DOI: https://doi.org/10.32515/2664-262X.2024.10(41).2.123-130

Investigation of the Corn Kernel Destruction Process Using Digital Models

Volodymyr Dudin, Illia Bilous

About the Authors

Volodymyr Dudin, Associate Professor, PhD in Technics (Candidate of Technics Sciences), Dnipro State Agrarian and Economic University, Dnipro, Ukraine, e-mail: dudin.v.yu@dsau.dp.ua, ORCID ID: 0000-0002-1414-7690

Illia Bilous, PhD student, Dnipro State Agrarian and Economic University, Dnipro, Ukraine, bilous.i.m@dsau.dp.ua

Abstract

In modern mechanical engineering, Model-Based Systems Engineering (MBSE) has become widely adopted. One of its key concepts is the creation and testing of virtual models of the designed technical systems. Compared to traditional approaches, this method significantly reduces resources and development time for new machines or improvements to existing ones. However, the accuracy of modeling entirely depends on the precision of the input data used for simulation. This also applies to the process of feed material destruction, particularly corn kernels. To obtain reliable simulation results, it is necessary to create a model of a kernel that closely resembles the real one and assign it physical and mechanical parameters that most accurately correspond to the studied sample. The aim of this research is to create an adequate virtual model of a corn kernel in the Simcenter STAR-CCM+ environment by comparing the destruction force obtained virtually and in laboratory experiments. Based on measurements and a review of studies in the Simcenter STAR-CCM+ software, a discrete element model (DEM) was developed using the aggregation of 86 spheres, employing the Hertz-Mindlin contact model. A virtual full-factorial experiment was conducted, resulting in regression equations describing the dependence of the kernel destruction force on Poisson’s ratio (µp), Young’s modulus (Ep), and ultimate tensile and shear stress (Wp) under different loading directions. A laboratory test stand was developed and implemented, where experimental studies of the destruction force of corn kernels were conducted under conditions analogous to the virtual experiment. The comparison of results identified factor values at which the model matches the laboratory research findings: Young’s modulus Ep = 10 MPa, Poisson’s ratio µp = 0.48, and ultimate tensile and shear stress Wp = 2.4 MPa. The obtained results allow for refining the input parameters in corn kernel simulations, ensuring more accurate virtual studies of the destruction process under various interactions with working elements.

Keywords

corn grain, modeling, compression, fracture force

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References

1. Shevchenko, N., 2020: An Introduction to Model-Based Systems Engineering (MBSE). Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed January 28, 2025, https://doi.org/10.58012/d464-qf49.

2. Arendarenko, V. M., & Samoilenko, T. V. (2018). The mathematical modeling of the loading process of grain to the silos. Scientific Progress & Innovations, (2), 158–161. https://doi.org/10.31210/visnyk2018.02.26

3. Kubík, Ľ., Božiková, M. & Kažimírová, V. Mechanical Properties of Wheat Grains at Compression. Acta Technologica Agriculturae, 2021, Slovak University of Agriculture in Nitra, vol. 24 no. 4, pp. 202-208. https://doi.org/10.2478/ata-2021-0033

4. Chen, Z., Wassgren, C., Tamrakar, A., & Ambrose, R. P. K. (2023). Validation of a DEM Model for predicting grain damage in an industrial-scale handling system. Smart Agricultural Technology, 5, 100274. https://doi.org/10.1016/j.atech.2023.100274

5. Aliiev Elchyn. Numerical simulation of agricultural production processes: textbook. Kyiv: Agrarna nauka, 2023. 340 p.

6. Zhong, J., Tao, L., Li, S., Zhang, B., Wang, J., & He, Y. (2022). Determination and interpretation of parameters of double-bud sugarcane model based on discrete element. Computers and Electronics in Agriculture, 203, 107428. https://doi.org/10.1016/j.compag.2022.107428

7. Liu, Z., Ma, H., & Zhao, Y. (2021). Comparative study of discrete element modeling of tablets using multi-spheres, multi-super-ellipsoids, and polyhedrons. Powder Technology, 390, 34–49. https://doi.org/10.1016/j.powtec.2021.05.065

8. Kruggel‐Emden, H., Rickelt, S., Wirtz, S., & Scherer, V. (2008). A study on the validity of the multi-sphere Discrete Element Method. Powder Technology, 188(2). P. 153–165. https://doi.org/10.1016/j.powtec.2008.04.037

9. Wiącek, J., Molenda, M., Horabik, J., & Ooi, J. Y. (2012). Influence of grain shape and intergranular friction on material behavior in uniaxial compression: Experimental and DEM modeling. Powder Technology, 217, 435–442. https://doi.org/10.1016/j.powtec.2011.10.060

10. Markauskas, D., & Kačianauskas, R. (2010). Investigation of rice grain flow by multi-sphere particle model with rolling resistance. Granular Matter, 13(2). P. 143–148. https://doi.org/10.1007/s10035-010-0196-5

11. 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. Transactions of the ASABE, 53(4), 1201–1216. https://doi.org/10.13031/2013.32577

12. Weigler, F., & Mellmann, J. (2014). Investigation of grain mass flow in a mixed flow dryer. Particuology, 12, 33–39. https://doi.org/10.1016/j.partic.2013.04.004

13. Coetzee, C. (2017). Review: Calibration of the discrete element method. Powder Technology, 310, 104–142. https://doi.org/10.1016/j.powtec.2017.01.015

14. Wang, L., Zhou, W., Ding, Z., Li, X., & Zhang, C. (2015). Experimental determination of parameter effects on the coefficient of restitution of differently shaped maize in three-dimensions. Powder Technology, 284. P. 187–194. https://doi.org/10.1016/j.powtec.2015.06.042

15. Markauskas, D., Ramírez-Gómez, Á., Kačianauskas, R., & Zdancevičius, E. (2015). Maize grain shape approaches for DEM modelling. Computers and Electronics in Agriculture, 118. P. 247–258. https://doi.org/10.1016/j.compag.2015.09.004

16. Li, X., Liu, F., Zhao, M., Zhang, T., Li, F., & Zhang, Y. Determination of contact parameters of maize seed and seed metering device. (2018). Agric. Mech. Res., 40. P. 149–153.

Citations

1. Shevchenko, N., 2020: An Introduction to Model-Based Systems Engineering (MBSE). Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed January 28, 2025, https://doi.org/10.58012/d464-qf49.

2. Арендаренко, В. М. Самойленко, Т. В. Математичне моделювання процесу завантаження силосів зерном. Вісник Полтавської Державної Аграрної Академії, 2018. (2), 158-161. https://doi.org/10.31210/visnyk2018.02.26

3. Kubík, Ľ., Božiková, M., Kažimírová, V. Mechanical Properties of Wheat Grains at Compression. Acta Technologica Agriculturae, 2021, Slovak University of Agriculture in Nitra, vol. 24 no. 4, pp. 202-208. https://doi.org/10.2478/ata-2021-0033

4. Chen, Z., Wassgren, C., Tamrakar, A., Ambrose, R. P. K. Validation of a DEM Model for predicting grain damage in an industrial-scale handling system. Smart Agricultural Technology, 2023. 5, 100274. https://doi.org/10.1016/j.atech.2023.100274

5. Алієв Е. Б. Чисельне моделювання процесів агропромислового виробництва: підручник. Київ: Аграрна наука, 2023. 340 с.

6. Zhong, J., Tao, L., Li, S., Zhang, B., Wang, J., & He, Y. Determination and interpretation of parameters of double-bud sugarcane model based on discrete element. Computers and Electronics in Agriculture, 2022. 203, 107428. https://doi.org/10.1016/j.compag.2022.107428

7. Liu, Z., Ma, H., Zhao, Y. Comparative study of discrete element modeling of tablets using multi-spheres, multi-super-ellipsoids, and polyhedrons. Powder Technology, 2021. 390, 34–49. https://doi.org/10.1016/j.powtec.2021.05.065

8. Kruggel‐Emden, H., Rickelt, S., Wirtz, S., Scherer, V. A study on the validity of the multi-sphere Discrete Element Method. Powder Technology, 2008. 188(2). P. 153–165. https://doi.org/10.1016/j.powtec.2008.04.037

9. Wiącek, J., Molenda, M., Horabik, J., Ooi, J. Y. Influence of grain shape and intergranular friction on material behavior in uniaxial compression: Experimental and DEM modeling. Powder Technology, 2012. 217, 435–442. https://doi.org/10.1016/j.powtec.2011.10.060

10. Markauskas, D., Kačianauskas, R. Investigation of rice grain flow by multi-sphere particle model with rolling resistance. Granular Matter, 2010. 13(2). P. 143–148. https://doi.org/10.1007/s10035-010-0196-5

11. Boac, J. M., Casada, M. E., Maghirang, R. G., Harner, J. P. Material and interaction properties of selected grains and oilseeds for modeling discrete particles. Transactions of the ASABE, 2010. 53(4), 1201–1216. https://doi.org/10.13031/2013.32577

12. Weigler, F., Mellmann, J. Investigation of grain mass flow in a mixed flow dryer. Particuology, 2014. 12. P. 33–39. https://doi.org/10.1016/j.partic.2013.04.004

13. Coetzee, C. Review: Calibration of the discrete element method. Powder Technology, 2017. 310, 104–142. https://doi.org/10.1016/j.powtec.2017.01.015

14. Wang, L., Zhou, W., Ding, Z., Li, X., Zhang, C. Experimental determination of parameter effects on the coefficient of restitution of differently shaped maize in three-dimensions. Powder Technology, 2015. 284. P. 187–194. https://doi.org/10.1016/j.powtec.2015.06.042

15. Markauskas, D., Ramírez-Gómez, Á., Kačianauskas, R., Zdancevičius, E. Maize grain shape approaches for DEM modelling. Computers and Electronics in Agriculture, 2015. 118. P. 247–258. https://doi.org/10.1016/j.compag.2015.09.004

16. Li, X., Liu, F., Zhao, M., Zhang, T., Li, F., Zhang, Y. Determination of contact parameters of maize seed and seed metering device. (2018). Agric. Mech. Res., 40. P. 149–153.

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