Projekt-, Bachelor- und Masterarbeiten

Der Lehrstuhl für Strömungslehre bietet innerhalb seiner Forschungsschwerpunkte verschiedene Möglichkeiten für Studierende und  Absolventen an den Lehr- und Forschungsaktivitäten teilzunehmen.

Ansprechpartner numerische Arbeiten: Dr.-Ing. M. Meinke

Ansprechparnter experimentelle Arbeiten: Dr.-Ing. M. Klaas

Auch wenn im Folgenden keine Studien-, Master-, Bachelor- oder Projektarbeiten aufgeführt sind können Sie sich bei Interesse gerne jederzeit an die oben genannten Ansprechpartner wenden.

Angebotene Bachelorarbeiten:

Analysis of time-filtering methods for wall-stress models in LES at high Reynolds numbersDownload
At high Reynolds numbers in the order of 10 000 000, which are frequently encountered in aerospace applications, the size of turbulent structures is so small that high-fidelity large-eddy simulations (LES) become unfeasible even on modern supercomputers. However, high resolution of the near-wall region is necessary to accurately predict wall-shear stress and guarantee a proper development of the boundary layer. To overcome this problem various wall-modeling approaches have been developed, that compute the correct wall-shear stress from the outer boundary layer and therefore allow for a significant reduction of the number of cells in the computational mesh. Simply computing the local wall-shear stress based on instantaneous data from the outer boundary layer transfers the dynamics of the outer layer turbulence to the boundary surface. In reality, however, the dynamics at the wall are suppressed significantly by the viscous sublayer. Therefore, the wall-model introduces a modeling error in the instantaneous behaviour of the wall-shear stress while giving correct predictions with respect to the temporal mean. In this thesis various time-filtering procedures with respect to the sampled data from the outer boundary layer and their effect on the predicted wall-shear stress will be analyzed. This requires the implementation of the filtering procedures into the multiphysics framework m-AIA. The implemented filtering strategies will then be evaluated by conducting multiple LES of a turbulent channel flow on the RWTH High Perfomance Computer CLAIX and analyzing characteristic properties the of boundary layer flow with respect to the literature.

Angebotene Masterarbeiten:

Topics in multiphase flow simulations using the Lattice Boltzmann MethodDownload
Using super-resolution networks to generate highly resolved computed tomography images from recordings with low resolutionsDownload
Numerische Analyse partikelbeladener turbulenter StrömungenDownload
Fast alle Strömungen, die in der Umwelt und Technik vorkommen, sind turbulent. Jedoch ist bereits die numerische Simulation einphasiger turbulenter Strömungen aufwendig, wobei es viele erfolgreiche Modellierungsansätze gibt. Eine noch größere Herausforderung besteht hingegen in der numerischen Analyse partikelbeladener turbulenter Strömungen. Trotz ihrer hohen Relevanz in Umwelt und Technik, sind vorhandene Modelle nur für vereinfachte Bedingungen gültig und eine Validierung steht oft noch aus. Ein wichtiger Anwendungsfall ist die numerische Auslegung einer Biomasse-Brennkammer. Dabei ist die Bestimmung der Aufheizraten, der Dynamik, und der turbulenten Durchmischung nicht-sphärischer Partikel entscheidend um den gesamten Verbrennungsprozess zuverlässig auszulegen. Die Generierung von hoch-aufgelösten Referenzdaten mit Hilfe von Simulationen und die Entwicklung von genauen Modellen für Anwender, sowie deren Validierung sind aktuelle Forschungsvorhaben, die am Aerodynamischen Institut intensiv verfolgt werden. Für dieses Projekt sind wir auf der Suche nach motivierten Masterarbeitern.
Machine learning-based prediction of forces in Lagrangian point-particle approachesDownload
Particle laden turbulent flows play an import- ant role in many technical fields such as drug de- position in human airways, or the combustion of solid biofuels. To simulate these problems, two dif- ferent approaches are available: The fully resol- ved direct numerical simulation (DNS), where all features of the flow field including the surface of the particles are resolved, and the Lagrangian point-particle approach, where empirical models are used to determine the forces acting on each particle. Due to the high computational costs of the DNS approach, reduced order point-particle models are indispensable for the simulation of lar- ge scale technical applications. These models pro- duce good results for very small particles but lead to significant errors for larger ones. In this thesis, data from a DNS will be used to train an artificial neural network (ANN) which predicts the forces acting on a particle in a reduced order point-particle model. In a first step, fully re- solved simulations with many particles have to be performed to extract the local flow field and force data for each particle, resulting in a large databa- se for the determination of new point-particle mo- dels. In the second stage, the ANN is trained based on this database, where velocity data from a par- ticle’s surrounding flow field function as input, the acting force as output, and the acting forces from the DNS as ground truth. In a final step, it is inve- stigated how transfer learning improves the pre- dictive capabilities of the ANN. That is, the ANN is pre-trained with ground truth data from empi- rical drag laws, and the highly resolved data from the DNS are employed solely in the final training iterations.
Machine learning-based modeling of non-equilibrium chemical reaction rates for the computation of hydrogen-air flamesDownload
Hydrogen combustion is shaping to be an im- portant component for the future of electricity grids based on renewable energies. Combustion si- mulations solve the mass, momentum and energy equations in addition to equations describing the reaction kinetics and transport of species. The- se additional equations are, in general, expensive to solve when compared to the non-reacting flow equations. In order to reduce computational expense, some simplified alternatives exist, like skeletal reaction mechanisms or two-step chemistry. If one desires to use the complete, relevant reaction chain, tabu- lated chemistry or machine learning models are a good alternative and a good compromise between accuracy and efficiency. In this context, four ty- pes of machine learning algorithms are employed. The first algorithm maps each location in the high- dimensional flow domain to a low-dimensional sub- space. With the second algorithm, representations in the low-dimensional subspace are clustered into regions of different thermochemical equilibrium. A classifier is then trained to classify new points into You ... these regions. Finally, reaction kinetics and trans- port species are learned for each cluster by artificial neural networks. The main tasks of the thesis are to (1) im- plement the proposed machine learning-based me- thod, and (2) compare the accuracy and speed of the proposed method compared to solving the re- acting flow equations, chemical kinetics and trans- port phenomena directly.
Numerical analysis of control of shock-wave / boundary layer interaction using air-jet vortex-generatorDownload
Multiphysics simulations with applications to aeroacousticsDownload
Injection and Turbulent Mixture Formation of Bio-Hybrid Fuels in Internal Combustion EnginesDownload
Active drag reduction in turbulent boundary layer flows subjected to spanwise traveling transversal surface waves using learning-enhanced CFDDownload
Thermoacoustic Investigations of Hydrogen-Air FlamesDownload
Rim seal gap sealingDownload
Researching the multiphase flows of the Precise Electrochemical Machining (PECM) processDownload
Automated assistance for diagnoses and treatments in rhinologyDownload
Correlations for inclined prolates using artificial intelligenceDownload
Particle-laden flows play a crucial role in many natural and technical fields such as pollutant transport through the human airways or solid-fuel combustion. A common approach to simulate such particle-laden flows is the Lagrangian point-particle method where empirical formulations approximate the particle motion. Precise point-particle models are necessary to properly determine particle dynamics, particle temperature, as well as the heat exchange between the solid and fluid phases. The goal of this thesis is to derive a new drag correlation for prolate spheroids as a function of the particle Reynolds number, the particle orientation as well as its aspect ratio using state-of-the-art machine learning techniques. For this purpose, highly-resolved direct numerical simulations past a fixed ellipsoid will be used as ground-truth solutions. The predicted correlation is then included in an ellipsoidal Lagrangian point-particle model.