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:

Implementation of a transport equation for the air humidity to analyze nasal cavity flowsDownload
Methods to diagnose pathologies in the human respiratory system have evolved recently from mainly focusing on medical imaging data to the consideration of computational fluid dynamics (CFD). In the past, the thermal lattice-Boltzmann (TLB) solver of the simulation framework multiphysics Aerodynamisches Institut Aachen (m-AIA) has been frequently used to numerically qualify the nasal cavity by analyzing the fluid mechanical properties of the respiratory flow, such as the pressure loss, the temperature distribution, and the mass flux distribution. However, an important quantity that has not been considered so far in these studies is the humidity of the inhailed air. Dry nasal passages can lead to discomfort and irritated sinuses and in the worst case to lung infections.
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.
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
Numerical analysis of turbulent particle-laden flowsDownload
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