Die Realisierung einer effizienten und umfassenden urbanen Luftmobilität (UAM) erfordert eine detaillierte Betrachtung der Infrastruktur, insbesondere der Vertiports, die als grundlegende Drehkreuze für einen potenziellen Flugtaxi-Betrieb dienen könnten. Dabei erfordert die Skalierung des Flugtaxi-Betriebs eine vorausschauende Planung, die sicherstellt, dass An- und Abflüge an Vertiports mit der potenziellen zukünftigen Nachfrage Schritt halten können, ohne die Sicherheit und Effizienz zu beeinträchtigen. In dieser Hinsicht spielt die Forschung zur Entwicklung und Bewertung automatisierter An- und Abflugstrategien eine entscheidende Rolle, um eine robuste und fortschrittliche Infrastruktur für zukünftige Flugtaxioperationen in urbanen Gebieten zu schaffen.

This master's thesis investigates the integration of metallic energy carriers into large-scale energy systems. Metallic energy carriers are first implemented in the open-source framework PyPSA (Python for Power System Analysis). Subsequently, their role and potential in future multimodal energy systems are analyzed.

At the IMS, the potential for increasing the efficiency of powertrains by knowing the speed to be expected in the next few seconds is being investigated on the basis of AI-based predictions.

The Kolmogorov-Arnold networks (KAN) approach presented in April 2024 promises faster training, improved accuracy and interpretability compared to conventional multi-layer perceptrons (MLP). So far, only a simple Matlab implementation of KANs with basic functionality exists.

The aim of this advertised work is to realise a KAN implementation against the background of the Matlab Deep Learning Toolbox and to use it for speed prediction.

A data set comprising over 29,000 kilometres is available as a data basis.

The constant availability of cabin elements in the aircraft is an essential part of efficient, customer-friendly and economical processes for the operator. Particularly in the case of business class seats, failures of individual components can prevent them from being allocated to passengers and thus prevent optimal flight services. The coordination of maintenance procedures is often challenging due to tightly scheduled flights and therefore has significant potential for optimization. By recording usage data, it should be possible in future to monitor the condition of worn seat components so that maintenance measures can be prepared and carried out in a more targeted manner. The physical behavior of brushless DC motors can already be simulated at the FSR using a MATLAB Simulink model, taking into account various degradation mechanisms.

Surface engineering at the micro- and nanoscale is a rapidly growing field with significant potential for developing innovative smart interfaces. An important aspect that requires further research is understanding how nanosuspension droplets are deposited on nanofibre mats during evaporation. Mastering this process is crucial to precisely control the final morphology and properties of the deposited materials.

Film evaporation is of crucial importance in both scientific research and industry. An important aspect in this area is understanding the behaviour of an evaporation film consisting of two components. Simulation of such systems promises to improve our understanding of these complex phenomena and provide deeper insights into their behaviour and properties.

The evaporation of liquid droplets, a phenomenon ubiquitous in daily life, has garnered significant attention in scientific research. Of particular interest is the evaporation of droplets laden with nonvolatile solutes, which results in intricate deposition patterns on the substrate. Understanding the mechanisms underlying the formation of these patterns is crucial for various technical applications, spanning from coating and inkjet printing to disease detection. This work delves into the development of a comprehensive model of the specific ring-like deposition patterns observed during the drying of droplets.

At the IMS, the potential for increasing the efficiency of powertrains by knowing the speed to be expected in the next few seconds is being investigated on the basis of AI-based predictions.

The Kolmogorov-Arnold networks (KAN) approach presented in April 2024 promises faster training, improved accuracy and interpretability compared to conventional multi-layer perceptrons (MLP). So far, only a simple Matlab implementation of KANs with basic functionality exists.

The aim of this advertised work is to realise a KAN implementation against the background of the Matlab Deep Learning Toolbox and to use it for speed prediction.

A data set comprising over 29,000 kilometres is available as a data basis.

The Institute of Mechatronic Systems in Mechanical Engineering is conducting research into energy-efficient drive concepts for electric robot vehicles. To better assess the potential for future drive and transport concepts, these must be tested using simulations. In order to be able to assess the influence of a traffic environment, a traffic simulation of the city centre of Darmstadt is therefore used at the IMS. This is implemented in the SUMO simulation software. An initial approach to parameterizing the simulation was developed in previous works and is now to be continued.

As part of the FlowForLife research project, a microfluidic supply network for 3D cell clusters is being developed. One aspect of the network design is the oxygen transport in the surrounding matrix. To characterize this, oxygen quenching luminescent particles are added to the surrounding matrix. The oxygen concentration can be determined at each particle.

From these sparse pointwise concentration data, the flow field in the porous surrounding matrix is to be determined. A solution of the convection-diffusion equation representing the measured concentration field must be found so that conservation of mass and momentum is satisfied in the underlying flow field. Physically-informed neural networks (PINNs) are a promising approach to find such a solution. In this thesis a PINN code should be developed and the achievable accuracy is to be evaluated. The following tasks could be part of the thesis:

Extreme weather conditions are increasingly contributing to aircraft icing events, which pose significant safety risks. The analysis of this phenomenon has gained increased attention, particularly in the field of aviation safety.

Our new research aims to better understand the involved phenomena by analyzing moving surfaces and high relative impact velocities. This approach will closely simulate the real-world conditions that aircraft wings experience.

The project involves simulating a single water drop impact onto a moving substrate in order to replicate existing experimental data.

Am IMS wird an der Verbesserung der Betriebsstrategie von Brennstoffzellenfahrzeugen geforscht. Bisherige Ergebnisse zeigen Verbesserungspotential durch den Einsatz KI-basierter Prädiktionen. Um die Ansätze gut bewerten zu können, ist der Vergleich mit einer global-optimalen Lösung sinnvoll.

Dynamic Programming (DP) ist ein weit verbreiteter Ansatz, um global-optimale Lösungen für auf Zeitreihen basierende Probleme zu finden. In der Literatur wird er vielfach bspw. als Referenzstrategie für Hybridfahrzeuge verwendet. Auf Basis eines an der ETH Zürich entwickelten Tools soll im Rahmen dieser Arbeit ein Modell für ein Brennstoffzellenfahrzeug für DP vorbereitet und in das Tool integriert werden.

Im Projekt „DigiTain“ wird am IMS an der Verbesserung der Betriebsstrategie von Brennstoffzellen-fahrzeugen geforscht. Aktuelle Ergebnisse zeigen, dass mittels Geschwindigkeitsprädiktion eine Effizienzsteigerung erreicht werden kann.

In den bisherigen Untersuchungen wurden das Thermalsystem des Brennstoffzellensystems sowie die Alterung von Batterie und Brennstoffzelle (BZ) unberücksichtigt gelassen. Im Rahmen dieser Arbeit soll durch deren Ergänzung die Simulation realitätsnäher werden sowie Alterung von Batterie und Brennstoffzelle in der Betriebsstrategie berücksichtigt werden.

At the IMS, the potential for increasing the efficiency of powertrains by knowing the speed to be expected in the next few seconds is being investigated on the basis of AI-based predictions.

The Kolmogorov-Arnold networks (KAN) approach presented in April 2024 promises faster training, improved accuracy and interpretability compared to conventional multi-layer perceptrons (MLP). So far, only a simple Matlab implementation of KANs with basic functionality exists.

The aim of this advertised work is to realise a KAN implementation against the background of the Matlab Deep Learning Toolbox and to use it for speed prediction.

A data set comprising over 29,000 kilometres is available as a data basis.

At the IMS, the potential for increasing the efficiency of powertrains by knowing the speed to be expected in the next few seconds is being investigated on the basis of AI-based predictions.

The Kolmogorov-Arnold networks (KAN) approach presented in April 2024 promises faster training, improved accuracy and interpretability compared to conventional multi-layer perceptrons (MLP). So far, only a simple Matlab implementation of KANs with basic functionality exists.

The aim of this advertised work is to realise a KAN implementation against the background of the Matlab Deep Learning Toolbox and to use it for speed prediction.

A data set comprising over 29,000 kilometres is available as a data basis.

The aim of this work is to enable the automated consideration of life cycle assessment in the optimisation of design process.

As emission regulations tighten and policies evolve to address global climate change, reducing pollutant and greenhouse gas emissions has become a top priority in combustion research. The advanced combustion concept of matrix-stabilized combustion is essential for achieving low emissions and improved flame stabilization in fuel-lean conditions. Combustion within an inert porous matrix differs significantly from conventional burners that use a free flame. Porous media burners (PMBs) rely on the principle that the solid porous matrix internally recirculates heat from combustion products back to the reactants. This internal heat recirculation in PMBs lowers the lean flammability limit of fuel-air mixtures, enabling lower emissions, reduced thermal stresses due to lower flame temperatures, and complete fuel conversion through lean combustion. However, stabilizing these flames within the porous matrix poses challenges due to the complex thermophysical, transport, and heat-transfer processes involved.

The objective of this project is to install a well-designed PMB in the RSM laboratories and perform the first experimental investigation using different pore structures and fuel mixtures.

As the push for sustainable energy solutions intensifies, the combustion of biomass solid fuels in ammonia (NH3)-enriched atmospheres presents a promising path forward. This approach not only leverages renewable biomass resources but also explores the potential of ammonia as a carbon-free hydrogen carrier and fuel. To deepen our understanding of the combustion dynamics and emission characteristics under these conditions, we propose the development of a novel laminar flow reactor specifically designed for optical measurements of biomass solid fuel flames in an NH3-enriched atmosphere.

As global climate policies become increasingly stringent, reducing pollutant and greenhouse gas emissions has become a paramount objective in combustion research. Hydrogen-powered combustors promise to reduce pollutant emissions, particularly NOx, while enhancing efficiency and performance under extreme conditions. However, harnessing hydrogen's potential requires overcoming significant technical challenges, particularly in designing combustors that can operate under extreme conditions while minimizing emissions. A better fundamental understanding of hydrogen combustion requires urgently experimental research efforts. This project focuses on developing a lab-scale hydrogen-powered combustor capable of operating under high-temperature, high-pressure, and high-turbulence conditions.

Shared test platforms can realize multi-functionality investigations at the early stage of the vehicle powertrain development.

At IMS, the CONNECT (powertrain test bench) and Driveception (driving simulator) are integrated for a distributed measurement of powertrain dynamics and their impact on subjective perception during the launch process. The student work will be focused on the realization of the test bench networking and its real-time performance for the launch process study.