Tip leakage flow in turbomachinery inherently generates intense unsteady features, named self-excited unsteadiness, which significantly affects the operating stability, aerodynamic efficiency, and noise but has not been well understood. A zonalized large eddy simulation is employed for a linear cascade, with wall-modeled large eddy simulation active only in the tip region. The simulation is well validated with advantages demonstrated for effectively reducing the computational cost while maintaining an equivalent prediction accuracy in the region of interest. The time-averaged and spatial-spectral characteristics of tip leakage vortex (TLV) structures are systematically discussed. The self-excited unsteady processes of TLV include the tip gap separation, the tip leakage and jet-mainstream interaction, the primary tip leakage vortex (PTLV) wandering motion, and the induced separation near end wall. The Spectral Proper Orthogonal Decomposition (SPOD) is used to examine the dominant frequencies and their coherent structures. It is found that these unsteady features change from a single high frequency to multiple lower frequencies due to the PTLV breakdown. The SPOD and correlation analyses reveal that the self-excited unsteadiness originates initially from unsteady vortex separation in the tip gap and is then fed by the interactions between the tip leakage jet and mainstream. The associated unsteady fluctuations are convected along the tip leakage jet trajectory, causing the wandering motion of PTLV core. Based on the revealed unsteadiness sources, a micro-offset tip design is proposed and shown to be an effective solution to reducing the tip flow unsteadiness. This work improves the understanding of tip-leakage-flow dynamics and informs the control of the associated unsteady fluid oscillation and noise.

The accurate prediction of transitional flows is crucial for the industrial turbomachinery design process. While a Reynolds-averaged Navier–Stokes approach inherently brings conceptual weaknesses, large-eddy simulation will still be too expensive in the near future to affordably analyze complex turbomachinery configurations. We introduce a transitional delayed detached-eddy simulation (DDES) model, namely, DDES-[ ], and analyze the numerical results of the compressor cascade V103. A comparison with the fully turbulent DDES approach emphasizes the benefit of coupling DDES with a transition model. Issues with undesired decay of modeled turbulent kinetic energy in the freestream are improved when running DDES-[ ] in combination with the synthetic turbulence generator method. The best results for DDES-[ ] are obtained when changing the inviscid flux solver blending from dynamic to constant mode. We show that DDES-[ ] is capable of predicting the transitional flow through a linear compressor cascade, but we also critically discuss the general concept and results.

We present real-world data processing on measured electron time-of-flight data via neural networks. Specifically, the use of disentangled variational autoencoders on data from a diagnostic instrument for online wavelength monitoring at the free electron laser FLASH in Hamburg. Without a-priori knowledge the network is able to find representations of single-shot FEL spectra, which have a low signal-to-noise ratio. This reveals, in a directly human-interpretable way, crucial information about the photon properties. The central photon energy and the intensity as well as very detector-specific features are identified. The network is also capable of data cleaning, i.e. denoising, as well as the removal of artefacts. In the reconstruction, this allows for identification of signatures with very low intensity which are hardly recognisable in the raw data. In this particular case, the network enhances the quality of the diagnostic analysis at FLASH. However, this unsupervised method also has the potential to improve the analysis of other similar types of spectroscopy data.

The computation of industrial turbomachinery applications is still a big challenge for LES methods due to their requirements in terms of mesh resolution, which lead to high computational costs. Therefore, hybrid RANS/LES methods, such as Detached Eddy-Simulation, are gaining more attention. There is potential in combining the strengths of LES (accuracy where required) and RANS (reduced computational costs where possible) within one modelling approach. Nevertheless, so far, hybrid methods have mostly been developed for fully turbulent flow configurations. The application in transitional flows is still not well considered yet, although the phenomenon laminar-to-turbulent flow transition has a noticeable impact on turbomachinery performance. To capture the transition accurately is a key for improving the predictive quality of hybrid RANS/LES methods. Therefore, we propose a coupling of DDES and the γ-transition model. In this paper, we first introduce the underlying turbulence and transition model. A detailed investigation of how these two models interact with each other resulted in a potential coupled DDES-γ model with a modified k-transport equation. We show the application of DDES-γ and discuss numerical results with two exemplary test cases, namely a flat plat boundary layer with adverse pressure gradient, experimentally investigated by Volino & Hultgren (2000) and the low-pressure turbine cascade T106C, experimentally considered by Michálek et al. (2012). Both test cases represent characteristic flow conditions in turbomachinery such as separation-induced transition under low free-stream turbulence intensity. Main focus is the assessment of the proposed DDES-γ with focus on the improvement of predictive quality, but also potential issues, coming up when coupling DDES and the γ-transition model. The flat plate case serves as a starting point to assess the general behavior of DDES in transitional flows and how the γ-model interacts with the DDES. Secondly, the T106C case revealed also predictive improvements when analyzing turbomachinery-relevant values such as wake losses. For a better assessment, we always put the results into context and compare them with RANS and LES results. This supports the need for more sophisticated approaches such as DDES compared to RANS and illustrates the competitiveness of DDES approaches compared to LES. The considered cases helped to understand the model coupling and yield promising results for the DDES-γ model predicting separation-induced transition, while we showed, that the fully-turbulent DDES failed to capture relevant features for this transition type. After initially assessing DDES- γ for separation-induced transition in this paper, future research needs to address bypass transition to get a better sense for the performance of DDES-γ in transitional flows.