Effect of boundary layer thickness on transverse sonic jet mixing in a supersonic turbulent crossflow

Abstract

The surface boundary through which a sonic jet in supersonic cross flow is injected is shown to have a significant effect on the size, penetration, and mixing characteristics of the jet plume. A circular, high-pressure, sonic jet is injected into a M = 3.4 supersonic crossflow through a well-characterized turbulent boundary layer of two different thicknesses (δ/d = 0.6 and 6.1), with variable momentum ratios (J = 1.2, 2.6, and 5). Planar laser Mie scattering of condensed ethanol droplets is used to quantitatively image the injected fluid concentration in both side and end-views at multiple downstream locations. Jet penetration, plume area, and characteristic size and location of regions of intense mixing are compared. The jets injected through the thicker boundary layer are shown to have significantly enhanced jet penetration (∼50%), spread (∼100%), and mixing intensity (∼100%, especially in the near-field) over a wider area of the jet plume. Additionally, characterization of mixing is examined using the variance in the concentration field as well as probability density functions of concentration determined along contours of constant jet fluid concentration. From these results, the jet injections associated with the thicker boundary layer transition from shear dominated mixing zones on the windward side to more distributed mixing zones throughout the plume at earlier downstream locations and show influence of interactions between boundary layer vorticity and vortical structures within the jet leading to larger lateral expansion.

Turbulent mixing and trajectories of jets in a supersonic cross-flow with different injectants

“…(2016), achieve better trajectory collapse using a scaling, suggesting that jet operating conditions have some influence on the ideal scaling parameter. Recently, in a compressible JISCF study, Pizzaia & Rossmann (2018) had success collapsing their jet trajectories using the scaling. Thus, this study favours the latter scaling, but will assess potential improvements in trajectory correlations using the scaling, as well.…”

“…The data sets by Pizzaia & Rossmann (2018) are an exception, but that is not too surprising as they are the only ones reported for normalized jet fluid signal values and significant variation in the boundary layer effects . However, the Pizzaia & Rossmann (2018) data sets also show clearly that the Rothstein & Wantuck (1992) scaling does not explicitly consider differences in , i.e. the Pizzaia & Rossmann (2018) trajectories cluster by values, with the higher cases lying around and the thinner boundary layer cases lying around .…”

“…However, the Pizzaia & Rossmann (2018) data sets also show clearly that the Rothstein & Wantuck (1992) scaling does not explicitly consider differences in , i.e. the Pizzaia & Rossmann (2018) trajectories cluster by values, with the higher cases lying around and the thinner boundary layer cases lying around .

Figure 16.Trajectory collapse using the correlation reported by Rothstein & Wantuck (1992); dotted line (blue) Pizzaia & Rossmann (2018), solid line with rectangle (purple) Sun & Hu (2018), circle (black) Lin et al.

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“…the Pizzaia & Rossmann (2018) trajectories cluster by values, with the higher cases lying around and the thinner boundary layer cases lying around .

Figure 16.Trajectory collapse using the correlation reported by Rothstein & Wantuck (1992); dotted line (blue) Pizzaia & Rossmann (2018), solid line with rectangle (purple) Sun & Hu (2018), circle (black) Lin et al. (2010), (green) Portz & Segal (2006), dash-dotted line (orange) Rothstein & Wantuck (1992), dashed line (red) current study, solid line (black) power-law curve fit.

Figure 17.Trajectory collapse using the correlation reported by Lin et al.

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“…A brief survey of studies in table 4 shows that the majority of results is not reported with a normalization to local maximum values. Out of the selected studies, only Pizzaia & Rossmann (2018) normalize their results and base further evaluation on the normalized data. However, some trajectory definitions are ambiguous, e.g.…”