Moritz Sieber

The global dynamics of cloud cavitation are not always obvious; cloud cavitation may exhibit chaotic, multimodal, and intermittent behaviour, where dominant flow structures are hidden to the naked eye. To address this, spectral proper orthogonal decomposition (SPOD) is applied, a method that can continuously transition between Proper Orthogonal Decomposition (POD) and Discrete Fourier Transformation (DFT)/ Dynamic Mode Decomposition (DMD). This provides the opportunity to break down the complex dynamics of interacting and transient processes into interpretable modal bases. Experiments were conducted in a high-speed cavitation tunnel using a two-dimensional NACA 0015 hydrofoil at a fixed Reynolds number of 8 * 105, and an incidence of 12° for varying cavitation numbers. The cavitation was recorded using a synchronised dual-camera setup with simultaneously captured pressure signals. Shockwave-driven and re-entrant flow-driven cloud shedding are identified, as well as the transition regime in between, exhibiting more complex behaviour. SPOD modes allow for a frequency and amplitude variation, which successfully decomposes the data into the dominant modes, whereas classical modal decomposition methods such as POD and DMD do not provide interpretable decompositions. SPOD grants access to a transient analysis of the data via the SPOD time coefficients. We validate the SPOD results using space-time plots and Power Spectral Density (PSD) of the pressure signals, being in good agreement with the SPOD spatial modes and time coefficients. The complex time coefficients give access to instantaneous mode frequencies and allow calculating a standard deviation of the frequency modulation of the modes. The findings provide a deep insight into the spatial and temporal behaviour of cloud cavitation and support the understanding of its physics.

Physics-informed neural networks (PINNs) can be used to solve partial differential equations (PDEs) and identify hidden variables by incorporating the governing equations into neural network training. In this study, we apply PINNs to the assimilation of turbulent mean flow data and investigate the method's ability to identify inaccessible variables and closure terms from sparse data. Using high-fidelity large-eddy simulation data and particle image velocimetry measured mean fields, we show that PINNs are suitable for simultaneously identifying multiple missing quantities in turbulent flows and providing continuous and differentiable mean fields consistent with the provided PDEs. In this way, consistent and complete mean states can be provided, which are essential for linearized mean field methods. The presented method does not require a grid or discretization scheme, is easy to implement, and can be used for a wide range of applications, making it a very promising tool for mean field-based methods in fluid mechanics.

Vortex induced vibrations (VIV) of a circular cylinder have been investigated experimentally using a cyberphysical apparatus with m∗ = 8, ζ = 0.005, and Re = 4000. This study considers the application of proper orthogonal decomposition (POD) and spectral POD (SPOD) analysis to the wake dynamics of the low-mass-ratio VIV of a circular cylinder in the lower branch at U∗ = 7.5. SPOD has been previously shown to better separate frequency-centered modal dynamics, compared to POD. Coherent POD and SPOD modes were compared and the newly separated third SPOD mode pair was found to have a periodicity characteristic of vortex shedding and a peak in the temporal coefficient spectra at St = f D/U∞ = 0.2248. The literature has identified that the wake dynamics within the lower branch are synchronized to the cylinder motion; however the present study suggests that some hidden dynamics persist at the Strouhal frequency. Low order models based on the first eight POD and SPOD modes were compared, and it was found that the filtering operation in SPOD removes the uncorrelated stochastic energy component of the POD modes while producing a comparable representation of the coherent deterministic part of the wake dynamics. Using SPOD to separate the distinct frequency-centered dynamics into unique, interpretable mode pairs will simplify future efforts to develop sparse dynamical models of the flow.

The reduction of polluting NOx emission remains a driving factor in the design process of swirl-stabilized combustion systems to meet strict legislative restrictions. In reacting swirl flows, hydrodynamic coherent structures, such as periodic large-scale vortices in the shear layer, induce zones with increased heat release rate fluctuations in connection with temperature peaks,