An active flow control method is used with the aim of friction drag reduction. The method utilizes streamwise oscillation of spanwise velocity at the wall. Owing to reduced turbulence intensity, less energy is required to drive the flow against viscous resistance. The key question is how compressibility affects the drag reduction results. A huge dataset of different flow cases with carefully selected parameter combinations has been created to reliably extract the influence of important flow parameters on the flow behavior. Direct numerical simulations and large eddy simulations of subsonic and supersonic channel flow were run with Mach numbers based on the mean velocity of Ma = 0.3, 1.5 and 3.0 and Reynolds numbers Re based on the friction velocity in the range of 190 up to 2540. Mean property effects are predominant in supersonic channel flow through large variations of the mean density and temperature and wall cooling. Consequences are a higher Reynolds stress anisotropy, increased length scales in the viscous sublayer flow and enhanced streak stability. The pressure-strain correlation and spanwise dissipation terms undergo a strong attenuation compared to the incompressible counterparts, especially in the controlled flows. Higher drag reduction and a larger optimum control wavelength are observed compared to the corresponding incompressible flows. Increasing the Mach number enhances these effects due to stronger mean property variations. A mitigation of near-wall compressibility effects occurs with an increase of the bulk Reynolds number, though, which attenuates the drag reduction benefits in the controlled flow as well. In the second part of this work, cooling terms were introduced in the Navier-Stokes equations to mimic the wall-normal temperature profile of an external boundary layer flow and to minimize variable property effects within the complete channel, respectively. This approach allows for a better isolation of intrinsic compressibility effects and wall cooling effects. The cooling strategies highlight the importance of intrinsic compressibility effects, which contribute to an increased control efficiency, too. As a potential case of application for the flow control method, a jet flow emanating from a round pipe is considered in the third part of this work. The turbulent subsonic pipe flow is manipulated by the transverse wall velocity and the focus is on the impact on the jet development. No direct interference of the streanwise oscillation pattern of transverse velocity within the pipe and the jet region is detected. However, the lowered turbulence intensity at the pipe exit noticeably affects the jet flow development, independent of how the reduction is achieved. The shear layer of the controlled jet flow develops with higher turbulence intensities associated with intensified Kelvin-Helmholtz instabilities. The effects can be relativized if the maximum transverse velocity is located directly at the pipe exit. Consequently, a jet with similar characteristics to the uncontrolled one can be generated with the advantage of a substantially lowered energy expenditure.

This thesis focuses on advancing the synthesis of polydivinylbenzene (PDVB) microparticles via precipitation polymerization and swelling techniques, aiming to tailor their morphological features for enhanced applications, particularly in size exclusion chromatography (SEC). The first part focuses on the synthesis of core-shell particles via precipitation polymerization, consisting of a non-porous core and a porous shell, intended as column material for SEC. Monodisperse, non-porous core particles were reliably produced using cost-efficient standard laboratory equipment in place of specialized devices. Next to shell growth, it could be shown that low-molecular-weight porogens common for suspension polymerization do not yield pores suitable for SEC in precipitation polymerization under the tested conditions. This could be explained by the lack of confined space during particle formation in precipitation polymerization. The synthesized particles proved effective for hydrodynamic chromatography (HDC) and can be used as size standards for analytical applications like particle size analyzers. The second part presents an approach to PDVB particle synthesis by combining precipitation polymerization with a template-swelling technique. Three distinct strategies involving different polystyrene templates were explored, resulting in particles with intricate morphologies, including surface grooves and hollow cores. SEM analysis revealed the impact of template architecture on the particle morphology. The resulting particles with adjustable void sizes hold promise for various applications, like catalysis and delivery systems. This work provides a proof of concept, and future research may further extend control over the nanoarchitecture and explore other templating methods. The third part explores an alternative route to create a porous shell on a highly crosslinked PDVB core particle, utilizing the template swelling method with macromolecular porogen aiming at pore sizes larger than 10 nm. The two-step swelling method sought to integrate the PS porogen into the precursor particle shells and subsequently remove it to leave behind the desired porous structure. However, despite successful incorporation of the PS template during the initial swelling stage, the subsequent removal of the porogen did not lead to the expected porous structure. Excessive, unintended crosslinking, possibly due to chain transfer reactions or residual double bonds in the PDVB system, appears to have hindered the removal of the PS template. While the procedure still seems promising, further research needs to be done to lower the crosslinking degree of the precursor particle shell. The findings contribute to the ongoing efforts to enhance the versatility of PDVB particles for diverse applications, like for example for HDC chromatography or in the biomedical field, emphasizing the importance of combining and tailoring synthesis methods to achieve specific morphological features. Parts of this thesis were performed in collaboration with PSS GmbH, Mainz and LiNaCon Dr. Ingo Lieberwirth, Mainz.

In engineering, the modeling of complex systems plays a central role. Increasing computing power and storage capacities as well as the trend towards deep neural networks are resulting in more and more data being stored. This dissertation addresses two main challenges arising from handling large amounts of data for data-driven modeling: Firstly, the choice of a subset that is representative of the dataset from which it is selected. Secondly, the handling of unbalanced datasets, i.e., datasets with regimes of higher and lower point density. The first challenge is addressed by developing a novel subset selection algorithm based on kernel density estimation. The method ensures that the selected subset is representative of the original dataset or any desired arbitrary distribution. A sophisticated yet simple approach to evaluating the estimated density allows to save computing time. The second challenge is addressed by introducing a data weighting method that extends the standard loss function. The weights for the individual data points are adjusted in such a way that data points from regions of sparser point densities are weighted higher and data points from regions of higher point densities are weighted lower in order to ensure a more balanced model performance. This approach is independent of model architecture and suited for any training algorithm. The effectiveness of the developed methods is demonstrated by using benchmark datasets and real-world application examples. Among others, the examples of thermal modeling of a permanent magnet synchronous motor and a cold forming process are used. The results show that the presented method for subset selection can effectively select representative datasets and is on par with state-of-the-art approaches to subset selection. Additionally, the presented method is able to select the subset to represent arbitrary desired pdfs which gives the user much freedom of design. The introduced method for data weighting typically results in significant performance improvements for dynamic models, especially for imbalanced training datasets. Overall, these contributions provide a valuable contribution to the further development of data-driven modeling methods and offer practicable solutions for real-world challenges.

The final report of the DFG-funded project ‘State History on the Radio: The Production Process and the intermedial Transfer of Regional Historical Narratives by the WDR Regional Editorial Office under Walter Först (1960–early 1990s)’ briefly summarises the results of the historical research. Using North Rhine-Westphalia as an example, the project analyses narratives of state history distributed by mass media between 1960 and the early 1990s and traces practices of "doing history". In a symbiosis of public history and media and state historical perspective, the intermedial transfer of historical contributions as building modules of historical culture will be examined exemplarily on the basis of the WDR-Hörfunk-Landesredaktion (WDR radio editorial office dealing with state affairs) around the journalistic NRW history expert Walter Först (1920-1993). The editorial team has actively produced and anchored formative historical images for North Rhine-Westphalia. On the basis of intensive archival research, it analyses how the journalistic actors located the state in the field of tension of a diverse region, the Federal Republic of Germany and its European neighbours and how they promoted historical processes of meaning and identity formation. It is examined how the editor-in-chief established a network with other institutions and persons working in the fields of politics, culture and history, transferred this into institutional forms beyond broadcasting and thus created an additional communication basis for negotiated narratives.