minerals Engineering. 141: 105835.

[8]. Ghasemi, A.R., Hasankhoei, A.R., Parsapour, Gh.A., Razi, E. and Banisi, S. (2020). A combined physical and DEM modelling approach to improve performance of rotary dryers by modifying flights design. Drying Technology. 1-18.

[9]. Wensrich, C.M. and Katterfeld, A. (2012). Rolling friction as a technique for modelling particle shape in DEM. Powder Technology. 217: 409-417.

[10]. Podlozhnyuk, A., Pirker, S. and Kloss, C. (2017). Efficient implementation of super quadric particles in Discrete Element Method within an open-source framework, Computational Particle Mechanics. 4: 101-118.

[11]. Ghasemi, A.R., Razi, E. and Banisi, S. (2020). Determining a lower boundary of elasticity modulus used in the discrete element method (DEM) in simulation of tumbling mills. Advanced Powder Technology. 31: 1365-1371.

[12]. Iwashita, K. and Oda, M. (1998). Rolling resistance at contacts in simulation of shear band development by DEM. Journal of Engineering Mechanics. 124 (3): 285-292.

[13]. Zhou, Y.C., Wright, B.D., Yang, R.Y., Xu, B.H. and Yu, A.B. (1999). Rolling friction in the dynamic simulation of sandpile formation. Physica A. 269: 536-553. 

[14]. Bagi, K. and Kuhn, M.R. (2004). A definition of particle rolling in a granular assembly in terms of particle translations and rotations. Journal of Applied Mechanics. 71: 493-501.

[15]. Sakaguchi, H., Ozaki, E. and Igarashi, T. (1993). Plugging of the flow of Granular materials during the discharge from a silo. International Journal of Modern Physics B. 7: 1949-1963.

[16]. Ai, J., Chen, J-F., Rotter, J. M. and Ooi, J.Y. (2011). Assessment of rolling resistance models in discrete element simulations. Powder Technology. 206: 269-282.

[17]. Wensrich, C.M., Katterfeld, A. and Sugo, D. (2014). Characterization of the effects of particle shape using a normalized contact eccentricity. Granular Matter. 16: 327-337. 

[18]. Zhao, C., Li, C. and Hu, L. (2018). Rolling and sliding between non-spherical particles. Physica A. 492: 181-191.

[19]. Bardet, J.P. and Huang, Q. (1992), Numerical modeling of micropolar effects in idealized granular materials. Mechanics of Granular Materials and Powder Systems. 37: 85-92. 

[20]. Baxter, J., Tuzun, U., Burnell, J. and Heyes, D.M. (1997). Granular dynamics simulations of two-dimensional heap formation. Physical review E. 55: 3546-3554.

[21]. Estrada, N., Azéma, E., Radjai, F. and Taboada, A. (2011). Identification of rolling resistance as a shape parameter in sheared granular media. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, American Physical Society. 84: 011306. 

[22] Fukumoto, Y., Sakaguchi, H. and Murakami, A. (2013). The role of rolling friction in granular packing. Granular Matter. 15 (2): 175–182. 

[23] Horabik, J., Parafiniuk, P. and Molenda M. (2017). Discrete element modelling study of force distribution in a 3D pile of spherical particles. Powder Technology. 312: 194-203.

[24]. Zhou, C., Xu, B.H., ZOU, R.P., Yu, A.B. and Zulli, P. (2001). Stress distribution in a sandpile formed on a defected base. Advanced Powder Technology. 14: 401-410.

[25]. Zhou, C., Xu, B.H., Yu, A.B. and Zulli, P. (2002). An experimental and numerical study of the angle of repose of coarse spheres. Powder Technology. 125: 45-54.

[26]. Jiang, M.J., Yu, H.-S. and Harris, D. (2005). A novel discrete model for granular material incorporating rolling resistance. Computers and Geotechnics. 32: 340-357.

[27]. Li, C., Honeyands, T., O'Dea, D. and Moreno-Atanasio, R. (2017). The angle of repose and size segregation of iron ore granules: DEM analysis and experimental investigation. Powder Technology. 320: 257-272.

[28]. Oda, M., Konishi, J. and Nemat-nasser, S. (1982). Experimental micromechanical evaluation of strength of granular materials: Effects of particle rolling. Mechanics of Materials. 1: 269-283.

[29]. Oda, M. and Kazama, H. (1998). Microstructure of shear bands and its relation to the mechanisms of dilatancy and failure of dense granular soils. Geotechnique. 48 (4): 465-481.

[30]. Barrios, G.k.P., 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.

[31]. Frankowski, P. and Morgeneyer, M. (2013). Calibration and validation of DEM rolling and sliding friction coefficients in angle of repose and shear measurements, Proc, 7th International Conference on Micromechanics of Granular Media (AIP 1542). pp. 851-854.

[32]. Santos, K.G., Campos, A.V.P., Oliveira, O.S., Ferreira, L.V., Francisquetti, M. C. and Barrozo, M.A.S. (2014). DEM simulations of dynamic angle of repose of acerola residue: A parametric study using a response surface technique, Proc, XX Brazilian Congress of Chemical Engineering, Brazil.

[33]. Boemer, D. and Ponthot, G.P. (2016). DEM modeling of ball mills with experimental validation: influence of contact parameters on charge motion and power draw. Computational Particle Mechanics. 4. https://doi.org/10.1007/s40571-016-0125-4.

[34]. Cheng, N.S. and Zhao, K. (2016). Difference between static and dynamic angle of repose of uniform sediment grains. International Journal of Sediment Research. https://doi.org/10.1016/j.ijsrc.2016.09.001.

[35]. Bablena, A. and Hungary, G. (2017). DEM Calibration: a complex optimization problem, International Conference on Control, Artificial Intelligence, Robotics & Optimization, Athens, Greece. pp. 198-201. 

[36]. Beakawi, H.M., Al-Hashemi, Baghabra, O.S. and Al-Amoudi, (2018). A review on the angle of repose of granular materials. Powder Technology. 330: 397-417. 

[37]. Alizadeh, M., Asachi, M., Ghadiri, M., Bayly, A. and Hassanpour, A. (2018). A methodology for calibration of DEM input parameters in simulation of segregation of powder mixtures, a special focus on adhesion. Powder Technology. 339: 789-800. 

[38]. Roessler, T. and Katterfeld, A. (2018). Scaling of the angle of repose test and its influence on the calibration of DEM parameters using upscaled particles. Powder Technology. 330: 58-66.

[39]. Jin, F., Xin, H., Change, C. and Sun, Q. (2011). Probability-based contact algorithm for non-spherical particles in DEM. Powder Technology. 212: 134-144.  

[40]. 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.

[41]. Roessler, T., Richter, C., Katterfeld, A. and Will, F. (2019). Development of a standard calibration procedure for the DEM parameters of cohesionless bulk materials – part I: Solving the problem of ambiguous parameter combinations. Powder Technology. 343: 803-812.