ASSESSMENT OF SIGNIFICANCE AND SELECTION OF CONTACT MODEL PARAMETERS WHEN SIMULATING PEAS SEEDS USING THE DISCRETE ELEMENT METHOD
DOI 10.12737/2073-0462-2024-54-60 УДК 631.3:004.942
Keywords:
modeling, discrete element method, calibration, angle of repose, contact model, pea seeds.Abstract
An integral part of technological operations in agriculture during sowing, harvesting, transportation and post-harvest processing is the impact of the working parts of machines on crop seeds. To increase the efficiency of machines and reduce seed injury, it is necessary to improve the design and technological parameters of the working units. Currently, the most promising method for theoretically substantiating the parameters of machines working with seeds and improving their design is modeling using the discrete element method. The study is carried out to substantiate the parameters of the contact model of discrete elements when modeling pea seeds. The parameters of the contact model were justified based on the results of testing on the angle of slope and arch when pouring seeds. To analyze the angle of repose and vault, a transparent rectangular container was used, when seeds were poured out of the upper part of which, the remaining seeds formed the angle of the vault, and the crumbled ones formed the angle of repose. Evaluation of the coefficients of the regression equations for changes in the angles of the arch and slope when shedding seeds in a container from the parameters of the selected contact linear viscoelastic model made it possible to establish a significant parameter - the coefficient of dynamic friction between particles. The nature of the change in the angle of repose of pea seeds from moisture is identical to the change in this angle for particles modeled by the discrete element method, with their recovery coefficient v = 0.2 and the coefficient of dynamic friction between them fd = 0.15...0.17. The dependences of the angle of repose on the moisture content of pea seeds and the coefficient of dynamic friction between particles obtained during the implementation of experiments represent a nomogram for choosing the coefficient of dynamic friction between particles fd for a contact linear viscoelastic model depending on the moisture content of pea seeds when modeling using the discrete element method.
References
1. Horabik J., Molenda M. Parameters and contact models for DEM simulations of agricultural granular materials: A review // Biosystems Engineering. 2016. No. 147. P. 206-225.
2. Validation and Calibration of Maize Seed–Soil Inter-Parameters Based on the Discrete Element Method / L. Zhou, Q. Dong, J. Yu, et al. // Agronomy. 2015. Vol. 13. No. 8. 2115. URL: https://www.researchgate.net/publication/373134767
3. Coetzee C. J. Particle upscaling: Calibration and validation of the discrete element method // Powder technology. 2019. No. 344. P. 487-503.
4. Parafiniuk P., Molenda M., Horabik J. 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. 2014. No. 416. P. 279-289.
5. Weir G., Tallon S. The coefficient of restitution for normal incident, low velocity particle impacts // Chemical Engineering Science. 2005. Vol. 60. No. 13. P. 3637-3647.
6. Determination of the dynamical behaviour of biological materials during impact using a pendulum device / M. Van Zeebroeck, E. Tijskens, P. Van Liedekerke, et al. // Journal of sound and vibration. 2003. Vol. 266. No. 3. P. 465-480.
7. Predicting discharge dynamics of wet cohesive particles from a rectangular hopper using the discrete element method (DEM) / A. Anand, J.S. Curtis, C.R. Wassgren, et al. // Chemical Engineering Science. 2009. Vol. 64. No. 24. P. 5268-5275.
8. Method of measurement of coefficient of friction between pairs of metallic and organic objects / J. Łukaszuk, M. Molenda, J. Horabik, et al. // Acta Agrophysica. 2009. Vol. 13. No. 2. P. 407-418.
9. Material and interaction properties of selected grains and oilseeds for modeling discrete particles / J.M. Boac, M.E. Casada, R.G. Maghirang, et al. // American Society of Agricultural and Biological Engineers. 2009. - С 1.
10. Liping Z., Zhang L., Zheng W. Fertilizer Feeding Mechanism and Experimental Study of a Spiral GroovedWheel Fertilizer Feeder // J Eng Sci Tech Review. 2018. Vol. 11. P. 107–115.
11. A modelling and verification approach for soybean seed particles using the discrete element method / T. Xu, J. Yu, Y. Yu et al. //Advanced Powder Technology. 2018. Vol. 29. No. 12. P. 3274-3290.
12. Sharaby N., Doroshenko A., Butovchenko A. Modelling and verification of sesame seed particles using the discrete element method //Journal of Agricultural Engineering. 2022. Vol. 53. No. 2. URL:https://www.agroengineering.org/index.php/jae/article/view/1286
13. Исследования угла естественного откоса компонентов зерносмеси / Н.П. Тишанинов, А.В. Анашкин, К.Н. Тишанинов и др. // Наука в центральной России. №5 (47). 2020. С 31-40
14. Fast and precise DEM parameter calibration for Cucurbita ficifolia seeds / X. Ding, B. Wang, Z. He, et al. // Biosystems Engineering. 2023. Vol. 236. P. 258-276.
15. Wang S., Yu Z., Zhang W. Study on the modeling method of sunflower seed particles based on the discrete element method // Computers and Electronics in Agriculture. 2022. Vol. 198. 107012. URL: https: //www. sciencedirect.com /science/article/pii/S0168169922003295
16. Сельскохозяйственные материалы (виды, состав, свойства) / Н.Г. Ковалев, Г.А. Хайлис, М.М. Ковалёв. М.: ИК «Родник», 1998. 208 с.
1. Horabik J, Molenda M. Parameters and contact models for DEM simulations of agricultural granular materials: a review. Biosystems Engineering. 2016; 147. 206-225 p.
2. Zhou L, Dong Q, Yu J, Wang Y, Chen Y, Li M, Wang W, Yu Y, Yuan J. Validation and calibration of maize seedsoil interparameters based on the discrete element method. [Internet]. Agronomy. 2015; 13(8). 2115. Available from: https://www.researchgate.net/publication/373134767
3. Coetzee CJ. Particle upscaling: calibration and validation of the discrete element method. Powder technology. 2019; Vol.344. 487-503 p.
4. Parafiniuk P, Molenda M, Horabik J. 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. 2014; Vol.416. 279-289 p.
5. Weir G, Tallon S. The coefficient of restitution for normal incident, low velocity particle impacts. Chemical Engineering Science. 2005; Vol.60. 13. 3637-3647 p.
6. Van Zeebroeck M, Tijskens E, Van Liedekerke P, Deli V, De Baerdemaeker J, Ramon H. Determination of the dynamical behaviour of biological materials during impact using a pendulum device. Journal of sound and vibration. 2003; 266(3), 465-480 p.
7. Anand A, Curtis JS, Wassgren CR, Hancock BC, Ketterhagen WR. Predicting discharge dynamics of wet cohesive particles from a rectangular hopper using the discrete element method (DEM). Chemical Engineering Science. 2009; 64 (24), 5268-5275 p.
8. Lukaszuk J, Molenda M, Horabik J, Wiacek J. Method of measurement of coefficient of friction between pairs of metallic and organic objects. Acta Agrophysica. 2009; 13(2). 407-418 p.
9. Boac JM, Casada ME, Maghirang RG, Harner JP. Material and interaction properties of selected grains and oilseeds for modeling discrete particles. American Society of Agricultural and Biological Engineers. 2009; June 21-June 24, 1 p.
10. Liping Z, Zhang L, Zheng W. Fertilizer feeding mechanism and experimental study of a spiral grooved-wheel fertilizer feeder. J Eng Sci Tech Review. 2018; 11. 107-115 p.
11. Xu T, Yu J, Yu Y, Wang Y. A modelling and verification approach for soybean seed particles using the discrete element method. Advanced Powder Technology. 2018; Vol.29. 12. 3274-3290 p.
12. Sharaby N, Doroshenko A, Butovchenko A. Modelling and verification of sesame seed particles using the discrete element method. Journal of Agricultural Engineering. 2022; Vol.53. 2. Available from: https://www.agroengineering.org/index.php/jae/article/view/1286
13. Tishaninov NP, Anashkin AV, Tishaninov KN. [Research of the angle of repose of grain mixture components]. Nauka v tsentralnoy Rossii. 2020; 5 (47). 31-40 p.
14. Ding X, Wang B, He Z, Shi Y, Li K, Cui Y, Yang Q. Fast and precise DEM parameter calibration for Cucurbita ficifolia seeds. Biosystems Engineering. 2023; 236. 258-276 p.
15. Wang S, Yu Z, Zhang W. Study on the modeling method of sunflower seed particles based on the discrete element method. Computers and Electronics in Agriculture. 2022; Vol.198. 107012 p. Available from: https: //www. sciencedirect.com /science/article/pii/S0168169922003295
16. Kovalev NG, Khailis GA, Kovalev MM. Selskokhozyaystvennye materialy (vidy, sostav, svoystva). [Agricultural materials (types, composition, properties)]. Moscow: IK “Rodnik”. 1998; 208 p.
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2026 Vestnik of Kazan state agrarian university
This work is licensed under a Creative Commons Attribution 4.0 International License.
Метаданные научных статей, включая название, аннотацию, сведения об авторах, список литературы, идентификаторы и иные элементы библиографического описания для дальнейшего неограниченного использования, передаются в редакцию с правом их неограниченного использования на условиях открытой лицензии Creative Commons Attribution (CC BY).
