A Robust Physics-based Method to Filter Coherent Wavepackets from High-speed Schlieren Images

Abstract

A complete understanding of jet dynamics is greatly enabled by accurate separation of the acoustically efficient wavepackets from their higher-energy convecting turbulent counterparts. Recent developments using Momentum Potential Theory (MPT) have successfully isolated the acoustic component in all regions of the jet, to better understand the dynamics as well as to develop wavepacket models. MPT is however a data-intensive method since the inherent Poisson equation solution requires fluctuation quantities in the entire flowfield; as such, it has to date been applied only to numerically obtained data. This work develops an approach to extend its application to extract coherent wavepackets from high-speed schlieren images. The procedure maps pixel intensities from the schlieren to a scaled surrogate for the density gradient integrated along the line of sight. The effectiveness of the procedure is demonstrated using experimental as well as simulated schlieren images representing a wide range of imperfectly-expanded free and impinging jet configurations. When combined with Spectral Proper Orthogonal Decomposition, the method yields modes that accurately capture (i) the Mach wave radiation from a military-style jet, (ii) the mode shapes of the feedback tones in an impinging jet, and (iii) the screech signature in twin rectangular jets, without recourse to user adjusted parameters. This technique has the potential to greatly expand the use of high-speed diagnostics and provide real-time monitoring of the acoustic content of the jet in the nearfield, with feedback control implications. Additionally, although the present study focuses on jets, the general nature of the approach allows a straightforward application to other flows, such as cavity flow-acoustic interactions, among others.