References
- Ai, J., J.-F. Chen, J. M. Rotter, and J. Y. Ooi. 2011. Assessment of rolling resistance models in discrete element simulations. Powder Technology 206 (3):269–82. doi: https://doi.org/10.1016/j.powtec.2010.09.030.
- Ayachit, U. 2015. The ParaView guide: A parallel visualization application. ParaView 4.3. New York: Kitware, Incorporated.
- Benvenuti, L., C. Kloss, and S. Pirker. 2016. Identification of DEM simulation parameters by artificial neural networks and bulk experiments. Powder Technology 291:456–65. doi: https://doi.org/10.1016/j.powtec.2016.01.003.
- Bidier, S., and W. Ehlers. 2013. Particle simulation of granular media and homogenisation towards continuum quantities. PAMM 13 (1):575–6. doi: https://doi.org/10.1002/pamm.201310269.
- Boac, J. M., R. P. K. Ambrose, M. E. Casada, R. G. Maghirang, and D. E. Maier. 2014. Applications of discrete element method in modeling of grain postharvest operations. Food Engineering Reviews 6 (4):128–49. doi: https://doi.org/10.1007/s12393-014-9090-y.
- Boac, J. M., M. E. Casada, R. G. Maghirang, and J. P. III Harner. 2010. Material and interaction properties of selected grains and oilseeds for modeling discrete particles. Transactions of the ASABE 53 (4):1201–16.
- Chen, J., M. Furuichi, and D. Nishiura. 2020. Discrete element simulation and validation of a mixing process of granular materials. Materials 13 (5):1208. doi: https://doi.org/10.3390/ma13051208.
- Coetzee, C. J. 2016. Calibration of the discrete element method and the effect of particle shape. Powder Technology 297 (September):50–70. doi: https://doi.org/10.1016/j.powtec.2016.04.003.
- Cuong, N. T., B. H. Ha, and R. Fukagawa. 2015. Failure mechanism of two-dimensional granular columns: Numerical simulation and experiments. Vietnam Journal of Mechanics 37 (4):239–50. doi: https://doi.org/10.15625/0866-7136/37/4/5844.
- Deng, Z., P. B. Umbanhowar, J. M. Ottino, and R. M. Lueptow. 2018. Continuum modelling of segregating tridisperse granular chute flow. Proceedings. Mathematical, Physical, and Engineering Sciences 474 (2211):20170384. doi: https://doi.org/10.1098/rspa.2017.0384.
- Ding, S., L. Bai, Y. Yao, B. Yue, Z. Fu, Z. Zheng, and Y. Huang. 2018. Discrete element modelling (DEM) of fertilizer dual-banding with adjustable rates. Computers and Electronics in Agriculture 152 (September):32–9. doi: https://doi.org/10.1016/j.compag.2018.06.044.
- Dunatunga, S., and K. Kamrin. 2015. Continuum modelling and simulation of granular flows through their many phases. Journal of Fluid Mechanics 779 (September):483–513. doi: https://doi.org/10.1017/jfm.2015.383.
- Fu, H., C. Jin, and J. Yu. 2017. The DEM-based digital design platform for agricultural machinery—AgriDEM. In Proceedings of the 7th international conference on discrete element methods, edited by Xikui Li, Yuntian Feng, and Graham Mustoe, 1253–63. Springer Proceedings in Physics. Singapore: Springer. doi: https://doi.org/10.1007/978-981-10-1926-5_129.
- Gan, Y., and M. Kamlah. 2010. Discrete element modelling of pebble beds: With application to uniaxial compression tests of ceramic breeder pebble beds. Journal of the Mechanics and Physics of Solids 58 (2):129–44. doi: https://doi.org/10.1016/j.jmps.2009.10.009.
- Ghaboussi, J., and R. Barbosa. 1990. Three-dimensional discrete element method for granular materials. International Journal for Numerical and Analytical Methods in Geomechanics 14 (7):451–72. doi: https://doi.org/10.1002/nag.1610140702.
- Ghodki, B. M., M. Patel, R. Namdeo, and G. Carpenter. 2019. Calibration of discrete element model parameters: Soybeans. Computational Particle Mechanics 6 (1):3–10. doi: https://doi.org/10.1007/s40571-018-0194-7.
- Horabik, J., and M. Molenda. 2016. Parameters and contact models for DEM simulations of agricultural granular materials: A review. Biosystems Engineering 147:206–25. doi: https://doi.org/10.1016/j.biosystemseng.2016.02.017.
- Hanley, J. K., and C. O’Sullivan. 2017. Analytical study of the accuracy of discrete element simulations. International Journal for Numerical Methods in Engineering 109 (1):29–51. doi: https://doi.org/10.1002/nme.5275.
- Jin, X., Q. Li, K. Zhao, B. Zhao, Z. He, and Z. Qiu. 2019. Development and test of an electric precision seeder for small-size vegetable seeds. International Journal of Agricultural and Biological Engineering 12 (2):75–81. doi: https://doi.org/10.25165/ijabe.v12i2.4618.
- Kaliniewicz, Z., and Z. Żuk. 2017. A Relationship between friction plate roughness and the external friction angle of wheat kernels. International Journal of Food Properties 20 (sup3):S2409–S17. doi: https://doi.org/10.1080/10942912.2017.1371190.
- Kibar, H., and T. Öztürk. n.d. Physical and mechanical properties of soybean. International Agrophysics 22 (3):239–44.
- Kloss, C., C. Goniva, A. Hager, S. Amberger, and S. Pirker. 2012. Models, algorithms and validation for opensource DEM and CFD–DEM. Progress in Computational Fluid Dynamics, an International Journal 12 (2/3):140–52. doi: https://doi.org/10.1504/PCFD.2012.047457.
- Kruggel-Emden, H., M. Sturm, S. Wirtz, and V. Scherer. 2008. Selection of an appropriate time integration scheme for the discrete element method (DEM). Computers & Chemical Engineering 32 (10):2263–79. doi: https://doi.org/10.1016/j.compchemeng.2007.11.002.
- Kruggel-Emden, H., S. Rickelt, S. Wirtz, and V. Scherer. 2008. A study on the validity of the multi-sphere discrete element method. Powder Technology 188 (2):153–65. doi: https://doi.org/10.1016/j.powtec.2008.04.037.
- Kruggel-Emden, H., E. Simsek, S. Rickelt, S. Wirtz, and V. Scherer. 2007. Review and extension of normal force models for the discrete element method. Powder Technology 171 (3):157–73. doi: https://doi.org/10.1016/j.powtec.2006.10.004.
- Le, T.-T. 2020. Practical machine learning-based prediction model for axial capacity of square CFST columns. Mechanics of Advanced Materials and Structures 0 (0):1–16. doi: https://doi.org/10.1080/15376494.2020.1839608.
- Le, T.-T., and M. V. Le. 2021. Nanoscale effect investigation for effective bulk modulus of particulate polymer nanocomposites using micromechanical framework. Advances in Materials Science and Engineering 2021 (March):e1563845–13. doi: https://doi.org/10.1155/2021/1563845.
- Le, L. M., T. C. Nguyen, B. T. Pham, H.-B. Ly, V. M. Le, and T.-T. Le. 2019. Development and identification of working parameters for a lychee peeling machine combining rollers and a pressing belt. AgriEngineering 1 (4):550–66. doi: https://doi.org/10.3390/agriengineering1040040.
- Le, T.-T., D. Miclet, P. Heritier, E. Piron, A. Chateauneuf, and M. Berducat. 2018. Morphology characterization of irregular particles using image analysis. Application to solid inorganic fertilizers. Computers and Electronics in Agriculture 147 (April):146–57. doi: https://doi.org/10.1016/j.compag.2018.02.022.
- Le, T.-T. 2015. Modélisation Stochastique, En Mécanique Des Milieux Continus, de l’interphase Inclusion-Matrice à Partir de Simulations En Dynamique Moléculaire. PhD thesis, Paris, France: University of Paris-Est Marne-la-Vallée. http://www.theses.fr/2015PESC1172.
- Liu, J., A. Wautier, S. Bonelli, F. Nicot, and F. Darve. 2020. Macroscopic softening in granular materials from a mesoscale perspective. International Journal of Solids and Structures 193–194 (June):222–38. doi: https://doi.org/10.1016/j.ijsolstr.2020.02.022.
- Lommen, S., D. Schott, and G. Lodewijks. 2014. DEM speedup: Stiffness effects on behavior of bulk material. Particuology, Special Issue on Conveying and Handling of Particulate Solids – Challenges of Discrete Element Simulation, Application and Calibration 12 (February):107–12. doi: https://doi.org/10.1016/j.partic.2013.03.006.
- Lu, M. M. G. R., and G. R. McDowell. 2006. The importance of modelling ballast particle shape in the discrete element method. Granular Matter. 9 (1-2):69–80. doi: https://doi.org/10.1007/s10035-006-0021-3.
- Malone, K. F., and B. H. Xu. 2008. Determination of contact parameters for discrete element method simulations of granular systems. Particuology 6 (6):521–8. doi: https://doi.org/10.1016/j.partic.2008.07.012.
- Maw, N., J. R. Barber, and J. N. Fawcett. 1976. The oblique impact of elastic spheres. Wear 38 (1):101–14. doi: https://doi.org/10.1016/0043-1648(76)90201-5.
- Mishra, B. K., and R. K. Rajamani. 1992. The discrete element method for the simulation of ball mills. Applied Mathematical Modelling 16 (11):598–604. doi: https://doi.org/10.1016/0307-904X(92)90035-2.
- Nezami, E. G., Y. M. Hashash, D. Zhao, and J. Ghaboussi. 2004. A fast contact detection algorithm for 3-D discrete element method. Computers and Geotechnics 31 (7):575–87. doi: https://doi.org/10.1016/j.compgeo.2004.08.002.
- Nguyen, T. C., L. M. Le, H.-B. Ly, and T.-T. Le. 2020. Numerical investigation of force transmission in granular media using discrete element method. Vietnam Journal of Mechanics 42 (2):153–71. doi: https://doi.org/10.15625/0866-7136/14787.
- Nguyen, T. X., L. M. Le, T. C. Nguyen, N. T. H. Nguyen, T.-T. Le, B. T. Pham, V. M. Le, and H.-B. Ly. 2021. Characterization of soybeans and calibration of their DEM input parameters. Particulate Science and Technology 39 (5):530–48. doi: https://doi.org/10.1080/02726351.2020.1775739.
- Parafiniuk, P., M. Molenda, and J. Horabik. 2014. Influence of particle shape and sample width on uniaxial compression of assembly of prolate spheroids examined by discrete element method. Physica A: Statistical Mechanics and Its Applications 416 (December):279–89. doi: https://doi.org/10.1016/j.physa.2014.08.063.
- Peters, J. F., M. Muthuswamy, J. Wibowo, and A. Tordesillas. 2005. Characterization of force chains in granular material. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics 72 (4 Pt 1):041307. doi: https://doi.org/10.1103/PhysRevE.72.041307.
- Rougier, E., A. Munjiza, and N. W. M. John. 2004. Numerical comparison of some explicit time integration schemes used in DEM, FEM/DEM and molecular dynamics. International Journal for Numerical Methods in Engineering 61 (6):856–79. doi: https://doi.org/10.1002/nme.1092.
- Tamás, K., B. Földesi, J. Péter Rádics, I. J. Jóri, and L. Fenyvesi. 2015. A simulation model for determining the mechanical properties of rapeseed using the discrete element method. Periodica Polytechnica Civil Engineering 59 (4):575–82. doi: https://doi.org/10.3311/PPci.8173.
- Than, V. D., S. Khamseh, A. M. Tang, J.-M. Pereira, F. Chevoir, and J.-N. Roux. 2017. Basic mechanical properties of wet granular materials: A DEM study. Journal of Engineering Mechanics 143 (1):C4016001. doi: https://doi.org/10.1061/(ASCE)EM.1943-7889.0001043.
- The MathWorks. 2018. MATLAB. Natick, MA: The MathWorks.
- Tran, V. T., F.-V. Donzé, and P. Marin. 2011. A discrete element model of concrete under high triaxial loading. Cement and Concrete Composites 33 (9):936–48. doi: https://doi.org/10.1016/j.cemconcomp.2011.01.003.
- Wiącek, J., and M. Molenda. 2014. Effect of particle size distribution on micro- and macromechanical response of granular packings under compression. International Journal of Solids and Structures 51 (25-26):4189–95. doi: https://doi.org/10.1016/j.ijsolstr.2014.06.029.
- Woo, S. M., D. D. Uyeh, M. S. Sagong, and Y. S. Ha. 2017. Development of seeder for mixed planting of corn and soybeans. International Journal of Agricultural and Biological Engineering 10 (3):95–101. doi: https://doi.org/10.25165/ijabe.v10i3.2543.
- Xie, L., P. Jin, T.-C. Su, X. Li, and Z. Liang. 2020. Numerical simulation of uniaxial compression tests on layered rock specimens using the discrete element method. Computational Particle Mechanics 7 (4):753–62. December. doi: https://doi.org/10.1007/s40571-019-00307-3.
- Xu, Z., A. M. Tartakovsky, and W. Pan. 2012. Discrete-element model for the interaction between ocean waves and sea ice. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics 85 (1 Pt 2):016703. doi: https://doi.org/10.1103/PhysRevE.85.016703.
- Xu, T., J. Yu, Y. Yu, and Y. Wang. 2018. A modelling and verification approach for soybean seed particles using the discrete element method. Advanced Powder Technology 29 (12):3274–90. doi: https://doi.org/10.1016/j.apt.2018.09.006.
- Zhang, L., N. Gia Hien Nguyen, S. Lambert, F. Nicot, F. Prunier, and I. Djeran-Maigre. 2017. The role of force chains in granular materials: From statics to dynamics. European Journal of Environmental and Civil Engineering 21 (7–8):874–95. doi: https://doi.org/10.1080/19648189.2016.1194332.
- Zhang, Y., F. Jia, Y. Zeng, Y. Han, and Y. Xiao. 2018. DEM study in the critical height of flow mechanism transition in a conical silo. Powder Technology 331:98–106. doi: https://doi.org/10.1016/j.powtec.2018.03.024.
- Zhou, Y., H. Wang, B. Zhou, and J. Li. 2018. DEM-aided direct shear testing of granular sands incorporating realistic particle shape. Granular Matter 20 (3):55. doi: https://doi.org/10.1007/s10035-018-0828-8.