Chair of

Material Handling

The Discrete Element Method (DEM) is a numerical procedure specifically developed for simulating the movement and interaction of a large number of particles. Each particle is treated as an independent, discrete element. The contacts between the particles are simulated in the computer as overlaps, and these overlaps give rise to particle forces. Then, Newton's law of motion can be applied to calculate particle acceleration from these forces, and by integrating, the particle velocities or new particle positions can be determined. Using different contact laws, friction, damping, and cohesive forces can be considered, allowing the simulation of a wide variety of bulk material behaviors.

The main research focuses in DEM are on the development of calibration methods for DEM parameters and on quantitative wear prediction. Calibration methods are crucial to ensure that the simulated bulk material behaves like the real material. There is still significant research potential, especially for cohesive and entangled materials. Research on quantitative wear prediction aims to provide a direct estimation of the lifespan of wear-resistant materials, which are constantly exposed to abrasive contact with bulk material.

In addition to the aforementioned research areas, DEM simulations are used in nearly all design-related research projects where the optimization of geometry or operating parameters of bulk material technology is a key focus. Furthermore, work is being done on coupling DEM simulations with other simulation methods, especially Finite Element Method (FEM) or Multi-Body System (MBS) simulations.

For the analysis, prediction, and optimization of bulk material processes, experiments are often required. For these purposes, we develop customized test setups – from small testing facilities for bulk material studies to innovative conveying technologies, such as a powder conveyor for weightlessness. Our experience includes the development, design, and construction of such test rigs and prototypes, as well as the planning, execution, and evaluation of the experiments. We also implement complex projects, such as our conveyor system, precisely and efficiently, providing comprehensive support for individual bulk material solutions.

The Internet of Things (IoT) is revolutionizing conveyor technology by enabling connected and intelligent control of conveyor systems. Sensors and actuators collect real-time data on throughput, speed, maintenance needs, and energy consumption, which are then analyzed in cloud systems. This helps reduce operating costs and increase efficiency, as problems can be detected early and downtime minimized. IoT-driven solutions also make it easier to adapt to changing material flow requirements, as systems can respond flexibly to production changes. Therefore, the integration of IoT in conveyor technology is an important step towards the Smart Factory and Industry 4.0.

The traceability of bulk materials in the field of food technology presents a challenging task that current
state-of-the-art solutions address only inadequately. In situations where the material flows under the influence of gravity rather than being actively conveyed, static storage models—such as those used for silo management today—are only partially applicable. Computer-aided traceability models based on the theory of cellular automata offer a novel and more precise approach to managing batch storage of free-flowing to slightly cohesive bulk materials in large silos or stockpiles.