Uncovering Hidden Features from High-speed Schlieren Images

Schlieren imaging is a widely used flow visualization technique that has seen rapid improvements in spatio-temporal resolution and fidelity in recent years. Schlieren data is often used with data-driven approaches such as Spectral Proper Orthogonal Decomposition (SPOD) to extract frequency-specific coherent structures in the flow. This investigation presents a sifting procedure based on Momentum Potential Theory (MPT) that substantially improves results from the subsequent application of data-driven approaches, with a focus on the hidden irrotational signature of the turbulent flow structures. The key step involves the extraction of an irrotational scalar potential, which is exact even in the presence of large fluctuations and non-linearities. When the resulting sifted data are combined with SPOD, the MPT-filtered schlieren images offer superior insights that are not evident in raw schlieren data. Two examples illustrate the unique strengths of the approach. First, when applied to a screeching twin rectangular jet configuration, the resulting SPOD modes independently capture the flapping screech mode and the symmetric super-directive noise radiation. For a transitioning hypersonic boundary layer, the MPT-filtered SPOD modes capture the acoustic and thermal characteristics associated with second-mode transition. The method is free of geometrical constraints and problem-dependent parameters and has the potential to greatly enhance the use of high-speed diagnostics with feedback control implications.