4.4 Article

Passive Scalar Transport in a Turbulent Mixing Layer

期刊

FLOW TURBULENCE AND COMBUSTION
卷 85, 期 1, 页码 1-24

出版社

SPRINGER
DOI: 10.1007/s10494-010-9249-4

关键词

Turbulent mixing layer; Passive scalar transport; Large-eddy simulation

资金

  1. NSF [CTS-961862]

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This paper describes a combined experimental and numerical study of scalar transport in spatially developing, two-stream, turbulent mixing layers with velocity ratios of approximately 2:1. The experimental mixing layer was created by an S-shaped splitter plate mounted in a wind tunnel, and the concentration field was realized by releasing incense smoke into the high-speed side boundary layer above the splitter plate. Simultaneous measurements of the velocity and concentration fields were performed. A 12-sensor hot-wire probe was used to measure the velocity field and its gradients, while the concentration field was recorded with digital photographs of the laser-illuminated smoke. In parallel, a large-eddy simulation (LES) of the spatially developing mixing layer was carried out. Auxiliary turbulent boundary layer LES were used to provide high quality inflow boundary conditions for the velocity and concentration fields. By synchronizing the velocity and concentration measurements, concentration fluxes were also determined. Octant analysis based on the sign combinations of the velocity and concentration fuctuations was performed on the flux data to investigate the scalar transport processes. It was found that octants compatible with mean gradient transport of the scalar contribute most to the scalar fluxes. Conditional planar averages of scalar and momentum fluxes were obtained to determine their spatial distribution with respect to the organized roller and rib vortices of the mixing layer, and distinct patterns were observed. The simulation provided additional insight about the flow and scalar flux distribution topology. This topology was found to be partially compatible with simple models of roller and rib vortices that transport the scalar in a mean gradient sense.

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