Particle Movement in DEM Models and Artificial Neural Network for Validation by Using Contrast Points

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41310%2F24%3A101825" target="_blank" >RIV/60460709:41310/24:101825 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3390/technologies12120257" target="_blank" >https://doi.org/10.3390/technologies12120257</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/technologies12120257" target="_blank" >10.3390/technologies12120257</a>

Result language

angličtina

Original language name

Particle Movement in DEM Models and Artificial Neural Network for Validation by Using Contrast Points

Original language description

The calibration and validation of input parameters in the Discrete Element Method (DEM) are crucial for accurately simulating physical processes, typically achieved through experimental particle behavior analysis. Enhancing the accuracy of DEM models allows for more reliable predictions of material behavior, which is essential for optimizing engineering applications that involve particulate materials. In this study, we present a methodology for analyzing the movement properties of particulate materials, employing a combination of Caliscope software to obtain the real-world co-ordinates based on pixel values from both cameras and artificial neural networks for regression as straightforward and efficient tools. This approach enables the validation and calibration of digital twins of particulate matter systems with respect to motion characteristics. The method of contrast points was utilized to acquire spatial co-ordinates of particulate material movement from experimental measurements, facilitating precise trajectory determination and the subsequent verification of simulation predictions. The neural network analysis demonstrated high accuracy, achieving R2 values of 0.9988, 0.9972, and 0.9982 for the X-, Y-, and Z-axes, respectively. The standard deviation between the predicted and actual co-ordinates was found to be 1.8 mm. A comparative analysis of particle trajectories from both the model and experimental data indicated strong agreement, underscoring the soundness and reliability of this approach.

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

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

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

12

Issue of the periodical within the volume

DEC 2024

Country of publishing house

CH - SWITZERLAND

Number of pages

22

Pages from-to

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UT code for WoS article

001383708500001

EID of the result in the Scopus database

2-s2.0-85213483287