Spreading of two-stream supersonic turbulent mixing layers

Chinzei, Nobuo; Masuya, Goro; Komuro, Tomoyuki; Murakami, A.; Kudou, Kenji

doi:10.1063/1.865698

Cited by 243 publications

(55 citation statements)

References 6 publications

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“…One good [3]: (a) Bogdanoff [7]; Papamoschou and Roshko [40]; (b) Chinzei at al. [9]; (c) Samimy and Elliott [48,49]; -nonlinear regression curve from [3] with method to identify the location of a shock is to use a Schlieren based technique to portray shocks and even more weak discontinuities in the fluid (see ref. [21] for more details about flow visualization).…”

Section: Flow Structures and Shockletsmentioning

confidence: 99%

LES of temporally evolving mixing layers by an eighth‐order filter scheme

Hadjadj

Yee

Sjögreen

2012

Numerical Methods in Fluids

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An eighth-order filter method for a wide range of compressible flow speeds (H.C. Yee and B. Sjogreen, Proceedings of ICOSAHOM09, June 22-26, 2009, Trondheim, Norway) are employed for large eddy simulations (LES) of temporally evolving mixing layers (TML) for different convective Mach numbers (M c ) and Reynolds numbers. The high order filter method is designed for accurate and efficient simulations of shock-free compressible turbulence, turbulence with shocklets and turbulence with strong shocks with minimum tuning of scheme parameters. The value of M c considered is for the TML range from the quasi-incompressible regime to the highly compressible supersonic regime. The three main characteristics of compressible TML (the self similarity property, compressibility effects and the presence of large-scale structure with shocklets for high M c ) are considered for the LES study. The LES results using the same scheme parameters for all studied cases agree well with experimental results of , and published direct numerical simulations (DNS) work of Rogers & Moser (1994) and Pantano & Sarkar (2002). Key words: DNS, LES, Homogeneous turbulence, Mixing layer, SWBLI Motivation and ObjectiveFor the last decade there has been an increase in the use of computational fluid dynamics (CFD) in engineering science, not only for fundamental understanding of complex compressible turbulent physics, but also for the development and design of industrial devices. Owing to the recent progress in petascale computing, in tandem with advances in algorithm development for accurate direct numerical simulations (DNS) and large eddy simulations (LES) of shock free compressible turbulence and turbulence with strong shocks, this type of DNS and LES computation has gradually been able to tackle more complex flows physics. Advances in flow visualization tools have paved the way to extracting valuable information from the computed results containing hundreds of terabytes of data. Examples include * This work was performed while the first author was a visiting scholar at the Center for Turbulence Research, Stanford University.

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Section: Flow Structures and Shockletsmentioning

confidence: 99%

LES of temporally evolving mixing layers by an eighth‐order filter scheme

Hadjadj

Yee

Sjögreen

2012

Numerical Methods in Fluids

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“…The success of linear stability analyses of these phenomena, at least for subsonic convective Mach numbers, can perhaps also be understood in the same light, since the dominant surviving mode must abide by the same considerations. Papamoschou and Roshko (1988) find that the compressible shear-layer growth rate, when normalized by the corresponding incompressible flow growth rate estimated at the same velocity and density ratio, is well represented as a function of the isentropically estimated convective Mach number only, say, Al(') i.e., Figure 4 includes the data of Papamoschou and Roshko (1988), shear-layer growthrate estimates computed from the earlier data of Chinzei et al (1986) that were processed to estimate M( ) for each of their runs and normalized to the value of 6)l b(MAic )/6(O) at one point (filled circle), the data of Clemens and Mungal (1990), and the data of Hall et al (1991). The smooth curve in Fig.…”

Section: Compressibility Effectsmentioning

confidence: 99%

Turbulent Free Shear Layer Mixing and Combustion

1991

High-Speed Flight Propulsion Systems

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PasadenaApproved for public release; distribution is unlimited ABSTRACT (Maximum 200 wods)Some experimental data on turbulent free-shear-layer growth, mixing, and chemical reactions are reviewed. The dependence of these phenomena on such fluid and flow parameters as Reynolds number, Schmidt number, and Mach number are discussed, with the aid of some direct consequences deducible from the large-scale organization of the flow as well as from some recent models.14.

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“…The outcome of the added freedom associated with the data from Chinzei et al (1986) is shown in Figure 23. Each data series in this plot supposedly represents the exact same outcome of one experiment, however they were each extracted from a different source.…”

Section: Apparent Dependence On Mixing Layer Thickness Definitionmentioning

confidence: 99%

“…Take the experimental data published by Chinzei et al (1986) as a case example. Since it was published before the theoretical work of Papamoschou and Roshko (1988), the measured mixing layer growth rates were normalized according to a process that is quite different from the presently accepted incompressible growth rate models.…”

Section: Apparent Dependence On Mixing Layer Thickness Definitionmentioning

confidence: 99%

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On the Growth Rate of Turbulent Mixing Layers: A New Parametric Model

Freeman¹

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A new parametric model for the growth rate of turbulent mixing layers is proposed. A database of experimental and numerical mixing layer studies was extracted from the literature to support this effort. The domain of the model was limited to planar, spatial, nonreacting, free shear layers that were not affected by artificial mixing enhancement techniques. The model is split into two parts which were each tuned to optimally fit the database; equations for an incompressible growth rate were derived from the error function velocity profile, and a function for a compressibility factor was generalized from existing theory on the convective Mach number. The compressible model is supported by a detailed evaluation of the currently accepted models and practices, including error analysis of the convective Mach number derivation and a critical analysis of Slessor's renormalization technique which affected his 1998 compressibility parameter. Analysis of the database suggested that a distinction should be made between thickness definitions that are based on the velocity profile and those based on the density profile. Additionally, the accumulation of different normalization approaches throughout the literature was shown to have introduced non-physical variance in the trends. Resolution of this issue through a consistent normalization process has greatly improved the normality and scatter of the data and the goodness-of-fit of the models, resulting in R 2 = 0.9856 for the incompressible model and R 2 = 0.9004 for the compressible model.

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Spreading of two-stream supersonic turbulent mixing layers

Cited by 243 publications

References 6 publications

LES of temporally evolving mixing layers by an eighth‐order filter scheme

LES of temporally evolving mixing layers by an eighth‐order filter scheme

Turbulent Free Shear Layer Mixing and Combustion

On the Growth Rate of Turbulent Mixing Layers: A New Parametric Model

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