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Institutsvorstellung zur Orientierung für 5.Semester und Abschlussarbeitende am Mittwoch, 10. Februar 2021 um 14:00 Uhr


Am 10. Februar ab 14:00 Uhr möchten wir für interessierte Studierende am Vertiefungsfach unser Institut und die Möglichkeiten, die sich bei uns zur Anfertigung einer Bachelor- oder Masterarbeit anbieten, vorstellen. Dabei ist ein Gespräch mit wissenschaftlichen Mitarbeitern über deren Forschungsgebiete möglich.
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Contact

Engler-Bunte-Ring 7
76131 Karlsruhe 

Building number 40.13.I 

Tel: +49(0)721 608-42571
Fax: +49(0)721 608-47770

E-Mail: Secretariat

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Bachelor- and Masterthesis

Current proposals for topics of bachelor- and master thesis you find on the following page.
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List of current research projects
Energy Efficient Coil Coating Process
Emissions Soot Model
Machine learning for Advanced Gas turbine Injection SysTems to Enhance combustoR performance
Methane Engines for Passenger Vehicles





Energy Efficient Coil Coating Process
(ECCO)
Energy Efficient Coil COating Process

Coil coating is a continuous process for providing coating to a metal strip. In 2017, a total area of 1.37 billion m² of aluminium and steel was coated with 219 kt of paint in Europe, representing one third of the worldwide production. The coil coated products are mainly used in the construction market as building envelope. Consumers encounter coil coated products in everyday life for example as casing in a variety of size from fridges, washing machines to toasters and wireless speakers. In the coil coating process, a paint, mainly consisting of pigments, chemical crosslinkers and solvents, is applied to a metal strip. In a following step the paint is dried while the solvents evaporate. Afterwards, the paint is cured up to a certain temperature where the crosslinkers increase the adhesion between pigments and metal strip. In the conventional process the required heat is provided through convective heat transfer using hot air. In order to prevent the creation of an explosive atmosphere in the process, operation at a solvent concentration below the lower explosion limit by using an excess amount of air is inevitable. Prevention of VOC emission entails either recovery or thermal decomposition of the solvents, which can be stated as being technically complex and expensive due to the high dilution of the solvents.

In the ECCO project the proof of concept of a novel curing oven will be performed in a pilot scale coil coating line. In ECCO, the curing oven is operated at elevated solvent concentration which allows the direct utilization of solvents as a fuel for heat generation. Therefore, the oven system is separated in two sections: The radiant burner section, where intense radiation in the IR-spectrum is emitted at high temperatures resulting from combustion inside of a ceramic porous structure, and the curing oven section which is operated over the upper explosion limit or, in other words, below a critical oxygen concentration. The prevention of a thermal decomposition of the solvent loaded atmosphere at high temperatures is ensured through separation of the two oven sections by an IR-transmissive material. Starting from previous activities at TRL 4, an interdisciplinary approach is foreseen, based on advanced-materials, combustion technology and prediction tools for system design/optimization, with active participation of key industrial stakeholders, to bring this technology to TRL 6 and realize a prototype curing oven at industrially relevant size and environment. ECCO proposes an oven concept which leads to a drastically reduced size and increased energy efficiency as we as well a higher production flexibility due to a fuel-flexible, modular and potentially energetically self-sustainable process. In comparison to existing conventional convective curing systems, ECCO presents a less energy demanding, environment-friendly and economical technical curing oven concept.





Emissions Soot Model
(ESTiMatE)

Within the EU-H2020 project "Emissions Soot Model" (ESTiMatE), a modelling strategy will be developed to predict carbon particle (soot) emissions from the operation of aircraft engines. 

This requires the improvement or development of sophisticated models for the relevant sub-processes and validation using reference experiments to ensure a reliable prediction of soot emissions. The aim of the work of the Institute of Combustion Technology within the project is to generate data sets under representative combustion conditions which can be used to validate the models developed.

In this respect, laminar counterflowmodel flames of a kerosene surrogate and its individual components (e.g. dodecane and iso-octane) are investigated fundamentally to explain the influence of fuel composition and pressure (up to 8 bar) on the flame structure and in particular on the formation of soot precursors [benzene (A1), naphthalene (A2), pyrene (A4), etc.] and soot particles. The data obtained are first compared with already developed chemistry models and then used to validate the models developed in ESTiMatE. The figure shows experimental and numerically calculated concentrations of gaseous species in a non-premixed counterflow flame of iso-octane.





Machine learning for Advanced Gas turbine Injection SysTems to Enhance combustoR performance
(MAGISTER)


The project is funded in the framework of Marie Skłodowska-Curie Actions as Innovative Training Network (ITN).
 
Air transportation is expected to grow persistently over the next decades. Clean combustion technology for aircraft engines is a key enabler to reduce the impact of this growth on ecosystems and humans’ health. The vision for European aviation is shaped by the Advisory Council for Aviation Research and Innovation in Europe in the Flight Path 2050 goals, which define stringent regulations on pollutant emissions.  
 
To meet these goals, the major engine manufacturers develop lean premixed combustors operated at very high pressure. This development introduces a large risk for reduced reliability and lifetime of engines: pressure oscillations in the combustor called thermoacoustics.  
 
Aviation industry encounters currently the fourth industrial revolution: cyber-physical systems analyze and monitor technical systems and take automated decisions. This industrial revolution is known as “Industry 4.0” in Germany and “Industrial Internet” in the USA. An essential enabler of the fourth industrial revolution is Machine Learning.  
 
The ITN MAGISTER will utilize Machine Learning to predict and understand thermoacoustics in aircraft engine combustors, and to lead combustion research to a revolutionary new approach in this area.




Methane Engines for Passenger Vehicles
(MetCar)
Contemporary Natural Gas (NG) engines for passenger car applications are not consequently optimized for NG operation. But, due to its high knock resistance, NG offers a high efficiency potential versus gasoline already. EE-C-Methane consists mainly of very neat methane. Therefore, it offers an even higher knock resistance (higher Methane Number) than NG. The higher knock resistance can be transformed into higher efficiency by further increasing the compression ratio (CR) and the boost level of the engine.
In order to exploit the potential and to achieve the high efficiencies, while maintaining drivability and component durability, many aspects need to be considered during the development of a dedicated EE-C-Methane engine, which are content of the described project MethCar. Beside the significant rise of the peak combustion pressure capability of the base engine, volumetric efficiency is going to be increased by means of a new methane direct injection system and a turbo charger with variable turbine geometry, as well as a fully variable valvetrain. Furthermore, the impact of the methane composition (EE-C-Methane) is an important factor for the market introduction potential of methane as automotive fuel. Therefore, in MethCar, the impact of the expected main components of EE-Methane (H2, CH4) and trace elements (as sulfur and compressor oil) on component wear and catalyst efficiency is investigated.
The 3rd innovative element of the study is the investigation how to avoid particle emissions robustly, with the focus on small particles.

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