Computational Particle Mechanics

, Volume 6, Issue 1, pp 3–10 | Cite as

Calibration of discrete element model parameters: soybeans

  • Bhupendra M GhodkiEmail author
  • Manish Patel
  • Rohit Namdeo
  • Gopal Carpenter


Discrete element method (DEM) simulations are broadly used to get an insight of flow characteristics of granular materials in complex particulate systems. DEM input parameters for a model are the critical prerequisite for an efficient simulation. Thus, the present investigation aims to determine DEM input parameters for Hertz–Mindlin model using soybeans as a granular material. To achieve this aim, widely acceptable calibration approach was used having standard box-type apparatus. Further, qualitative and quantitative findings such as particle profile, height of kernels retaining the acrylic wall, and angle of repose of experiments and numerical simulations were compared to get the parameters. The calibrated set of DEM input parameters includes the following (a) material properties: particle geometric mean diameter (6.24 mm); spherical shape; particle density (\(1220~\hbox {kg m}^{-3}\)), and (b) interaction parameters such as particle–particle: coefficient of restitution (0.17); coefficient of static friction (0.26); coefficient of rolling friction (0.08), and particle–wall: coefficient of restitution (0.35); coefficient of static friction (0.30); coefficient of rolling friction (0.08). The results may adequately be used to simulate particle scale mechanics (grain commingling, flow/motion, forces, etc) of soybeans in post-harvest machinery and devices.


DEM Material properties Interaction properties Simulation Coefficient of restitution 



The authors would like to acknowledge Prof. T. K. Goswami, Agricultural and Food Engineering Department, IIT Kharagpur, India for providing his invaluable support in conducting this study. The opinions expressed in this article do by no means reflect the official opinion of IIT Kharagpur and Indian Council of Agricultural Research or their representatives. There is no conflict of interest among authors.


  1. 1.
    Coetzee CJ (2016) Calibration of the discrete element method and the effect of particle shape. Powder Technol 297:50–70CrossRefGoogle Scholar
  2. 2.
    Chaudhuri B, Muzzio FJ, Tomassone MS (2006) Modeling of heat transfer in granular flow in rotating vessels. Chem Eng Sci 61:6348–60CrossRefGoogle Scholar
  3. 3.
    Horabik J, Molenda M (2016) Parameters and contact models for DEM simulations of agricultural granular materials: a review. Biosyst Eng 147:206–25CrossRefGoogle Scholar
  4. 4.
    Zhu HP, Zhou ZY, Yang RY, Yu AB (2008) Discrete particle simulation of particulate systems: a review of major applications and findings. Chem Eng Sci 63:5728–70CrossRefGoogle Scholar
  5. 5.
    Ghodki BM, Charith Kumar K, Goswami TK (2018) Modeling breakage and motion of black pepper seeds in cryogenic mill. Adv Powder Technol 29:1055–1071CrossRefGoogle Scholar
  6. 6.
    Cundall PA, Strack OD (1979) A discrete numerical model for granular assemblies. Geotechnique 29:47–65CrossRefGoogle Scholar
  7. 7.
    Tijskens E, Ramon H, De Baerdemaeker J (2003) Discrete element modelling for process simulation in agriculture. J Sound Vib 266:493–514CrossRefGoogle Scholar
  8. 8.
    Zhu HP, Zhou ZY, Yang RY, Yu AB (2007) Discrete particle simulation of particulate systems: theoretical developments. Chem Eng Sci 62:3378–96CrossRefGoogle Scholar
  9. 9.
    Boac JM, Ambrose RPK, Casada ME, Maghirang RG, Maier DE (2014) Applications of discrete element method in modeling of grain postharvest operations. Food Eng Rev 6:128–49CrossRefGoogle Scholar
  10. 10.
    Ghodki BM, Goswami TK (2017) DEM simulation of flow of black pepper seeds in cryogenic grinding system. J Food Eng 196:36–51CrossRefGoogle Scholar
  11. 11.
    González-Montellano C, Fuentes JM, Ayuga-Téllez E, Ayuga F (2012) Determination of the mechanical properties of maize grains and olives required for use in DEM simulations. J Food Eng 111:553–62CrossRefGoogle Scholar
  12. 12.
    Marigo M, Stitt EH (2015) Discrete element method (DEM) for industrial applications: comments on calibration and validation for the modelling of cylindrical pellets. KONA Powder Part J 32:236–52CrossRefGoogle Scholar
  13. 13.
    Williams KC, Chen W, Weeger S, Donohue TJ (2014) Particle shape characterisation and its application to discrete element modelling. Particuology 12:80–9CrossRefGoogle Scholar
  14. 14.
    Yan Z, Wilkinson SK, Stitt EH, Marigo M (2015) Discrete element modelling (DEM) input parameters: understanding their impact on model predictions using statistical analysis. Comput Part Mech 2:283–99CrossRefGoogle Scholar
  15. 15.
    Derakhshani SM, Schott DL, Lodewijks G (2015) Micro-macro properties of quartz sand: experimental investigation and DEM simulation. Powder Technol 269:127–38CrossRefGoogle Scholar
  16. 16.
    Zhou YC, Wright BD, Yang RY, Xu BH, Yu AB (1999) Rolling friction in the dynamic simulation of sandpile formation. Phys A Stat Mech Appl 269:536–53CrossRefGoogle Scholar
  17. 17.
    Dutta SK, Nema VK, Bhardwaj RK (1988) Physical properties of gram. J Agric Eng Res 39:259–68CrossRefGoogle Scholar
  18. 18.
    Khazaei J, Ghanbari S (2010) New method for simultaneously measuring the angles of repose and frictional properties of wheat grains. Int Agrophys 24:275–86Google Scholar
  19. 19.
    ASTM G194-08 (2013) Standard test method for measuring rolling friction characteristics of a spherical shape on a flat horizontal plane.
  20. 20.
    Boac JM, Casada ME, Maghirang RG, Harner JP III (2010) Material and interaction properties of selected grains and oilseeds for modeling discrete particles. Trans ASABE 2010(53):1201–16CrossRefGoogle Scholar
  21. 21.
    Bortolotti CT, Santos KG, Francisquetti MCC, Duarte CR, Barrozo MAS (2013) Hydrodynamic study of a mixture of West Indian cherry residue and soybean grains in a spouted bed. Can J Chem Eng 91:1871–80Google Scholar
  22. 22.
    Markauskas D, Ramírez-Gómez Á, Kačianauskas R, Zdancevičius E (2015) Maize grain shape approaches for DEM modelling. Comput Electron Agric 118:247–58CrossRefGoogle Scholar
  23. 23.
    González-Montellano C, Ramírez Á, Gallego E, Ayuga F (2011) Validation and experimental calibration of 3D discrete element models for the simulation of the discharge flow in silos. Chem Eng Sci 66:5116–26CrossRefGoogle Scholar
  24. 24.
    Marinack MC Jr, Musgrave RE, Higgs CF III (2013) Experimental investigations on the coefficient of restitution of single particles. Tribol Trans 56:572–80CrossRefGoogle Scholar
  25. 25.
    Mishra BK (2003) A review of computer simulation of tumbling mills by the discrete element method: part I—contact mechanics. Int J Miner Process 71:73–93CrossRefGoogle Scholar
  26. 26.
    Dietsche F, Mülhaupt R (1999) Thermal properties and flammability of acrylic nanocomposites based upon organophilic layered silicates. Polym Bull 43:395–402CrossRefGoogle Scholar
  27. 27.
    Pohndorf RS, da Rocha JC, Lindemann I, Peres WB, de Oliveira M, Elias MC (2017) Physical properties and effective thermal diffusivity of soybean grains as a function of moisture content and broken kernels. J Food Process Eng 41:e12626CrossRefGoogle Scholar
  28. 28.
    Goswami TK, Ghodki BM (2015) Cryogenic grinding of cassia. In: International conference on advanced in chemical, biological and environmental engineering, pp 242–249Google Scholar
  29. 29.
    Ghodki BM, Goswami TK (2016) Effect of moisture on physical and mechanical properties of cassia. Cogent Food Agric 2:1192975Google Scholar
  30. 30.
    Ghodki BM, Goswami TK (2017) Thermal and mechanical properties of black pepper at different temperatures. J Food Process Eng 40:e12342CrossRefGoogle Scholar
  31. 31.
    Murthy CT, Bhattacharya S (1998) Moisture dependant physical and uniaxial compression properties of black pepper. J Food Eng 37:193–205CrossRefGoogle Scholar
  32. 32.
    Joshi DC, Das SK, Mukherjee RK (1993) Physical properties of pumpkin seeds. J Agric Eng Res 54:219–29CrossRefGoogle Scholar
  33. 33.
    Paulick M, Morgeneyer M, Kwade A (2015) Review on the influence of elastic particle properties on DEM simulation results. Powder Technol 283:66–76CrossRefGoogle Scholar
  34. 34.
    Matuttis HG, Luding S, Herrmann HJ (2000) Discrete element simulations of dense packings and heaps made of spherical and non-spherical particles. Powder Technol 109:278–92CrossRefGoogle Scholar
  35. 35.
    Zhou YC, Xu BH, Yu AB, Zulli P (2002) An experimental and numerical study of the angle of repose of coarse spheres. Powder Technol 125:45–54CrossRefGoogle Scholar

Copyright information

© OWZ 2018

Authors and Affiliations

  • Bhupendra M Ghodki
    • 1
    Email author
  • Manish Patel
    • 2
  • Rohit Namdeo
    • 3
  • Gopal Carpenter
    • 4
  1. 1.Horticultural Crop Processing DivisionICAR-Central Institute of Post-Harvest Engineering and TechnologyAboharIndia
  2. 2.Agricultural and Food Engineering Department, Indian Institute of TechnologyKharagpurIndia
  3. 3.Farm Machinery and Power Engineering Department, College of Agricultural EngineeringJawaharlal Nehru Krishi VishwavidyalayaJabalpurIndia
  4. 4.Post-Harvest Management DivisionICAR-Central Institute of Subtropical HorticultureLucknowIndia

Personalised recommendations