Skip to content
Licensed Unlicensed Requires Authentication Published online by De Gruyter March 28, 2022

A comprehensive review of the application of DEM in the investigation of batch solid mixers

  • Behrooz Jadidi , Mohammadreza Ebrahimi , Farhad Ein-Mozaffari ORCID logo EMAIL logo and Ali Lohi

Abstract

Powder mixing is a vital operation in a wide range of industries, such as food, pharmaceutical, and cosmetics. Despite the common use of mixing systems in various industries, often due to the complex nature of mixing systems, the effects of operating and design parameters on the mixers’ performance and final blend are not fully known, and therefore optimal parameters are selected through experience or trial and error. Experimental and numerical techniques have been widely used to analyze mixing systems and to gain a detailed understanding of mixing processes. The limitations associated with experimental techniques, however, have made discrete element method (DEM) a valuable complementary tool to obtain comprehensive particle level information about mixing systems. In the present study, the fundamentals of solid-solid mixing, segregation, and characteristics of different types of batch solid mixers are briefly reviewed. Previously published papers related to the application of DEM in studying mixing quality and assessing the influence of operating and design parameters on the mixing performance of various batch mixing systems are summarized in detail. The challenges with regards to the DEM simulation of mixing systems, the available solutions to address those challenges and our recommendations for future simulations of solid mixing are also presented and discussed.


Corresponding author: Farhad Ein-Mozaffari, Department of Chemical Engineering, Ryerson University, 350 Victoria Street, Toronto M5B 2K3, Canada, E-mail:

Funding source: Natural Sciences and Engineering Research Council of Canada (NSERC)

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: The financial support of the Natural Sciences and Engineering Research Council of Canada (NSERC) is gratefully acknowledged.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

Abbaspour-Fard, M.H. (2000). Discrete element modeling of the dynamic behaviour of non-spherical particulate materials, Ph.D. thesis. Newcastle, Newcastle University.Search in Google Scholar

Ai, J., Chen, J.F., Rotter, J.M., and Ooi, J.Y. (2011). Assessment of rolling resistance models in discrete element simulations. Powder Technol. 206: 269–282, https://doi.org/10.1016/j.powtec.2010.09.030.Search in Google Scholar

Alchikh-Sulaiman, B., Ein-Mozaffari, F., and Lohi, A. (2015). Evaluation of poly-disperse solid particles mixing in a slant cone mixer using discrete element method. Chem. Eng. Res. Des. 96: 196–213, https://doi.org/10.1016/j.cherd.2015.02.020.Search in Google Scholar

Alchikh-Sulaiman, B., Alian, M., Ein-Mozaffari, F., Lohi, A., and Upreti, S.R. (2016). Using the discrete element method to assess the mixing of polydisperse solid particles in a rotary drum. Particuology 25: 133–142, https://doi.org/10.1016/j.partic.2015.05.006.Search in Google Scholar

Alexander, A., Shinbrot, T., Johnson, B., and Muzzio, F.J. (2004a). V-blender segregation patterns for free-flowing materials: effects of blender capacity and fill level. Int. J. Pharm. 269: 19–28, https://doi.org/10.1016/s0378-5173(03)00296-5.Search in Google Scholar

Alexander, A., Sudah, O., Arratia, P., Duong, N.-H., Rtynolds, S., and Muzzio, F. (2004b). Characterization of the performance of bin blenders: part 3 of 3: cohesive powders. Pharmaceut. Technol. 28: 54–65.Search in Google Scholar

Alian, M., Ein-Mozaffari, F., and Upreti, S.R. (2015a). Analysis of the mixing of solid particles in a plowshare mixer via discrete element method (DEM). Powder Technol. 274: 77–87, https://doi.org/10.1016/j.powtec.2015.01.012.Search in Google Scholar

Alian, M., Ein-Mozaffari, F., Upreti, S.R., and Wu, J. (2015b). Using discrete element method to analyze the mixing of the solid particles in a slant cone mixer. Chem. Eng. Res. Des. 93: 318–329, https://doi.org/10.1016/j.cherd.2014.07.003.Search in Google Scholar

Alizadeh, E. (2013). Numerical and experimental investigation of solid mixing and segregation in tumbling blenders, Ph.D. thesis. Montreal, University of Montreal.Search in Google Scholar

Alizadeh, E., Dubé, O., Bertrand, F., and Chaouki, J. (2013a). Characterization of mixing and size segregation in a rotating drum by a particle tracking method. AIChE J. 59: 1894–1905, https://doi.org/10.1002/aic.13982.Search in Google Scholar

Alizadeh, E., Hajhashemi, H., Bertrand, F., and Chaouki, J. (2013b). Experimental investigation of solid mixing and segregation in a tetrapodal blender. Chem. Eng. Sci. 97: 354–365, https://doi.org/10.1016/j.ces.2013.04.035.Search in Google Scholar

Alizadeh, E., Bertrand, F., and Chaouki, J. (2014a). Discrete element simulation of particle mixing and segregation in a tetrapodal blender. Comput. Chem. Eng. 64: 1–12, https://doi.org/10.1016/j.compchemeng.2013.12.009.Search in Google Scholar

Alizadeh, E., Bertrand, F., and Chaouki, J. (2014b). Comparison of DEM results and Lagrangian experimental data for the flow and mixing of granules in a rotating drum. AIChE J. 60: 60–75, https://doi.org/10.1002/aic.14259.Search in Google Scholar

Amritkar, A., Deb, S., and Tafti, D. (2014). Efficient parallel CFD-DEM simulations using OpenMP. J. Comput. Phys. 256: 501–519, https://doi.org/10.1016/j.jcp.2013.09.007.Search in Google Scholar

Arratia, P., Duong, N.H., Muzzio, F.J., Godbole, P., Lange, A., and Reynolds, S. (2006a). Characterizing mixing and lubrication in the Bohle bin blender. Powder Technol. 161: 202–208, https://doi.org/10.1016/j.powtec.2005.10.009.Search in Google Scholar

Arratia, P., Duong, N., Muzzio, F.J.J., Godbole, P., and Reynolds, S. (2006b). A study of the mixing and segregation mechanisms in the Bohle Tote blender via DEM simulations. Powder Technol. 164: 50–57, https://doi.org/10.1016/j.powtec.2006.01.018.Search in Google Scholar

Asachi, M., Nourafkan, E., and Hassanpour, A. (2018). A review of current techniques for the evaluation of powder mixing. Adv. Powder Technol. 29: 1525–1549, https://doi.org/10.1016/j.apt.2018.03.031.Search in Google Scholar

Barthel, E. (2008). Adhesive elastic contacts: JKR and more. J. Phys. Appl. Phys. 41: 163001, https://doi.org/10.1088/0022-3727/41/16/163001.Search in Google Scholar

Basinskas, G. and Sakai, M. (2016a). Numerical study of the mixing efficiency of a ribbon mixer using the discrete element method. Powder Technol. 287: 380–394, https://doi.org/10.1016/j.powtec.2015.10.017.Search in Google Scholar

Basinskas, G. and Sakai, M. (2016b). Numerical study of the mixing efficiency of a batch mixer using the discrete element method. Powder Technol. 301: 815–829, https://doi.org/10.1016/j.powtec.2016.07.017.Search in Google Scholar

Bednarek, X., Martin, S., Ndiaye, A., Peres, V., and Bonnefoy, O. (2019). Extrapolation of DEM simulations to large time scale application to the mixing of powder in a conical screw mixer. Chem. Eng. Sci. 197: 223–234, https://doi.org/10.1016/j.ces.2018.12.022.Search in Google Scholar

Benvenuti, L., Kloss, C., and Pirker, S. (2016). Identification of DEM simulation parameters by artificial neural networks and bulk experiments. Powder Technol. 291: 456–465, https://doi.org/10.1016/j.powtec.2016.01.003.Search in Google Scholar

Berger, R., Kloss, C., Kohlmeyer, A., and Pirker, S. (2015). Hybrid parallelization of the LIGGGHTS open-source DEM code. Powder Technol. 278: 234–247, https://doi.org/10.1016/j.powtec.2015.03.019.Search in Google Scholar

Bhalode, P. and Ierapetritou, M. (2020). A review of existing mixing indices in solid-based continuous blending operations. Powder Technol. 373: 195–209, https://doi.org/10.1016/j.powtec.2020.06.043.Search in Google Scholar

Blais, B., Vidal, D., Bertrand, F., Patience, G.S., and Chaouki, J. (2019). Experimental methods in chemical engineering: discrete element method—DEM. Can. J. Chem. Eng. 97: 1964–1973, https://doi.org/10.1002/cjce.23501.Search in Google Scholar

Boonkanokwong, V., Remy, B., Khinast, J.G.G., and Glasser, B.J.J. (2016). The effect of the number of impeller blades on granular flow in a bladed mixer. Powder Technol. 302: 333–349, https://doi.org/10.1016/j.powtec.2016.08.064.Search in Google Scholar

Boonkanokwong, V., Frank, R.P., Valliappan, P., Remy, B., Khinast, J.G., and Glasser, B.J. (2018). Flow of granular materials in a bladed mixer: effect of particle properties and process parameters on impeller torque and power consumption. Adv. Powder Technol. 29: 2733–2752, https://doi.org/10.1016/j.apt.2018.07.022.Search in Google Scholar

Bridgwater, J. (1976). Fundamental powder mixing mechanisms. Powder Technol. 15: 215–236, https://doi.org/10.1016/0032-5910(76)80051-4.Search in Google Scholar

Bridgwater, J. (2003). The dynamics of granular materials – towards grasping the fundamentals. Granul. Matter 4: 175–181, https://doi.org/10.1007/s10035-002-0120-8.Search in Google Scholar

Brilliantov, N.V. and Pöschel, T. (1998). Rolling friction of a viscous sphere on a hard plane. Europhys. Lett. 42: 511, https://doi.org/10.1209/epl/i1998-00281-7.Search in Google Scholar

Brone, D. and Muzzio, F.J. (2000). Enhanced mixing in double-cone blenders. Powder Technol. 110: 179–189, https://doi.org/10.1016/s0032-5910(99)00204-1.Search in Google Scholar

Brone, D., Wightman, C., Connor, K., Alexander, A., Muzzio, F.J., and Robinson, P. (1997). Using flow perturbations to enhance mixing of dry powders in V-blenders. Powder Technol. 91: 165–172, https://doi.org/10.1016/s0032-5910(96)03231-7.Search in Google Scholar

Lillie, C, and Wriggers, P (2006). Three-dimensional modelling of discrete particles by superellipsoids. Proc. Appl. Math. Mech. 6: 101–102, https://doi.org/10.1002/pamm.200610031.Search in Google Scholar

Cai, R., Hou, Z., and Zhao, Y. (2019). Numerical study on particle mixing in a double-screw conical mixer. Powder Technol. 352: 193–208, https://doi.org/10.1016/j.powtec.2019.04.065.Search in Google Scholar

Campbell, C.S. (2002). Granular shear flows at the elastic limit. J. Fluid Mech. 465: 261–291, https://doi.org/10.1017/s002211200200109x.Search in Google Scholar

Chandratilleke, G.R., Yu, A.B., Stewart, R.L., and Bridgwater, J. (2009). Effects of blade rake angle and gap on particle mixing in a cylindrical mixer. Powder Technol. 193: 303–311, https://doi.org/10.1016/j.powtec.2009.03.007.Search in Google Scholar

Chandratilleke, G.R., Zhou, Y.C., Yu, A.B., and Bridgwater, J. (2010). Effect of blade speed on granular flow and mixing in a cylindrical mixer. Ind. Eng. Chem. Res. 49: 5467–5478, https://doi.org/10.1021/ie901581t.Search in Google Scholar

Chandratilleke, G.R., Yu, A.B., Bridgwater, J., and Shinohara, K. (2012). A particle-scale index in the quantification of mixing of particles. AIChE J. 58: 1099–1118, https://doi.org/10.1002/aic.12654.Search in Google Scholar

Chandratilleke, R., Yu, A., Bridgwater, J., and Shinohara, K. (2014). Flow and mixing of cohesive particles in a vertical bladed mixer. Ind. Eng. Chem. Res. 53: 4119–4130, https://doi.org/10.1021/ie403877v.Search in Google Scholar

Chandratilleke, G.R., Dong, K.J., and Shen, Y.S. (2018). DEM study of the effect of blade-support spokes on mixing performance in a ribbon mixer. Powder Technol. 326: 123–136, https://doi.org/10.1016/j.powtec.2017.12.055.Search in Google Scholar

Chang, R.K., Chang, S.I., and Robinson, J.R. (1992). A study of the performance of a modified V-shaped solids mixer using segregating materials. Int. J. Pharm. 80: 171–178, https://doi.org/10.1016/0378-5173(92)90275-7.Search in Google Scholar

Chaudhuri, B., Mehrotra, A., Muzzio, F.J., and Tomassone, M.S. (2006). Cohesive effects in powder mixing in a tumbling blender. Powder Technol. 165: 105–114, https://doi.org/10.1016/j.powtec.2006.04.001.Search in Google Scholar

Chen, C.C. and Yu, C.K. (2004). Two-dimensional image characterization of powder mixing and its effects on the solid-state reactions. Mater. Chem. Phys. 85: 227–237, https://doi.org/10.1016/j.matchemphys.2004.01.024.Search in Google Scholar

Chen, M., Liu, M., Li, T., Tang, Y., Liu, R., Wen, Y., Liu, B., and Shao, Y. (2018). A novel mixing index and its application in particle mixing behavior study in multiple-spouted bed. Powder Technol. 339: 167–181, https://doi.org/10.1016/j.powtec.2018.08.036.Search in Google Scholar

Cho, M., Dutta, P., and Shim, J. (2017). A non-sampling mixing index for multicomponent mixtures. Powder Technol. 319: 434–444, https://doi.org/10.1016/j.powtec.2017.07.011.Search in Google Scholar

Coetzee, C.J. (2017). Review: calibration of the discrete element method. Powder Technol. 310: 104–142, https://doi.org/10.1016/j.powtec.2017.01.015.Search in Google Scholar

Cooke, M.H., Stephens, D.J., and Bridgwater, J. (1976). Powder mixing – a literature survey. Powder Technol. 15: 1–20, https://doi.org/10.1016/0032-5910(76)80025-3.Search in Google Scholar

Cullen, P.J., Romañach, R.J., Abatzoglou, N., and Rielly, C.D. (2015). Pharmaceutical blending and mixing. John Wiley & Sons, Chichester.10.1002/9781118682692Search in Google Scholar

Cundall, P.A. and Hart, R.D. (1992). Numerical modelling of discontinua. Eng. Comput. 9: 101–113, https://doi.org/10.1108/eb023851.Search in Google Scholar

Cundall, P.A. and Strack, O.D.L. (1979). A discrete numerical model for granular assemblies. Geotechnique 29: 47–65, https://doi.org/10.1680/geot.1979.29.1.47.Search in Google Scholar

Dai, B.B., Yang, J., Liu, F.T., Gu, X.Q., and Lin, K.R. (2020). A new index to characterize the segregation of binary mixture. Powder Technol. 363: 611–620, https://doi.org/10.1016/j.powtec.2020.01.005.Search in Google Scholar

Danckwerts, P.V. (1953). Continuous flow systems. Distribution of residence times. Chem. Eng. Sci. 2: 1–13, https://doi.org/10.1016/0009-2509(53)80001-1.Search in Google Scholar

Deen, N.G., Willem, G., Sander, G., and Kuipers, J.A.M. (2010). Numerical analysis of solids mixing in pressurized fluidized beds. Ind. Eng. Chem. Res. 49: 5246–5253, https://doi.org/10.1021/ie9014843.Search in Google Scholar

Ding, Y.L., Forster, R.N., Seville, J.P.K., and Parker, D.J. (2001). Scaling relationships for rotating drums. Chem. Eng. Sci. 56: 3737–3750, https://doi.org/10.1016/s0009-2509(01)00092-6.Search in Google Scholar

Di Renzo, A. and Di Maio, F.P. (2004). Comparison of contact-force models for the simulation of collisions in DEM-based granular flow codes. Chem. Eng. Sci. 59: 525–541, https://doi.org/10.1016/j.ces.2003.09.037.Search in Google Scholar

Do, H.Q., Aragón, A.M., and Schott, D.L. (2018). A calibration framework for discrete element model parameters using genetic algorithms. Adv. Powder Technol. 29: 1393–1403, https://doi.org/10.1016/j.apt.2018.03.001.Search in Google Scholar

Doucet, J., Hudon, N., Bertrand, F., and Chaouki, J. (2008). Modeling of the mixing of monodisperse particles using a stationary DEM-based Markov process. Comput. Chem. Eng. 32: 1334–1341, https://doi.org/10.1016/j.compchemeng.2007.06.017.Search in Google Scholar

Duong, N.H., Arratia, P., Muzzio, F., Lange, A., Timmermans, J., and Reynolds, S. (2003). A homogeneity study using NIR spectroscopy: tracking magnesium stearate in bohle bin-blender. Drug Dev. Ind. Pharm. 29: 679–687, https://doi.org/10.1081/ddc-120021317.Search in Google Scholar

Ebrahimi, M., Siegmann, E., Prieling, D., Glasser, B.J., and Khinast, J.G. (2017). An investigation of the hydrodynamic similarity of single-spout fluidized beds using CFD-DEM simulations. Adv. Powder Technol. 28: 2465–2481, https://doi.org/10.1016/j.apt.2017.05.009.Search in Google Scholar

Ebrahimi, M., Yaraghi, A., Ein-Mozaffari, F., and Lohi, A. (2018). The effect of impeller configurations on particle mixing in an agitated paddle mixer. Powder Technol. 332: 158–170, https://doi.org/10.1016/j.powtec.2018.03.061.Search in Google Scholar

Ebrahimi, M., Yaraghi, A., Jadidi, B., Ein-Mozaffari, F., and Lohi, A. (2020). Assessment of bi-disperse solid particles mixing in a horizontal paddle mixer through experiments and DEM. Powder Technol. 381: 129–140.10.1016/j.powtec.2020.11.041Search in Google Scholar

Elperin, T. and Golshtein, E. (1997). Comparison of different models for tangential forces using the particle dynamics method. Phys. Stat. Mech. Appl. 242: 332–340, https://doi.org/10.1016/s0378-4371(97)00218-5.Search in Google Scholar

Fan, L.T., Chen, S.J., and Watson, C.A. (1970). Annual review solids mixing. Ind. Eng. Chem. 62: 53–69, https://doi.org/10.1021/ie50727a009.Search in Google Scholar

Favier, J.F., Abbaspour-Fard, M.H., Kremmer, M., and Raji, A.O. (1999). Shape representation of axi-symmetrical, non-spherical particles in discrete element simulation using multi-element model particles. Eng. Comput. 16: 467–480, https://doi.org/10.1108/02644409910271894.Search in Google Scholar

Gan, J.Q., Zhou, Z.Y., and Yu, A.B. (2016). A GPU-based DEM approach for modelling of particulate systems. Powder Technol. 301: 1172–1182, https://doi.org/10.1016/j.powtec.2016.07.072.Search in Google Scholar

Gao, W., Liu, L., Liao, Z., Chen, S., Zang, M., and Tan, Y. (2019). Discrete element analysis of the particle mixing performance in a ribbon mixer with a double U-shaped vessel. Granul. Matter 21: 1–16, https://doi.org/10.1007/s10035-018-0864-4.Search in Google Scholar

Gao, Y., Muzzio, F.J., and Ierapetritou, M.G. (2012). Optimizing continuous powder mixing processes using periodic section modeling. Chem. Eng. Sci. 80: 70–80, https://doi.org/10.1016/j.ces.2012.05.037.Search in Google Scholar

Golshan, S., Zarghami, R., Norouzi, H.R., and Mostoufi, N. (2017). Granular mixing in nauta blenders. Powder Technol. 305: 279–288, https://doi.org/10.1016/j.powtec.2016.09.059.Search in Google Scholar

Govender, N., Wilke, D.N., Wu, C.Y., Rajamani, R., Khinast, J., and Glasser, B.J. (2018). Large-scale GPU based DEM modeling of mixing using irregularly shaped particles. Adv. Powder Technol. 29: 2476–2490, https://doi.org/10.1016/j.apt.2018.06.028.Search in Google Scholar

Govender, N., Cleary, P.W., Kiani-Oshtorjani, M., Wilke, D.N., Wu, C.-Y., and Kureck, H. (2020). The effect of particle shape on the packed bed effective thermal conductivity based on DEM with polyhedral particles on the GPU. Chem. Eng. Sci. 219: 115584, https://doi.org/10.1016/j.ces.2020.115584.Search in Google Scholar

Halidan, M., Chandratilleke, G.R., Chan, S.L.I., Yu, A.B., and Bridgwater, J. (2014). Prediction of the mixing behaviour of binary mixtures of particles in a bladed mixer. Chem. Eng. Sci. 120: 37–48, https://doi.org/10.1016/j.ces.2014.08.048.Search in Google Scholar

Halidan, M., Chandratilleke, G.R., Dong, K., and Yu, A. (2016). The effect of interparticle cohesion on powder mixing in a ribbon mixer. AIChE J. 62: 1023–1037, https://doi.org/10.1002/aic.15101.Search in Google Scholar

Halidan, M., Chandratilleke, G.R., Dong, K.J., and Yu, A.B. (2018). Mixing performance of ribbon mixers: effects of operational parameters. Powder Technol. 325: 92–106, https://doi.org/10.1016/j.powtec.2017.11.009.Search in Google Scholar

Hare, C., Zafar, U., Ghadiri, M., Freeman, T., Clayton, J., and Murtagh, M.J. (2015). Analysis of the dynamics of the FT4 powder rheometer. Powder Technol. 285: 123–127, https://doi.org/10.1016/j.powtec.2015.04.039.Search in Google Scholar

Harish, V.V.N., Cho, M., and Shim, J. (2019). Effect of rotating cylinder on mixing performance in a cylindrical double-ribbon mixer. Appl. Sci. 9: 5179, https://doi.org/10.3390/app9235179.Search in Google Scholar

Harnby, N., Edwards, M.F., and Nienow, A.W. (1985). Mixing in the process industries, 2nd ed. Butterworth-Heinemann, Oxford.Search in Google Scholar

Hassanpour, A. and Pasha, M. (2014). Discrete element method applications in process engineering. In: Introduction to software for chemical engineers. CRC Press, Boca Raton.10.1201/9780429451010-9Search in Google Scholar

Hassanpour, A., Tan, H., Bayly, A., Gopalkrishnan, P., Ng, B., and Ghadiri, M. (2011). Analysis of particle motion in a paddle mixer using discrete element method (DEM). Powder Technol. 206: 189–194, https://doi.org/10.1016/j.powtec.2010.07.025.Search in Google Scholar

He, L. and Tafti, D.K. (2019). A supervised machine learning approach for predicting variable drag forces on spherical particles in suspension. Powder Technol. 345: 379–389, https://doi.org/10.1016/j.powtec.2019.01.013.Search in Google Scholar

He, S., Gan, J., Pinson, D., Yu, A., and Zhou, Z. (2020). A discrete element method study of monodisperse mixing of ellipsoidal particles in a rotating drum. Ind. Eng. Chem. Res. 59: 12458–12470, https://doi.org/10.1021/acs.iecr.9b06623.Search in Google Scholar

He, S.Y., Gan, J.Q., Pinson, D., and Zhou, Z.Y. (2019). Particle shape-induced radial segregation of binary mixtures in a rotating drum. Powder Technol. 341: 157–166, https://doi.org/10.1016/j.powtec.2018.06.005.Search in Google Scholar

He, S.Y., Gan, J.Q., Pinson, D., Yu, A.B., and Zhou, Z.Y. (2021). Particle shape-induced axial segregation of binary mixtures of spheres and ellipsoids in a rotating drum. Chem. Eng. Sci. 235: 116491, https://doi.org/10.1016/j.ces.2021.116491.Search in Google Scholar

Herman, A.P., Gan, J., and Yu, A. (2021). GPU-based DEM simulation for scale-up of bladed mixers. Powder Technol. 382: 300–317, https://doi.org/10.1016/j.powtec.2020.12.045.Search in Google Scholar

Hlosta, J., Jezerská, L., Rozbroj, J., Žurovec, D., Nečas, J., and Zegzulka, J. (2020). DEM Investigation of the influence of particulate properties and operating conditions on the mixing process in rotary drums: part 2 – process validation and experimental study. Processes 8: 184, https://doi.org/10.3390/pr8020184.Search in Google Scholar

Hogg, R. (2009). Mixing and segregation in powders: evaluation, mechanisms and processes. KONA Powder Part. J. 27: 3–17, https://doi.org/10.14356/kona.2009005.Search in Google Scholar

Hopkins, M.A. (2014). Polyhedra faster than spheres? Eng. Comput. 31: 567–583, https://doi.org/10.1108/ec-09-2012-0211.Search in Google Scholar

Hoshishima, C., Ohsaki, S., Nakamura, H., and Watano, S. (2021). Parameter calibration of discrete element method modelling for cohesive and non-spherical particles of powder. Powder Technol. 386: 199–208, https://doi.org/10.1016/j.powtec.2021.03.044.Search in Google Scholar

Hwang, C.L. and Hogg, R. (1980). Diffusive mixing in flowing powders. Powder Technol. 26: 93–101, https://doi.org/10.1016/0032-5910(80)85011-x.Search in Google Scholar

Jadidi, B., Ebrahimi, M., Ein-Mozaffari, F.and Lohi, A. (2022). Mixing performance analysis of non-cohesive particles in a double paddle blender using DEM and experiments. Powder Technol., 397, 117122, https://doi.org/10.1016/j.powtec.2022.117122.Search in Google Scholar

Ji, S., Wang, S., and Zhou, Z. (2020). Influence of particle shape on mixing rate in rotating drums based on super-quadric DEM simulations. Adv. Powder Technol. 31: 3540–3550, https://doi.org/10.1016/j.apt.2020.06.040.Search in Google Scholar

Johnstone, M.W. (2010). Calibration of DEM models for granular materials using bulk physical tests, Ph.D. thesis. University of Edinburgh.Search in Google Scholar

Jones, J.R. and Bridgwater, J. (1998). A case study of particle mixing in a ploughshare mixer using positron emission particle tracking. Int. J. Miner. Process. 53: 29–38, https://doi.org/10.1016/s0301-7516(97)00054-9.Search in Google Scholar

Kehlenbeck, V. (2011). Use of near infrared spectroscopy for in- and off-line performance determination of continuous and batch powder mixers: opportunities & challenges. Procedia Food Sci. 1: 2015–2022, https://doi.org/10.1016/j.profoo.2011.10.002.Search in Google Scholar

Kingston, T.A. (2013). Granular mixing visualization and quantification in a double screw mixer, Ph.D. thesis. Digital Repository. Iowa State University.10.31274/etd-180810-261Search in Google Scholar

Kingston, T.A. and Heindel, T.J. (2014). Optical visualization and composition analysis to quantify continuous granular mixing processes. Powder Technol. 262: 257–264, https://doi.org/10.1016/j.powtec.2014.04.071.Search in Google Scholar

Kingston, T.A., Geick, T.A., Robinson, T.R., and Heindel, T.J. (2015). Characterizing 3D granular flow structures in a double screw mixer using x-ray particle tracking velocimetry. Powder Technol. 278: 211–222, https://doi.org/10.1016/j.powtec.2015.02.061.Search in Google Scholar

Knowlton, J.L. and Pearce, S.E. (2013). Handbook of cosmetic science & technology. Elsevier, Oxford.Search in Google Scholar

Kodam, M., Bharadwaj, R., Curtis, J., Hancock, B., and Wassgren, C. (2010). Cylindrical object contact detection for use in discrete element method simulations. Part I: contact detection algorithms. Chem. Eng. Sci. 65: 5852–5862, https://doi.org/10.1016/j.ces.2010.08.006.Search in Google Scholar

Koller, D.M., Posch, A., Hörl, G., Voura, C., Radl, S., Urbanetz, N., Fraser, S.D., Tritthart, W., Reiter, F., Schlingmann, M., et al. (2011). Continuous quantitative monitoring of powder mixing dynamics by near-infrared spectroscopy. Powder Technol. 205: 87–96, https://doi.org/10.1016/j.powtec.2010.08.070.Search in Google Scholar

Kondic, L. (1999). Dynamics of spherical particles on a surface: collision-induced sliding and other effects. Phys. Rev. E 60: 751–770, https://doi.org/10.1103/physreve.60.751.Search in Google Scholar PubMed

Kretz, D., Callau-Monje, S., Hitschler, M., Hien, A., Raedle, M., and Hesser, J. (2016). Discrete element method (DEM) simulation and validation of a screw feeder system. Powder Technol. 287: 131–138, https://doi.org/10.1016/j.powtec.2015.09.038.Search in Google Scholar

Kruggel-Emden, H., Simsek, E., Rickelt, S., Wirtz, S., and Scherer, V. (2007). Review and extension of normal force models for the discrete element method. Powder Technol. 171: 157–173, https://doi.org/10.1016/j.powtec.2006.10.004.Search in Google Scholar

Kumar, P., Sinha, K., Nere, N.K., Shin, Y., Ho, R., Mlinar, L.B., and Sheikh, A.Y. (2020). A machine learning framework for computationally expensive transient models. Sci. Rep. 10: 11492, https://doi.org/10.1038/s41598-020-67546-w.Search in Google Scholar PubMed PubMed Central

Kuo, H.P., Knight, P.C., Parker, D.J., and Seville, J.P.K. (2005). Solids circulation and axial dispersion of cohesionless particles in a V-mixer. Powder Technol. 152: 133–140, https://doi.org/10.1016/j.powtec.2004.12.003.Search in Google Scholar

Kurowski, K., Kulczewski, M., and Dobski, M. (2011). Parallel and GPU based strategies for selected CFD and climate modeling models. Environ. Sci. Eng. 3: 735–747, doi:https://doi.org/10.1007/978-3-642-19536-5_57.Search in Google Scholar

Lacey, P.M.C. (1954). Developments in the theory of particle mixing. J. Appl. Chem. 4: 257–268.10.1002/jctb.5010040504Search in Google Scholar

Langston, P.A., Tüzün, U., and Heyes, D.M. (1994). Continuous potential discrete particle simulations of stress and velocity fields in hoppers: transition from fluid to granular flow. Chem. Eng. Sci. 49: 1259–1275, https://doi.org/10.1016/0009-2509(94)85095-x.Search in Google Scholar

Laurent, B.F.C. and Cleary, P.W. (2012). Comparative study by PEPT and DEM for flow and mixing in a ploughshare mixer. Powder Technol. 228: 171–186, https://doi.org/10.1016/j.powtec.2012.05.013.Search in Google Scholar

Lemieux, M., Bertrand, F., Chaouki, J., and Gosselin, P. (2007). Comparative study of the mixing of free-flowing particles in a V-blender and a bin-blender. Chem. Eng. Sci. 62: 1783–1802, https://doi.org/10.1016/j.ces.2006.12.012.Search in Google Scholar

Lindley, J.A. (1991). Mixing processes for agricultural and food materials: 1. fundamentals of mixing. J. Agric. Eng. Res. 4: 153–170, https://doi.org/10.1016/0021-8634(91)80012-4.Search in Google Scholar

Liu, H., Tafti, D.K., and Li, T. (2014). Hybrid parallelism in MFIX CFD-DEM using OpenMP. Powder Technol. 259: 22–29, https://doi.org/10.1016/j.powtec.2014.03.047.Search in Google Scholar

Liu, P.Y., Yang, R.Y., and Yu, A.B. (2013). DEM study of the transverse mixing of wet particles in rotating drums. Chem. Eng. Sci. 86: 99–107, https://doi.org/10.1016/j.ces.2012.06.015.Search in Google Scholar

Lu, G., Third, J.R., and Müller, C.R. (2015). Discrete element models for non-spherical particle systems: from theoretical developments to applications. Chem. Eng. Sci. 127: 425–465, https://doi.org/10.1016/j.ces.2014.11.050.Search in Google Scholar

Luding, S. (2008). Cohesive, frictional powders: contact models for tension. Granul. Matter 10: 235–246, https://doi.org/10.1007/s10035-008-0099-x.Search in Google Scholar

Ma, H. and Zhao, Y. (2017). Modelling of the flow of ellipsoidal particles in a horizontal rotating drum based on DEM simulation. Chem. Eng. Sci. 172: 636–651, https://doi.org/10.1016/j.ces.2017.07.017.Search in Google Scholar

Maknickas, A., Kačeniauskas, A., Kačianauskas, R., Balevičius, R., and Džiugys, A. (2006). Parallel DEM software for simulation of granular media. Informatica 17: 207–224, https://doi.org/10.15388/informatica.2006.134.Search in Google Scholar

Marigo, M., Cairns, D.L., Davies, M., Ingram, A., and Stitt, E.H. (2012). A numerical comparison of mixing efficiencies of solids in a cylindrical vessel subject to a range of motions. Powder Technol. 217: 540–547, https://doi.org/10.1016/j.powtec.2011.11.016.Search in Google Scholar

Marigo, M., Davies, M., Leadbeater, T., Cairns, D.L., Ingram, A., and Stitt, E.H. (2013). Application of positron emission particle tracking (PEPT) to validate a discrete element method (DEM) model of granular flow and mixing in the Turbula mixer. Int. J. Pharm. 446: 46–58, https://doi.org/10.1016/j.ijpharm.2013.01.030.Search in Google Scholar

Marucci, M., Al-Saaigh, B., Boissier, C., Wahlgren, M., and Wikström, H. (2018). Sifting segregation of ideal blends in a two-hopper tester: segregation profiles and segregation magnitudes. Powder Technol. 331: 60–67, https://doi.org/10.1016/j.powtec.2018.01.070.Search in Google Scholar

Masuda, H., Higashitani, K., and Yoshida, H. (2006). Powder technology: fundamentals of particles, powder beds, and particle generation. CRC press, Boca Raton.10.1201/9781420044119Search in Google Scholar

Mendez, A.S.L., de Carli, G., and Garcia, C.V. (2010). Evaluation of powder mixing operation during batch production: application to operational qualification procedure in the pharmaceutical industry. Powder Technol. 198: 310–313, https://doi.org/10.1016/j.powtec.2009.11.027.Search in Google Scholar

Mindlin, D.R. and Deresiewicz, H. (1953). Elastic spheres in contact under varying oblique forces. J. Appl. Mech. 20: 327–344, https://doi.org/10.1115/1.4010702.Search in Google Scholar

Moakher, M., Shinbrot, T., and Muzzio, F.J. (2000). Experimentally validated computations of flow, mixing and segregation of non-cohesive grains in 3D tumbling blenders. Powder Technol. 109: 58–71, https://doi.org/10.1016/s0032-5910(99)00227-2.Search in Google Scholar

Mosby, J., de Silva, S.R., and Enstad, G.G. (1996). Segregation of particulate materials – mechanisms and testers. KONA Powder Part. J. 14: 31–43, https://doi.org/10.14356/kona.1996008.Search in Google Scholar

Muzzio, F.J., Robinson, P., Wightman, C., and Brone, D. (1997). Sampling practices in powder blending. Int. J. Pharm. 155: 153–178, https://doi.org/10.1016/s0378-5173(97)04865-5.Search in Google Scholar

Muzzio, F.J., Goodridge, C.L., Alexander, A., Arratia, P., Yang, H., Sudah, O., and Mergen, G. (2003). Sampling and characterization of pharmaceutical powders and granular blends. Int. J. Pharm. 250: 51–64, https://doi.org/10.1016/s0378-5173(02)00481-7.Search in Google Scholar

Nadeem, H. and Heindel, T.J. (2018). Review of noninvasive methods to characterize granular mixing. Powder Technol. 332: 331–350, https://doi.org/10.1016/j.powtec.2018.03.035.Search in Google Scholar

Nakamura, H., Fujii, H., and Watano, S. (2013). Scale-up of high shear mixer-granulator based on discrete element analysis. Powder Technol. 236: 149–156, https://doi.org/10.1016/j.powtec.2012.03.009.Search in Google Scholar

Nakamura, H., Takimoto, H., Kishida, N., Ohsaki, S., and Watano, S. (2020). Coarse-grained discrete element method for granular shear flow. Chem. Eng. J. Adv. 4: 100050, https://doi.org/10.1016/j.ceja.2020.100050.Search in Google Scholar

Nassauer, B., Liedke, T., and Kuna, M. (2012). Polyhedral particles for the discrete element method. Granul. Matter 15: 85–93, https://doi.org/10.1007/s10035-012-0381-9.Search in Google Scholar

Nezami, E.G., Hashash, Y.M.A., Zhao, D., and Ghaboussi, J. (2004). A fast contact detection algorithm for 3-D discrete element method. Comput. Geotech. 31: 575–587, https://doi.org/10.1016/j.compgeo.2004.08.002.Search in Google Scholar

Niranjan, K., Smith, D.L.O., Rielly, C.D., Lindley, J.A., and Phillips, V.R. (1994). Mixing processes for agricultural and food materials. Part 5: review of mixer types. J. Agric. Eng. Res. 59: 145–161, https://doi.org/10.1006/jaer.1994.1072.Search in Google Scholar

Norouzi, H.R., Zarghami, R., Sotudeh-Gharebagh, R., and Mostoufi, N. (2016). Coupled CFD-DEM modeling: formulation, implementation and application to multiphase flows. John Wiley & Sons, Chichester.10.1002/9781119005315Search in Google Scholar

Orefice, L. and Khinast, J.G. (2020). A novel framework for a rational, fully-automatised calibration routine for DEM models of cohesive powders. Powder Technol. 361: 687–703, https://doi.org/10.1016/j.powtec.2019.11.054.Search in Google Scholar

Ortega-Rivas, E. (2012). Unit operations of particulate solids: theory and practice. CRC Press, Boca Raton.Search in Google Scholar

Pachón-Morales, J., Perré, P., Casalinho, J., Do, H., Schott, D., Puel, F., and Colin, J. (2020). Potential of DEM for investigation of non-consolidated flow of cohesive and elongated biomass particles. Adv. Powder Technol. 31: 1500–1515.10.1016/j.apt.2020.01.023Search in Google Scholar

Palmer, J., Reynolds, G.K., Tahir, F., Yadav, I.K., Meehan, E., Holman, J., and Bajwa, G. (2020). Mapping key process parameters to the performance of a continuous dry powder blender in a continuous direct compression system. Powder Technol. 362: 659–670, https://doi.org/10.1016/j.powtec.2019.12.028.Search in Google Scholar

Pantaleev, S., Yordanova, S., Janda, A., Marigo, M., and Ooi, J.Y. (2017). An experimentally validated DEM study of powder mixing in a paddle blade mixer. Powder Technol. 311: 287–302, https://doi.org/10.1016/j.powtec.2016.12.053.Search in Google Scholar

Parker, D.J., Dijkstra, A.E., Martin, T.W., and Seville, J.P.K. (1997). Positron emission particle tracking studies of spherical particle motion in rotating drums. Chem. Eng. Sci. 52: 2011–2022, https://doi.org/10.1016/s0009-2509(97)00030-4.Search in Google Scholar

Pasha, M., Dogbe, S., Hare, C., Hassanpour, A., and Ghadiri, M. (2014). A linear model of elasto-plastic and adhesive contact deformation. Granul. Matter 16: 151–162, https://doi.org/10.1007/s10035-013-0476-y.Search in Google Scholar

Paul, E.L., Atiemo-Obeng, V.A., and Kresta, S.M. (2003). Handbook of industrial mixing: science and practice, 1. John Wiley & Sons, Chichester.10.1002/0471451452Search in Google Scholar

Perrault, M., Bertrand, F., and Chaouki, J. (2010). An investigation of magnesium stearate mixing in a V-blender through gamma-ray detection. Powder Technol.: 234–245, https://doi.org/10.1016/j.powtec.2010.02.030.Search in Google Scholar

Portillo, P.M., Ierapetritou, M., Tomassone, S., Mc Dade, C., Clancy, D., Avontuur, P.P.C., and Muzzio, F.J. (2008). Quality by design methodology for development and scale-up of batch mixing processes. J. Pharmaceut. Innovat. 3: 258–270, https://doi.org/10.1007/s12247-008-9048-9.Search in Google Scholar

Portillo, P.M., Ierapetritou, M.G., and Muzzio, F.J. (2009). Effects of rotation rate, mixing angle, and cohesion in two continuous powder mixers – a statistical approach. Powder Technol. 194: 217–227, https://doi.org/10.1016/j.powtec.2009.04.010.Search in Google Scholar

Portillo, P.M., Vanarase, A.U., Ingram, A., Seville, J.K., Ierapetritou, M.G., and Muzzio, F.J. (2010). Investigation of the effect of impeller rotation rate, powder flow rate, and cohesion on powder flow behavior in a continuous blender using PEPT. Chem. Eng. Sci. 65: 5658–5668, https://doi.org/10.1016/j.ces.2010.06.036.Search in Google Scholar

Poux, M., Fayolle, P., Bertrand, J., Bridoux, D., and Bousquet, J. (1991). Powder mixing: some practical rules applied to agitated systems. Powder Technol. 68: 213–234, https://doi.org/10.1016/0032-5910(91)80047-m.Search in Google Scholar

Prokopovich, P. and Perni, S. (2011). Comparison of JKR- and DMT-based multi-asperity adhesion model: theory and experiment. Colloids Surf. A Physicochem. Eng. Asp. 383: 95–101, https://doi.org/10.1016/j.colsurfa.2011.01.011.Search in Google Scholar

Qi, F., Heindel, T.J., and Wright, M.M. (2017). Numerical study of particle mixing in a lab-scale screw mixer using the discrete element method. Powder Technol. 308: 334–345, https://doi.org/10.1016/j.powtec.2016.12.043.Search in Google Scholar

Rackl, M. and Hanley, K.J. (2017). A methodical calibration procedure for discrete element models. Powder Technol. 307: 73–83, https://doi.org/10.1016/j.powtec.2016.11.048.Search in Google Scholar

Radeke, C.A., Glasser, B.J., and Khinast, J.G. (2010). Large-scale powder mixer simulations using massively parallel GPUarchitectures. Chem. Eng. Sci. 65: 6435–6442, https://doi.org/10.1016/j.ces.2010.09.035.Search in Google Scholar

Radl, S., Kalvoda, E., Glasser, B.J., and Khinast, J.G. (2010). Mixing characteristics of wet granular matter in a bladed mixer. Powder Technol. 200: 171–189, https://doi.org/10.1016/j.powtec.2010.02.022.Search in Google Scholar

Remy, B., Khinast, J.G., and Glasser, B.J. (2009). Discrete element simulation of free flowing grains in a four-bladed mixer. AIChE J. 55: 2035–2048, https://doi.org/10.1002/aic.11876.Search in Google Scholar

Remy, B., Canty, T.M., Khinast, J.G., and Glasser, B.J. (2010a). Experiments and simulations of cohesionless particles with varying roughness in a bladed mixer. Chem. Eng. Sci. 65: 4557–4571, https://doi.org/10.1016/j.ces.2010.04.034.Search in Google Scholar

Remy, B., Glasser, B.J., and Khinast, J.G. (2010b). The effect of mixer properties and fill level on granular flow in a bladed mixer. AIChE J. 56: 336–353.10.1002/aic.11979Search in Google Scholar

Remy, B., Khinast, J.G., and Glasser, B.J. (2011). Polydisperse granular flows in a bladed mixer: experiments and simulations of cohesionless spheres. Chem. Eng. Sci. 66: 1811–1824, https://doi.org/10.1016/j.ces.2010.12.022.Search in Google Scholar

Ren, X., Xu, J., Qi, H., Cui, L., Ge, W., and Li, J. (2013). GPU-based discrete element simulation on a tote blender for performance improvement. Powder Technol. 239: 348–357, https://doi.org/10.1016/j.powtec.2013.02.019.Search in Google Scholar

Rhodes, M. (2008). Introduction to particle technology, 2nd ed. John Wiley and Sons, Chichester.10.1002/9780470727102Search in Google Scholar

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 Technol. 360: 967–976, https://doi.org/10.1016/j.powtec.2019.10.052.Search in Google Scholar

Rong, W., Feng, Y., Schwarz, P., Yurata, T., Witt, P., Li, B., Song, T., and Zhou, J. (2020). Sensitivity analysis of particle contact parameters for DEM simulation in a rotating drum using response surface methodology. Powder Technol. 362: 604–614, https://doi.org/10.1016/j.powtec.2019.12.004.Search in Google Scholar

Saberian, M., Segonne, Y., Briens, C., Bousquet, J., Chabagno, J.M., and Denizart, O. (2002). Blending of polymers in high speed, vertical mixers: development of a thermal tracer measurement procedure. Powder Technol. 123: 25–32, https://doi.org/10.1016/s0032-5910(01)00428-4.Search in Google Scholar

Sacher, S. and Khinast, J.G. (2016). An overview of pharmaceutical manufacturing for solid dosage forms. Methods Pharmacol. Toxicol. 32: 311–383, https://doi.org/10.1007/978-1-4939-2996-2_10.Search in Google Scholar

Safranyik, F., Keppler, I., and Bablena, A. (2017). DEM Calibration: a complex optimization problem. 2017 international conference on control, artificial intelligence, robotics & optimization (ICCAIRO). IEEE, pp. 198–201.10.1109/ICCAIRO.2017.46Search in Google Scholar

Sakai, M. (2016). How should the discrete element method be applied in industrial systems? A review. KONA Powder Part. J. 33: 169–178, https://doi.org/10.14356/kona.2016023.Search in Google Scholar

Sakai, M. and Koshizuka, S. (2009). Large-scale discrete element modeling in pneumatic conveying. Chem. Eng. Sci. 64: 533–539, https://doi.org/10.1016/j.ces.2008.10.003.Search in Google Scholar

Sakai, M., Takahashi, H., Pain, C.C., Latham, J.P., and Xiang, J. (2012). Study on a large-scale discrete element model for fine particles in a fluidized bed. Adv. Powder Technol. 23: 673–681, https://doi.org/10.1016/j.apt.2011.08.006.Search in Google Scholar

Sakai, M., Shigeto, Y., Basinskas, G., Hosokawa, A., and Fuji, M. (2015). Discrete element simulation for the evaluation of solid mixing in an industrial blender. Chem. Eng. J. 279: 821–839, https://doi.org/10.1016/j.cej.2015.04.130.Search in Google Scholar

Schutyser, M.A.I., Briels, W.J., Rinzema, A., and Boom, R.M. (2003). Numerical simulation and PEPT measurements of a 3D conical helical-blade mixer: a high potential solids mixer for solid-state fermentation. Biotechnol. Bioeng. 84: 29–39, https://doi.org/10.1002/bit.10739.Search in Google Scholar PubMed

Sebastian Escotet-Espinoza, M., Foster, C.J., and Ierapetritou, M. (2018). Discrete element modeling (DEM) for mixing of cohesive solids in rotating cylinders. Powder Technol. 335: 124–136, https://doi.org/10.1016/j.powtec.2018.05.024.Search in Google Scholar

Shigeto, Y. and Sakai, M. (2011). Parallel computing of discrete element method on multi-core processors. Particuology 9: 398–405, https://doi.org/10.1016/j.partic.2011.04.002.Search in Google Scholar

Siiriä, S. and Yliruusi, J. (2009). Determining a value for mixing: mixing degree. Powder Technol. 196: 309–317.10.1016/j.powtec.2009.08.009Search in Google Scholar

Silva, S.R., Dyrøy, A., and Enstad, G.G. (2000). Segregation mechanisms and their quantification using segregation testers. Dordrecht: Springer, pp. 11–29.10.1007/978-94-015-9498-1_2Search in Google Scholar

Sinnott, M.D. and Cleary, P.W. (2016). The effect of particle shape on mixing in a high shear mixer. Comput. Part. Mech. 3: 477–504, https://doi.org/10.1007/s40571-015-0065-4.Search in Google Scholar

Stambaugh, J., Smith, Z., Ott, E., and Losert, W. (2004). Segregation in a monolayer of magnetic spheres. Phys. Rev. 70: 6, https://doi.org/10.1103/PhysRevE.70.031304.Search in Google Scholar

Stevens, A.B. and Hrenya, C.M. (2005). Comparison of soft-sphere models to measurements of collision properties during normal impacts. Powder Technol. 154: 99–109, https://doi.org/10.1016/j.powtec.2005.04.033.Search in Google Scholar

Stewart, R.L., Bridgwater, J., and Parker, D.J. (2001a). Granular flow over a flat-bladed stirrer. Chem. Eng. Sci. 56: 4257–4271, https://doi.org/10.1016/s0009-2509(01)00104-x.Search in Google Scholar

Stewart, R.L., Bridgwater, J., Zhou, Y.C., and Yu, A.B. (2001b). Simulated and measured flow of granules in a bladed mixer - a detailed comparison. Chem. Eng. Sci. 56: 5457–5471, https://doi.org/10.1016/s0009-2509(01)00190-7.Search in Google Scholar

Swarbrick, J. (2013). Encyclopedia of pharmaceutical technology, 6. CRC Press, Boca Raton.10.1201/b19309Search in Google Scholar

Tahvildarian, P., Ein-Mozaffari, F., and Upreti, S.R. (2013). Circulation intensity and axial dispersion of non-cohesive solid particles in a V-blender via DEM simulation. Particuology 11: 619–626, https://doi.org/10.1016/j.partic.2012.12.010.Search in Google Scholar

Tang, P. and Puri, V.M. (2004). Methods for minimizing segregation: a review. Part. Sci. Technol. 22: 321–337, https://doi.org/10.1080/02726350490501420.Search in Google Scholar

Thakur, S.C., Morrissey, J.P., Sun, J., Chen, J.F., and Ooi, J.Y. (2014). Micromechanical analysis of cohesive granular materials using the discrete element method with an adhesive elasto-plastic contact model. Granul. Matter 16: 383–400, https://doi.org/10.1007/s10035-014-0506-4.Search in Google Scholar

Thakur, S.C., Ooi, J.Y., and Ahmadian, H. (2016). Scaling of discrete element model parameters for cohesionless and cohesive solid. Powder Technol. 293: 130–137, https://doi.org/10.1016/j.powtec.2015.05.051.Search in Google Scholar

Tsugeno, Y., Sakai, M., Yamazaki, S., and Nishinomiya, T. (2021). DEM simulation for optimal design of powder mixing in a ribbon mixer. Adv. Powder Technol. 32: 1735–1749, https://doi.org/10.1016/j.apt.2021.03.026.Search in Google Scholar

Tsuji, Y., Tanaka, T., and Ishida, T. (1992). Lagrangian numerical simulation of plug flow of cohesionless particles in a horizontal pipe. Powder Technol. 71: 239–250, https://doi.org/10.1016/0032-5910(92)88030-l.Search in Google Scholar

Vanarase, A.U. and Muzzio, F.J. (2011). Effect of operating conditions and design parameters in a continuous powder mixer. Powder Technol. 208: 26–36, https://doi.org/10.1016/j.powtec.2010.11.038.Search in Google Scholar

Vasudeo Rane, A., Kanny, K., Abitha, V.K., Patil, S.S., and Thomas, S. (2017). Clay-polymer composites: design of clay polymer nanocomposite by mixing. Clay-Polymer Nanocomposites: 113–144, https://doi.org/10.1016/b978-0-323-46153-5.00004-5.Search in Google Scholar

Walton, O.R. and Braun, R.L. (1986a). Stress calculations for assemblies of inelastic speres in uniform shear. Acta Mech. 63: 73–86, https://doi.org/10.1007/bf01182541.Search in Google Scholar

Walton, O.R. and Braun, R.L. (1986b). Viscosity, granular‐temperature, and stress calculations for shearing assemblies of inelastic, frictional disks. J. Rheol. 30: 949–980, https://doi.org/10.1122/1.549893.Search in Google Scholar

Washizawa, T. and Nakahara, Y. (2013). Parallel computing of discrete element method on GPU. Appl. Math. 4: 242–247, https://doi.org/10.4236/am.2013.41a037.Search in Google Scholar

Wen, Y., Liu, M., Liu, B., and Shao, Y. (2015). Comparative study on the characterization method of particle mixing index using DEM method. Procedia Eng. 102: 1630–1642, https://doi.org/10.1016/j.proeng.2015.01.299.Search in Google Scholar

Williams, J.R. and Pentland, A.P. (1992). Superquadrics and modal dynamics for discrete elements in interactive design. Eng. Comput. 9: 115–127, https://doi.org/10.1108/eb023852.Search in Google Scholar

Yan, Z., Wilkinson, S.K., Stitt, E.H., and Marigo, M. (2015). Discrete element modelling (DEM) input parameters: understanding their impact on model predictions using statistical analysis. Comput. Part. Mech. 2: 283–299, https://doi.org/10.1007/s40571-015-0056-5.Search in Google Scholar

Yaraghi, A. (2018). Mixing assessment of non-cohesive mono-disperse and bi-disperse particles in a paddle mixer – experiments and discrete element method (DEM), M.Sc thesis. Toronto: Ryerson University.Search in Google Scholar

Yaraghi, A., Ebrahimi, M., Ein-Mozaffari, F., and Lohi, A. (2018). Mixing assessment of non-cohesive particles in a paddle mixer through experiments and discrete element method (DEM). Adv. Powder Technol. 29: 2693–2706, https://doi.org/10.1016/j.apt.2018.07.019.Search in Google Scholar

Yazdani, E. and Hashemabadi, S.H. (2019). The influence of cohesiveness on particulate bed segregation and mixing in rotating drum using DEM. Phys. Stat. Mech. Appl. 525: 788–797, https://doi.org/10.1016/j.physa.2019.03.127.Search in Google Scholar

Yeom, S.B., Ha, E., Kim, M., Jeong, S.H., Hwang, S.J., and Choi, D.H. (2019). Application of the discrete element method for manufacturing process simulation in the pharmaceutical industry. Pharmaceutics 11: 414, https://doi.org/10.3390/pharmaceutics11080414.Search in Google Scholar PubMed PubMed Central

Yoon, J. (2007). Application of experimental design and optimization to PFC model calibration in uniaxial compression simulation. Int. J. Rock Mech. Min. Sci. 44: 871–889, https://doi.org/10.1016/j.ijrmms.2007.01.004.Search in Google Scholar

You, Y. and Zhao, Y. (2018). Discrete element modelling of ellipsoidal particles using super-ellipsoids and multi-spheres: a comparative study. Powder Technol. 331: 179–191, https://doi.org/10.1016/j.powtec.2018.03.017.Search in Google Scholar

Zhao, Y., Akolekar, H.D., Weatheritt, J., Michelassi, V., and Sandberg, R.D. (2020). RANS turbulence model development using CFD-driven machine learning. J. Comput. Phys. 411: 109413, https://doi.org/10.1016/j.jcp.2020.109413.Search in Google Scholar

Zheng, Q.J., Zhu, H.P., and Yu, A.B. (2012). Finite element analysis of the contact forces between a viscoelastic sphere and rigid plane. Powder Technol. 226: 130–142, https://doi.org/10.1016/j.powtec.2012.04.032.Search in Google Scholar

Zhong, W., Yu, A., Liu, X., Tong, Z., and Zhang, H. (2016). DEM/CFD-DEM modelling of non-spherical particulate systems: theoretical developments and applications. Powder Technol. 302: 108–152, https://doi.org/10.1016/j.powtec.2016.07.010.Search in Google Scholar

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. Phys. Stat. Mech. Appl. 269: 536–553, https://doi.org/10.1016/s0378-4371(99)00183-1.Search in Google Scholar

Zhou, Y.C., Yu, A.B., and Bridgwater, J. (2003). Segregation of binary mixture of particles in a bladed mixer. J. Chem. Technol. Biotechnol. 78: 187–193, https://doi.org/10.1002/jctb.731.Search in Google Scholar

Zhu, H.P., Zhou, Z.Y., Yang, R.Y., and Yu, A.B. (2007). Discrete particle simulation of particulate systems: theoretical developments. Chem. Eng. Sci. 62: 3378–3396, https://doi.org/10.1016/j.ces.2006.12.089.Search in Google Scholar

Zhu, J., Zou, M., Liu, Y., Gao, K., Su, B., and Qi, Y. (2022). Measurement and calibration of DEM parameters of lunar soil simulant. Acta Astronaut. 191: 169–177, https://doi.org/10.1016/j.actaastro.2021.11.009.Search in Google Scholar

Received: 2021-06-06
Revised: 2022-02-21
Accepted: 2022-02-22
Published Online: 2022-03-28

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 26.3.2023 from https://www.degruyter.com/document/doi/10.1515/revce-2021-0049/html
Scroll Up Arrow