Hendrik Seehausen

In this study, the modeling of rough surfaces by eddy-viscosity-based roughness models is investigated, specifically focusing on surfaces representative of deterioration in aero-engines. In order to test these models, experimental measurements from a rough T106C blade section at a Reynolds number of 400 K are adopted. The modeling framework is based on the k-ω-SST with Dassler’s roughness transition model. The roughness model is recalibrated for the k-ω-SST model. As a complement to the available experimental data, a high-fidelity test rig designed for scale-resolving simulations is built. This allows us to examine the local flow phenomenon in detail, enabling the identification and rectification of shortcomings in the current RANS models. The scale-resolving simulations feature a high-order flux-reconstruction scheme, which enables the use of curved element faces to match the roughness geometry. The wake-loss predictions, as well as blade pressure profiles, show good agreement, especially between LES and the model-based RANS. The slight deviation from the experimental measurements can be attributed to the inherent uncertainties in the experiment, such as the end-wall effects. The outcomes of this study lend credibility to the roughness models proposed. In fact, these models have the potential to quantify the influence of roughness on the aerodynamics and the aero-acoustics of aero-engines, an area that remains an open question in the maintenance, repair, and overhaul (MRO) of aero-engines.

Operating airfoils under mechanical stress in combination with oxidation and corrosion, abrasion wear, and subsequent regeneration results in complex surface structures that influence the performance of aircraft engines. For predicting this impact on performance the authors propose a reduced order model capable of assessing the effect of surface roughness as a basis for making decisions before or during the regeneration process. The accuracy of this model is increased by using improved roughness sensitive transition and turbulence models created for Reynolds-Averaged Navier–Stokes simulations. Experimental studies are carried out to determine the local and integral effect of complex surface structures on blades and on a flat plate. Direct numerical simulations are also performed to study the effect of complex surface structures on a turbulent boundary layer and contribute to improving the accuracy of prediction, which is achieved by using re-calibrating models and taking into account the effect of skewness and anisotropy of complex surface structures on turbine blade losses.

The effects of real combined variances in components and modules of aero engines, due to production tolerances or deterioration, on the performance of an aircraft engine are analysed in a knowledge-based process. For this purpose, an aero-thermodynamic virtual evaluation process that combines physical and probabilistic models to determine the sensitivities in the local module aerodynamics and the global overall performance is developed. Therefore, an automatic process that digitises, parameterises, reconstructs and analyses the geometry automatically using the example of a real turbofan high-pressure turbine blade is developed. The influence on the local aerodynamics of the reconstructed blade is investigated via a computational fluid dynamics (CFD) simulations. The results of the high-pressure turbine (HPT) CFD as well as of a Gas-Path-Analysis for further modules, such as the compressors and the low-pressure turbine, are transferred into a simulation of the performance of the whole aircraft engine to evaluate the overall performance. All results are used to train, validate and test several deep learning architectures. These metamodels are utilised for a global sensitivity analysis that is able to evaluate the sensitivities and interactions. On the one hand, the results show that the aerodynamics (especially the efficiency ηHPT and capacity m˙HPT) are particularly driven by the variation of the stagger angle. On the other hand, ηHPT is significantly related to exhaust gas temperature (Tt5), while specific fuel consumption (SFC) and mass flow m˙HPT are related to HPC exit temperature (Tt3). However, it can be seen that the high-pressure compressor has the most significant impact on the overall performance. This novel knowledge-based approach can accurately determine the impact of component variances on overall performance and complement experience-based approaches.

In this study, numerical models are used to analyse the influence of isolated component deterioration as well as the combination of miscellaneous deteriorated components on the transient performance of a high-bypass jet engine. For this purpose, the aerodynamic impact of major degradation effects...