What question did this study set out to answer?

This work aims to improve the predictive accuracy of DEM simulations by calibrating microscopic parameters using experimental data.

April 13, 2026Open Access

Dynamic Calibration of DEM Simulations Using Rotating Drum Experiments Using the Mean Absolute Error

Key Points

This work aims to improve the predictive accuracy of DEM simulations by calibrating microscopic parameters using experimental data.
Utilized rotating drum experiments to gather macroscopic flow properties.
Extracted dynamic angle of repose and roughness index from images of materials in motion.
Introduced a new area-based comparison method using mean absolute error between simulated and experimental data.
MAE provides a more robust calibration metric for DEM simulations.
Demonstrated reliable identification of suitable DEM parameter combinations.

Abstract

ABSTRACT The discrete element method (DEM) is widely used, but its predictive capability strongly depends on microscopic contact parameters that are difficult to measure directly. This work presents a systematic calibration approach using rotating drum experiments combined with image‐based analysis. Two macroscopic flow properties, the dynamic angle of repose and the roughness index, are extracted from experimental and simulated image sequences with a unified workflow. Individual metrics often agree only partially between experiment and simulation. Therefore, a new area‐based comparison method is introduced: the mean absolute error (MAE) between sequence matrices of particle occurrence. The MAE integrates differences in surface structure and dynamic flow behavior, providing a more robust calibration metric. First results demonstrate that the MAE enables a more reliable identification of suitable DEM parameter combinations.

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