Airline catering, which includes food and beverage (F&B) services, plays a crucial role in shaping the passenger experience. Many travelers discuss the quality of the onboard F&B services. Excessive catering can result in significant waste, while insufficient catering can lead to unhappy passengers. According to IATA estimates, between $2 billion and $3 billion worth of F&B items are wasted each year. Moreover, the weight of unused F&B items contributes to unnecessary fuel consumption.

To address this issue, solutions can be found in determining optimal catering levels for every flight. The primary challenge is to gather objective data or develop a reliable model. Various concepts exist for collecting data on individual pre-, in-, or post-flight orders, utilizing behavioral analytics, or employing generic route modeling.

In this thesis:

The aim of this thesis is to predict optimal aircraft catering levels based on data available through the check-in process on a boarding pass. Parameters include names, nationalities, genders, ages, and aircraft destination, for instance. Historical ordering of individuals for a world-wide B787 fleet has been logged in a synthetic database. A Machine Learning (ML) model shall be trained using the boarding pass data to predict flight catering level and analyze the prediction quality. In a second step, an airline dashboard to show ordering fleet prediction using acceptable risk levels shall be designed. The dashboard will be integrated into the TU-Darmstadt cabin simulator software environment.

Airline catering, which includes food and beverage (F&B) services, plays a crucial role in shaping the passenger experience. Many travelers discuss the quality of the onboard F&B services. Excessive catering can result in significant waste, while insufficient catering can lead to unhappy passengers. According to IATA estimates, between $2 billion and $3 billion worth of F&B items are wasted each year. Moreover, the weight of unused F&B items contributes to unnecessary fuel consumption.

To address this issue, solutions can be found in determining optimal catering levels for every flight. The primary challenge is to gather objective data or develop a reliable model. Various concepts exist for collecting data on individual pre-, in-, or post-flight orders, utilizing behavioral analytics, or employing generic route modeling.

In this thesis:

The aim of this thesis is to predict an individuals future ordering based on their personal historical behavior that has been logged in a database. This Machine Learning (ML) model shall use available world-wide B787 synthetic data, augment the data, and train an ML algorithm. With the remaining data the objective is to determine prediction quality. In a second step, an inflight ordering/recording iPhone app for passenger and flight attendants shall be built to demonstrate how the data could be collected. The app will be integrated into the TU-Darmstadt cabin simulator software environment.

Airline catering, which includes food and beverage (F&B) services, plays a crucial role in shaping the passenger experience. Many travelers discuss the quality of the onboard F&B services. Excessive catering can result in significant waste, while insufficient catering can lead to unhappy passengers. According to IATA estimates, between $2 billion and $3 billion worth of F&B items are wasted each year. Moreover, the weight of unused F&B items contributes to unnecessary fuel consumption.

To address this issue, solutions can be found in determining optimal catering levels for every flight. The primary challenge is to gather objective data or develop a reliable model. Various concepts exist for collecting data on individual pre-, in-, or post-flight orders, utilizing behavioral analytics, or employing generic route modeling.

In this thesis:

The aim of this thesis is to build an image recognition collection algorithm to collect inflight food ordering data in a cabin mockup. Images are collected in a galley, on a food trolley, and withing a cabin, the data is then analyzed, and consumption will be identified. The data will be compared to the real consumption. The analysis should be on a cabin and an individual level and not include flight-attendant or passenger intervention. In a second step, an airline dashboard to show consumption levels shall be designed. The dashboard will be integrated into the TU-Darmstadt cabin simulator software environment.

Robots are extremely versatile machines with almost endless application possibilities. In the course of an exhibition, a prototype demonstrator of these diverse applications was developed, in which our 7-axis collaborative robot arm ‘Rica’ is used to draw faces. Images are converted into line drawings and paths to be traversed are calculated from them. This demonstrator is now to be further developed and upgraded.

Iron is considered as a promising energy carrier in metal-based energy storage cycles, where energy is released via thermochemical oxidation. As part of this thesis, a laser system for the controlled ignition of single levitated iron particles will be developed, constructed, and commissioned to enable their elemental analysis during combustion using Laser-Induced Breakdown Spectroscopy (LIBS).

Rising product complexity, shorter development cycles, and sustainability demands are driving automation in product development. AI-based generative design, especially in topology optimization, offers efficient ways to explore complex design spaces and enable largely automated processes.

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.

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.