Comprehensive Image-Based Validation Framework for Particle Motion in DEM Models Under Field-like Conditions

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41310%2F25%3A106246" target="_blank" >RIV/60460709:41310/25:106246 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3390/technologies13120570" target="_blank" >https://doi.org/10.3390/technologies13120570</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/technologies13120570" target="_blank" >10.3390/technologies13120570</a>

Result language

angličtina

Original language name

Comprehensive Image-Based Validation Framework for Particle Motion in DEM Models Under Field-like Conditions

Original language description

Accurate numerical prediction of particle-tool interaction requires validation methods that closely reflect the complexity of real operating conditions. This study introduces a comprehensive methodology for validating the motion of particulate material modeled using the Discrete Element Method (DEM) under field-like conditions, with experimental measurements conducted directly during agricultural processing. The proposed framework integrates image analysis with manual extraction of experimental particle trajectories, providing an efficient, flexible, and cost-effective validation approach. A multilayer perceptron artificial neural network (ANN) trained on 94,939 calibration samples was employed to transform pixel coordinates from two synchronized cameras into 3D spatial positions. To the best of the authors' knowledge, this represents the first application of an ANN-based trajectory reconstruction method under laboratory soil-channel conditions that replicate field-representative geometry and operating velocities. Experiments were conducted in a laboratory soil channel using a full-scale agricultural chisel operating at 1.0 and 1.5 m<middle dot>s-1, corresponding to realistic tillage velocities. The ANN achieved excellent accuracy (R2 = 0.9994, 0.9993, and 0.9988 for the X-, Y-, and Z-axes; average deviation 2.7 mm), and the subsequent comparison with DEM simulations resulted in an average nRMSE error of 4.7% for 1 m<middle dot>s-1 and 9.41% for 1.5 m<middle dot>s-1. The results confirm that the proposed methodology enables precise reconstruction of particle trajectories and provides a robust framework for the validation and calibration of DEM models under conditions closely approximating real field environments.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

40101 - Agriculture

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Technologies

ISSN

2227-7080

e-ISSN

2227-7080

Volume of the periodical

13

Issue of the periodical within the volume

DEC 5 2025

Country of publishing house

CH - SWITZERLAND

Number of pages

32

Pages from-to

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UT code for WoS article

001646335500001

EID of the result in the Scopus database

2-s2.0-105025969902