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DOI: 10.15507/2658-4123.26363.483-499

UDK 631.3:633.358:004.9:519.854

 

Parameters of the Digital Twin Model of Peas for Modeling Using the Discrete Element Method

 

Airat M. Mukhametdinov
Cand.Sci. (Eng.), Associate Professor, Head of the Laboratory Digital Twins and Design of Machines for Chemical and Biological Plant Protection, Bashkir State Agrarian University (34 50-Letiya Oktybrya St., 450001 Ufa, Russian Federation), ORCID: https://orcid.org/0000-0002-3802-8151, Researcher ID: G-3461-2018, Scopus ID: 57204634851, SPIN-code: 8297-3621, This email address is being protected from spambots. You need JavaScript enabled to view it.

Salavat G. Mudarisov
Dr.Sci. (Eng.), Professor, Academician of the Academy of Sciences of the Republic of Bashkortostan, Leading Researcher at the Laboratory Digital Twins and Design of Machines for Chemical and Biological Plant Protection, Bashkir State Agrarian University (34 50-Letiya Oktybrya St., 450001 Ufa, Russian Federation), ORCID: https://orcid.org/0000-0001-9344-2606, Researcher ID: G-2217-2018, Scopus ID: 57200284613, SPIN-code: 6893-9957, This email address is being protected from spambots. You need JavaScript enabled to view it.

Ilnur R. Miftakhov
Cand.Sci. (Eng.), Junior Researcher at the Laboratory of Digital Twins and Design of Machines for Chemical and Biological Plant Protection, Bashkir State Agrarian University (34 50-Letiya Oktybrya St., 450001 Ufa, Russian Federation), ORCID: https://orcid.org/0000-0002-3125-3532, Scopus ID: 57204635364, SPIN-code: 9429-5990, This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Abstract
Introduction. In the context of developing energy-saving technologies and machines based on digital twins, the use of the discrete element method for modeling the technological process of sowing is the most in-demand area. The key advantage of DEM modeling is the ability to minimize the cost of creating new technology through virtual testing and optimization. The quality of the sowing machines is known to directly affect the uniformity of sowing and, ultimately, the yield. Therefore, the creation of adequate digital twins taking into account the physical and mechanical properties of real seeds is an important scientific and practical task. To model the process of sowing peas, it is necessary to solve two main tasks: to select an adequate contact model and calibrate its parameters that is the main aim of this work.
Aim of the Study. The study is aimed at determining the parameters of the contact model of a digital twin of pea seeds for modeling using the discrete element method.
Materials and Methods. The object of the study is the calibration of the parameters of the Linear Spring Dashpot contact model applied to pea seeds. The stages of the study include: identifying the limitations of calibration methods when modeling seed movement; determining the effect of moisture content and fractional composition of pea seeds on friction coefficients; developing a method for calibrating the contact model taking into account force impact in conditions close to the sowing machine operation; verifying models by comparing DEM modeling with data from field experiments in a coil apparatus.
Results. There has been determined the fractional composition of pea seeds showing pronounced polydispersity of the material. The dominant fraction (83.4%) has a diameter of 6.0–7.0 mm, which has been taken into account when parameterizing the DEM model in the Rocky DEM software package to create a representative digital twin. The significant influence of seed moisture content on their friction properties has been experimentally proven. With an increase in moisture content from 9% to 23%, the coefficient of dynamic friction increases that leads to a regular increase in the torque on the sowing machine shaft from 0.3 to 1.0 N·m. There has been has been developed and tested a comprehensive calibration method using the torque on the shaft of the sowing machine as a compliance criterion. There has been defined the range of the main parameters of the Linear spring dashpot contact model, ensuring high modeling accuracy: the coefficient of dynamic friction between seeds kd = 0.14...0.16; the coefficient of dynamic friction of seeds with the walls of the device kd.k = 0.07...0.09. Based on the results of the experiments, there has been developed a nomogram for selecting the dynamic friction coefficient depending on the moisture content of pea seeds.
Conclusion. The use of the proposed calibration method based on torque allows the creation of high-precision digital twins suitable for optimizing the design and technological parameters of sowing machines that contributes to improving the quality of sowing and saving resources in agricultural production.

Keywords: contact model, torque, dynamic friction coefficient, fractional composition, pea seeds, digital twin, discrete element modeling

Funding: The work was carried out with the support of a grant from the Russian Science Foundation № 23-76-10070 (https://rscf.ru/project/23-76-10070).

Conflict of interest: The authors declare that there is no conflict of interest.

For citation: Mukhametdinov A.M., Mudarisov S.G., Miftakhov I.R. Parameters of the Digital Twin Model of Peas for Modeling Using the Discrete Element Method. Engineering Technologies and Systems. 2026;36(3):483–499. https://doi.org/10.15507/2658-4123.26363.483-499

Authors contribution:
A. M. Mukhametdinov – preparation, creation and preparation of the published work, specifically visualization.br /> S. G. Mudarisov – ideas; formulation or evolution of overarching the goals and aims.br /> I. R. Miftakhov – presentation, creation and preparation of the published work, specifically writing the initial draft (including substantive translation).

All authors have read and approved the final manuscript.

Submitted 17.11.2025;
revised 04.03.2026;
accepted 05.03.2026

 

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