Studentische Arbeiten

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Es soll ein Strömungssensor entwickelt werden, der unter rauen Umgebungsbedingungen Strömungsgeschwindigkeit und Richtung der Strömung erfasst. Raue Umgebungsbedingungen liegen u.a. in Landmaschinen bei Beladung der Luft mit Staub oder Partikeln vor. Im Rahmen von Voruntersuchungen wurde ein vielversprechendes Sensorkonzept umgesetzt und getestet, welches weiterentwickelt werden soll. Als Entwicklungswerkzeuge stehen sowohl Laboruntersuchungen als auch Software zur kombinierten multi-physikalischen Simulation der elektrischen, mechanischen und thermischen Vorgänge bereit. 

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Mithilfe der DEM (Diskrete Elemente Methode) soll der Druschprozess im Mähdrescher modelliert und simuliert werden. Insbesondere die Abscheidung der Körner entlang des Dreschkorbes, als auch die Entstehung von Bruchkorn und Kurzstroh stellen die Untersuchungsschwerpunkte dar. Verschiedene Aufgaben stehen innerhalb diese Übergeordneten Themas zur Vefügung: z.B.  Versuchsstandskonzeption und -konstruktion, Numerische Modellierung und Validierung

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Zur Optimierung strömungsbasierter Prozesse in der Landtechnik werden verstärkt numerische Berechnungsverfahren wie CFD und DEM eingesetzt. Die Kopplung dieser beiden Entwicklungswerkzeuge setzt die Modellierung des Strömungswiderstandes der im allgemeinen nichtsphärischen Partikel voraus. Die bisher in kommerziellen Tools implementierten Modelle erfüllen die Genauigkeitsanforderungen nicht, weshalb experimentelle und numerische Untersuchungen durchgeführt werden müssen. Auf deren Basis soll die Implementierung in kommerziellen CFD-Code erfolgen.

A current research project is developing an immersive operating and training simulator that replicates the real behaviour of the vehicle. A necessary component of a machine and process simulation is a corresponding background soundscape, as acoustic perception is an important source of feedback. Sound design for a combine harvester simulator is to be investigated, which acoustically reproduces the operating and process conditions of the machine. The sound synthesis should be linked to the process and machine models. Recordings of real machines could also be performed and used for the project.

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In experimental work and in the development of modern agricultural technology, determining field conditions and the mechanical properties of plants is of central importance. The subject of the work is the development of a digitalised sampling method. To achieve this, the method for determining plant parameters in field trials must first be optimised and standardised on the basis of a literature review. Furthermore, it should be possible to record the data digitally directly in the field. Also, a systematic storage solution for the data should also be developed. Finally, the developed system must be tested and evaluated in field use.

Technical approaches for adapting or upgrading forage harvesters for processing woody biomass are to be investigated.

A method is to be developed and validated that makes it possible to derive a yield estimate for market crops (wheat, barley, oats, rapeseed, etc.) from various remote sensing data. The aim is to support farmers and developers of agricultural technology in data-based decision-making. This will enable farmers to make more valid economic deci-sions. For research and development, the achievement of objectives can be reviewed at an early stage. 

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Understanding the forces required to detach grains from wheat ears or to separate kernels from barley husks is essential for improving threshing processes in agricultural machinery. However, reliable data on these detachment forces is scarce, because suitable measurement methods and repeatable fixtures are not yet standardized.

The aim of this work is to develop a conceptual experimental setup capable of measuring axial and peel type detachment loads under controlled conditions. Possible approaches include e.g. the use of micro load cells and precision fixtures such as monofilament–adhesive attachments, micro pin hooks, or wax based clamps to hold individual spikelets or kernels.

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Understanding the forces required to detach grains from wheat ears or to separate kernels from barley husks is essential for improving threshing processes in agricultural machinery. However, reliable data on these detachment forces is scarce, because suitable measurement methods and repeatable fixtures are not yet standardized.

The aim of this work is to develop a conceptual experimental setup capable of measuring axial and peel type detachment loads under controlled conditions. Possible approaches include e.g. the use of micro load cells and precision fixtures such as monofilament–adhesive attachments, micro pin hooks, or wax based clamps to hold individual spikelets or kernels.

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Objective assessment of straw quality is an important requirement for comparing the performance of different combine harvesters, yet no standardized testing device currently exists. A recent student project at Aarhus University established a concept based on a vibrated V‑plate mechanism that orients straws and enables length‑based sorting through staged gaps. 

Building on previous work, the aim of this work is to further develop the underlying concept, explore scaling strategies, and design a reliable feeding mechanism capable of delivering a steady, unclumped flow of straw to the sorting module. 

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In agricultural research, field trials and test stands generate large quantities of samples that need to be evaluated in order to assess and compare different experimental variants. An important parameter in this context is the proportion of kernels damaged during threshing. This broken kernel ratio provides insights into the quality of the harvesting process. Currently, it is determined manually, for example by counting or sieving. This is time-consuming and significantly delays the availability of results.

The aim of this thesis is to develop an AI-based method that can determine the broken kernel ratio quickly and conveniently using image captures. A simple photograph of a kernel sample should be sufficient to automatically detect damaged kernels and reliably calculate the broken kernel ratio. The method is intend-ed to replace manual evaluation and significantly accelerate data collection in agricultural research.