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
- Lucio Brunetti, Ferruccio Giametta, Pasquale Catalano, Francesco Villani, Jonathan Fioralba, Flavio Fucci, Giovanna La Fianza, Energy consumption and analysis of industrial drying plants for fresh pasta process , Journal of Agricultural Engineering: Vol. 46 No. 4 (2015)
- Elio Dinuccio, Jacopo Maffia, Carla Lazzaroni, Gianfranco Airoldi, Paolo Balsari, Davide Biagini, Clinoptilolite (E567), a natural zeolite, inclusion in heavy-pig diets: effect on the productive performance and gaseous emissions during fattening and manure storage , Journal of Agricultural Engineering: Vol. 53 No. 1 (2022)
- Albert Min, Nam Nguyen, Liam Howatt, Marlowe Tavares, Jaho Seo, Aeroponic systems design: considerations and challenges , Journal of Agricultural Engineering: Vol. 54 No. 1 (2023)
- Alessandro Parenti, Piernicola Masella, Lorenzo Guerrini, Antonio Guiso, Paolo Spugnoli, Energetic and economic viability of olive stone recovery as a renewable energy source: a Southern Italy case study , Journal of Agricultural Engineering: Vol. 45 No. 2 (2014)
- Timothy Denen Akpenpuun, Qazeem Opeyemi Ogunlowo, Wook-Ho Na, Prabhat Dutta, Anis Rabiu, Misbaudeen Aderemi Adesanya, Mohammadreza Nariman, Ezatullah Zakir, Hyeon Tae Kim, Hyun-Woo Lee, Dynamic neural network modeling of thermal environments of two adjacent single-span greenhouses with different thermal curtain positions , Journal of Agricultural Engineering: Vol. 55 No. 2 (2024)
- Xuhui Chen, Shilin Zhang, Jianxin Dong, Fang Liu, Xian Jia, Yuxiang Huang, Development of high-speed and precision metering device with gradient-feeding and control seed for soybean planting , Journal of Agricultural Engineering: Vol. 55 No. 3 (2024)
- Jiangui Zhao, Tingyu Zhu, Zhichao Qiu, Tao Li, Guoliang Wang, Zhiwei Li, Huiling Du, Hyperspectral prediction of pigment content in tomato leaves based on logistic-optimized sparrow search algorithm and back propagation neural network , Journal of Agricultural Engineering: Vol. 54 No. 4 (2023)
- Emanuele Cerruto, Giuseppe Manetto, Giampaolo Schillaci, Vibration produced by hand-held olive electrical harvesters , Journal of Agricultural Engineering: Vol. 43 No. 2 (2012)
- Alessio Cislaghi, Gian Battista Bischetti, Best practices in post-flood surveys: The study case of Pioverna torrent , Journal of Agricultural Engineering: Vol. 53 No. 2 (2022)
- Marzia Quattrone, Giovanna Tomaselli, Alessandro D'Emilio, Patrizia Russo, Analysis and evaluation of abandoned railways aimed at greenway conversion: A methodological application in the Sicilian landscape using multi-criteria analysis and geographical information system , Journal of Agricultural Engineering: Vol. 49 No. 3 (2018)
<< < 15 16 17 18 19 20 21 22 23 24 > >>
You may also start an advanced similarity search for this article.
