https://doi.org/10.4081/jae.2022.1286
Modelling and verification of sesame seed particles using the discrete element method
Published: 28 June 2022
Abstract Views: 1877
PDF: 498
HTML: 86
HTML: 86
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
The size of sesame seed particles has been measured and analysed to build a sesame seed particle model using the discrete element method (DEM). Despite the strength of simulations using the DEM method, one of the challenges that still require to be overcome is approximating the form of the actual particles, especially for irregular shapes, to obtain more realistic simulations. Thus, the sesame seed particle was simplified to be quite close to the actual seed forms by drawing an irregular 3D sesame particle model using Fusion 360 software with the average dimensions of five hundred randomly selected sesame seeds. Consequently, a modelling approach for sesame seed particles based on a multi-sphere (MS) method was suggested. In this paper, the simulated results of the sesame particle model were close to those obtained experimentally, with 28 filling spheres. The results for both piling tests and oscillating seed meter calibration have shown that the 28- sphere model is appropriate for modelling the sesame seed particle. Thus, the validity and feasibility of the modelling approach for sesame seed particles we proposed have been verified. Finally, the simulation analysis provided a good prediction for the outflow process of sesame seeds from the oscillating seed meter. The optimum values for the main parameters of the oscillating seed metering device for sowing sesame seeds are 9 mm for seed exit hole clearance, 20° for oscillation angle, and 0.022 sec for opening time, providing a sesame seed rate of 2.7 kg/ha. As a result, it provides a reference for the design and optimisation of oscillating seed meter for sowing sesame seeds.
Boac J.M., Ambrose R.P.K., Casada M.E., Maghirang R.G., Maier D.E. 2014. Applications of discrete element method in modeling of grain postharvest operations. Food Eng. Rev. 6:128-49.
DOI: https://doi.org/10.1007/s12393-014-9090-y
Boac J.M., Casada M.E., Maghirang R.G., Harner J.P. 2010. Material and interaction properties of selected grains and oilseeds for modeling discrete particles. Trans. ASABE 53:1201-16.
DOI: https://doi.org/10.13031/2013.32577
Borchani C., Besbes S., Blecker C.H., Attia H. 2010. Chemical characteristics and oxidative stability of sesame seed, sesame paste, and olive oils. J. Agric. Sci. Technol. 12:585-96.
Chen Z., Yu J., Xue D., Wang Y., Zhang Q., Ren L. 2018. An approach to and validation of maize-seed-assembly modelling based on the discrete element method. Powder Technol. 328:167-83.
DOI: https://doi.org/10.1016/j.powtec.2017.12.007
Cundall P.A., Strack O.D.L. 1979. A discrete numerical model for granular assemblies. Geotechnique 29:47-65.
DOI: https://doi.org/10.1680/geot.1979.29.1.47
Guo Y., Wassgren C., Ketterhagen W., Hancock B., Curtis J. 2012. Some computational considerations associated with discrete element modeling of cylindrical particles. Powder Technol. 228:193-8.
DOI: https://doi.org/10.1016/j.powtec.2012.05.015
Härtl J., Ooi J. Y. 2008. Experiments and simulations of direct shear tests: porosity, contact friction and bulk friction. Granul. Matter 10:263-71.
DOI: https://doi.org/10.1007/s10035-008-0085-3
Hongxin L., Lifeng G., Lulu F., Shifa T. 2015. Study on multi-size seed-metering device for vertical plate soybean precision planter. Int. J. Agric. Biol. Eng. 8:1-8.
Horabik J., Molenda M. 2016. Parameters and contact models for DEM simulations of agricultural granular materials: A review. Biosyst. Eng. 147:206-25.
DOI: https://doi.org/10.1016/j.biosystemseng.2016.02.017
Jia H., Chen Y., Zhao J., Guo M., Huang D., Zhuang J. 2018. Design and key parameter optimization of an agitated soybean seed metering device with horizontal seed filling. Int. J. Agric. Biol. Eng. 11:76-87.
DOI: https://doi.org/10.25165/j.ijabe.20181102.3464
Johnston L.J., Goihl J., Shurson G.C. 2009. Selected additives did not improve flowability of DDGS in commercial systems. Appl. Eng. Agric. 25:75-82.
DOI: https://doi.org/10.13031/2013.25422
Khatchatourian O. A., Binelo M. O., de Lima R. F. 2014. Simulation of soya bean flow in mixed-flow dryers using DEM. Biosyst. Eng. 123:68-76.
DOI: https://doi.org/10.1016/j.biosystemseng.2014.05.003
Kruggel-Emden H., Rickelt S., Wirtz S., Scherer V. 2008. A study on the validity of the multi-sphere Discrete Element Method. Powder Technol. 188:153–65.
DOI: https://doi.org/10.1016/j.powtec.2008.04.037
Leblicq T., Smeets B., Ramon H., Saeys W. 2016. A discrete element approach for modelling the compression of crop stems. Comput. Electron. Agric. 123:80-8.
DOI: https://doi.org/10.1016/j.compag.2016.02.018
Lei X., Liao Y., Liao Q. 2016. Simulation of seed motion in seed feeding device with DEM-CFD coupling approach for rapeseed and wheat. Comput. Electron. Agric. 131:29-39.
DOI: https://doi.org/10.1016/j.compag.2016.11.006
Li Y., Xiantao H., Tao C., Dongxing Z., Song S., Zhang R., Mantao W. 2015. Development of mechatronic driving system for seed meters equipped on conventional precision corn planter. Int. J. Agric. Biol. Eng. 8:1-9.
Lin X., Ng T. 1995. Contact detection algorithms for three‐dimensional ellipsoids in discrete element modelling. Int. J. Numer. Anal. Methods Geomech. 19:653-9.
DOI: https://doi.org/10.1002/nag.1610190905
Markauskas D., Kačianauskas R. 2011. Investigation of rice grain flow by multi-sphere particle model with rolling resistance. Granul. Matter 13:143-8.
DOI: https://doi.org/10.1007/s10035-010-0196-5
Markauskas D., Ramírez-Gómez Á., Kačianauskas R., Zdancevičius E. 2015. Maize grain shape approaches for DEM modelling. Comput. Electron. Agric. 118:247-58.
DOI: https://doi.org/10.1016/j.compag.2015.09.004
Noorka I.R., Hafiz S.I., El-Bramawy M.A.S. 2011. Response of sesame to population densities and nitrogen fertilization on newly reclaimed sandy soils. Pak. J. Bot 43:1953-8.
Ouadfel H., Rothenburg L. 1999. An algorithm for detecting inter-ellipsoid contacts. Comput. Geotech. 24:245-63.
DOI: https://doi.org/10.1016/S0266-352X(99)00013-0
Radvilaitė U., Ramírez-Gómez Á., Kačianauskas R. 2016. Determining the shape of agricultural materials using spherical harmonics. Comput. Electron. Agric. 128:160-71.
DOI: https://doi.org/10.1016/j.compag.2016.09.003
Ramírez A., Nielsen J., Ayuga F. 2010. On the use of plate-type normal pressure cells in silos: Part 2: Validation for pressure measurements. Comput. Electron. Agric. 71:64-70.
DOI: https://doi.org/10.1016/j.compag.2009.12.005
Ren B., Zhong W., Chen Y., Chen X., Jin B., Yuan Z., Lu Y. 2012. CFD-DEM simulation of spouting of corn-shaped particles. Particuology 10:562-72.
DOI: https://doi.org/10.1016/j.partic.2012.03.011
Sharaby N., Butovchenko A. 2019. Cultivation technology of sesame seeds and its production in the world and in Egypt. IOP Conf. Ser. Earth Environ. Sci. 403:012093.
DOI: https://doi.org/10.1088/1755-1315/403/1/012093
Sharaby N., Doroshenko A., Butovchenko A. 2020. Simulation of Sesame Seeds Outflow in Oscillating Seed Metering Device Using DEM. Eng. Technol. Syst. 30:219-31.
DOI: https://doi.org/10.15507/2658-4123.030.202002.219-231
Soltanbeigi B., Podlozhnyuk A., Kloss C., Pirker, S. 2021. Influence of various DEM shape representation methods on packing and shearing of granular assemblies. Granul. Matter 23:1-16.
DOI: https://doi.org/10.1007/s10035-020-01078-y
Wang X., Yu J., Lv F., Wang Y., Fu H. 2017. A multi-sphere based modelling method for maize grain assemblies. Adv. Powder Technol. 28:584-95.
DOI: https://doi.org/10.1016/j.apt.2016.10.027
Weigler F., Mellmann, J. 2014. Investigation of grain mass flow in a mixed flow dryer. Particuology 12: 33-9.
DOI: https://doi.org/10.1016/j.partic.2013.04.004
Xu T., Yu J., Yu Y., Wang Y. 2018. A modelling and verification approach for soybean seed particles using the discrete element method. Adv. Powder Technol. 29:3274-90.
DOI: https://doi.org/10.1016/j.apt.2018.09.006
Zhou Y.C., Wright B.D., Yang R.Y., Xu B.H., Yu A.-B. 1999. Rolling friction in the dynamic simulation of sandpile formation. Phys. A Stat. Mech. Its Appl. 269:536-53.
DOI: https://doi.org/10.1016/S0378-4371(99)00183-1
How to Cite
Sharaby, N., Doroshenko, A. and Butovchenko, A. . (2022) “Modelling and verification of sesame seed particles using the discrete element method”, Journal of Agricultural Engineering, 53(2). doi: 10.4081/jae.2022.1286.
Copyright (c) 2022 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Alessandro Parenti, Lorenzo Guerrini, Piernicola Masella, Riccardo Dainelli, Paolo Spugnoli, A new method for Espresso Coffee brewing: Caffè Firenze , Journal of Agricultural Engineering: Vol. 44 No. s2 (2013): Proceedings of the 10th Conference of the Italian Society of Agricultural Engineering
- Costanza Di Stefano, Vito Ferro, Establishing soil loss tolerance: an overview , Journal of Agricultural Engineering: Vol. 47 No. 3 (2016)
- Simone Pascuzzi, The effects of the forward speed and air volume of an air-assisted sprayer on spray deposition in tendone trained vineyards , Journal of Agricultural Engineering: Vol. 44 No. 3 (2013)
- Roberto Deboli, Angela Calvo, Christian Preti, Comparison between ISO 5008 and field whole body vibration tractor values , Journal of Agricultural Engineering: Vol. 43 No. 2 (2012)
- Yun Zhu, Shuwen Liu, Xiaojun Wu, Lianfeng Gao, Youyun Xu, Multi-class segmentation of navel orange surface defects based on improved DeepLabv3+ , Journal of Agricultural Engineering: Vol. 55 No. 2 (2024)
- Lorenzo Picco, Luca Mao, Emanuel Rigon, Johnny Moretto, Diego Ravazzolo, Fabio Delai, Mario Aristide Lenzi, An update of the sediment fluxes investigation in the Rio Cordon (Italy) after 25 years of monitoring , Journal of Agricultural Engineering: Vol. 43 No. 3 (2012)
- İlker Ünal, Önder Kabaş, Salih Sözer, Comparison of two different artificial neural network models for prediction of soil penetration resistance , Journal of Agricultural Engineering: Vol. 55 No. 1 (2024)
- Rodolfo Piscopia, Andrea Petroselli, Salvatore Grimaldi, A software package for predicting design-flood hydrographs in small and ungauged basins , Journal of Agricultural Engineering: Vol. 46 No. 2 (2015)
- Daniel Veal, Lutz Damerow, Peter Schulze Lammers, Michael Blanke, Kinematic model for mechanical apple blossom thinning , Journal of Agricultural Engineering: Vol. 55 No. 1 (2024)
- Lorenzo Vergni, Alessandra Vinci, Francesca Todisco, Francesco Saverio Santaga, Marco Vizzari, Comparing Sentinel-1, Sentinel-2, and Landsat-8 data in the early recognition of irrigated areas in central Italy , Journal of Agricultural Engineering: Vol. 52 No. 4 (2021)
<< < 14 15 16 17 18 19 20 21 22 23 > >>
You may also start an advanced similarity search for this article.
