With the growth of aviation traffic and the demand for emission reduction, alternative fuels like the so-called electrofuels could comprise a sustainable solution. Electrofuels are understood as those that use renewable energy for fuel synthesis and that are carbon-neutral with respect to greenhouse gas emission. In this study, five potential electrofuels are discussed with respect to the potential application as aviation fuels, being n-octane, methanol, methane, hydrogen and ammonia, and compared to conventional Jet A-1 fuel. Three important aspects are illuminated. Firstly, the synthesis process of the electrofuel is described with its technological paths, its energy efficiency and the maturity or research need of the production. Secondly, the physico-chemical properties are compared with respect to specific energy, energy density, as well as those properties relevant to the combustion of the fuels, i.e., autoignition delay time, adiabatic flame temperature, laminar flame speed and extinction strain rate. Results show that the physical and combustion properties significantly differ from jet fuel, except for n-octane. The results describe how the different electrofuels perform with respect to important aspects such as fuel and air mass flow rates. In addition, the results help determine mixture properties of the exhaust gas for each electrofuel. Thirdly, a turbine configuration is investigated at a constant operating point to further analyze the drop-in potential of electrofuels in aircraft engines. It is found that electrofuels can generally substitute conventional kerosene-based fuels, but have some downsides in the form of higher structural loads and potentially lower efficiencies. Finally, a preliminary comparative evaluation matrix is developed. It contains specifically those fields for the different proposed electrofuels where special challenges and problematic points are seen that need more research for potential application. Synthetically-produced n-octane is seen as a potential candidate for a future electrofuel where even a drop-in capability is given. For the other fuels, more issues need further research to allow the application as electrofuels in aviation. Specifically interesting could be the combination of hydrogen with ammonia in the far future; however, the research is just at the beginning stage.

The aim of this work is the decomposition, quantification, and analysis of losses related to the axial-gap size effect. Both experimental data and unsteady RANS calculations are investigated for axial gaps equal to 20%, 50% and 80% of the stator axial chord. A framework for identifying sources of loss typical in turbomachinery is derived and utilized for the low-pressure turbine presented. The analysis focuses on the dependency of these losses on the axial-gap variation. It is found that two-dimensional profile losses increase for smaller gaps due to higher wake-mixing losses and unsteady wake-blade interaction. Losses in the end-wall regions, however, decrease for smaller gaps. The total system efficiency can be described by a superposition of individual loss contributions, the optimum of which is found for the smallest gap investigated. It is concluded that these loss contributions are characteristic for the medium aspect-ratio airfoils and operating conditions investigated. This establishes a deeper physical understanding for future investigations into the axial-gap size effect and its interdependency with other design parameters.

Shroud cavities in aero engines are typically formed by a labyrinth seal between the rotating turbine shroud and the stationary casing wall. To mitigate rub-in and reduce weight, the casing often features honeycomb structures above the rotor seal fins. In this paper, the aerodynamic performance of such honeycomb structures is experimentally investigated using a rotating test rig featuring both smooth and honeycomb-tapered casing walls. Measurements show that the discharge coefficient decreases for the honeycomb configuration while losses and subsequent windage heating of the flow increase. A variation in rotational speed reveals additional sensitivities to the local flow field in the swirl chamber. Numerical simulations are conducted and validated using the experiments. A good agreement between the prediction and measurements of the jet via the evolution of pressure across the sealing fins is identified. In contrast, the prediction of losses and integral parameters reveals larger deficits. Empirical correlations from available literature satisfactorily predict the leakage mass flow rate if rotation is low and if the casing is smooth. High rotation and the presence of honeycombs, however, prove challenging and reveal the potential for further improvements. We propose a simple a-posteriori correction that can capture the effect of honeycomb structures on seal discharge by accounting for changes in momentum and flow area.

This paper presents those flow parameters at which coherent structures appear in the blade tip cavities of shrouded turbine blades. To the authors’ knowledge, this is reported for the first time in the open literature. The unsteady flow in a shroud cavity is analysed based on experimental data...

Defects in the hot-gas path of aero engines have been shown to leave typical signatures in the density distribution of the exhaust jet. These signatures are superposed when several defects are present. For improved maintenance and monitoring applications, it is important to not only detect that...

In this paper, the utility and accuracy of Machine Learning (ML) and Deep Learning (DL) methods are investigated for detecting defects in civil aircraft engines. Rather than to disassemble jet engines, the approach investigated in this study utilizes images of the exhaust of jet engines and infers defects in the turbine and burner section. While the proposed DL methods make use of one or two cameras, the ML methods depend on data obtained by extracting the density fields of the Hot Gas Path (HGP). The HPG data are computed from images acquired by an array of cameras. The corresponding ML features are crafted from these density fields. The proposed algorithms employ optimized hyperparameters and separate training as well as validation data sets. The study illustrates the potential of DL methods and the resulting simplification in the necessary instrumentation to accomplish near perfect defect classification outcomes.