Turbulence statistics and coherent structures in compressible channel flow

Yao, Jie; Hussain, Fazle

doi:10.1103/physrevfluids.5.084603

Cited by 40 publications

(90 citation statements)

References 63 publications

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“…for higher Re * τ (Yao & Hussain 2020). Figure 8 shows the distribution of the normalized intensity of wall pressure fluctuations as a function of the Reynolds number.…”

Section: Spatial Evolution Of Mean and Turbulence Propertiesmentioning

confidence: 99%

“…Given that the boundary-layer turbulence would be better established at a higher Reynolds number and less likely to suffer from low-Reynolds-number effects, it would also be beneficial to reevaluate the compressibility transformations at a high Reynolds number and to assess whether or not the existing compressibility transformations would perform better at higher Reynolds numbers. A recent study of compressible channel flows for bulk Mach numbers between 0.8 and 1.5 and bulk Reynolds numbers in the range 3000-34 000 by Yao & Hussain (2020) has found that the differences between incompressible and compressible flows (e.g. the increase in the streamwise Reynolds stress peak with increasing Mach number) become less prominent as the Reynolds number increases.…”

Section: Introductionmentioning

confidence: 99%

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Direct numerical simulation of hypersonic turbulent boundary layers: effect of spatial evolution and Reynolds number

2022

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Direct numerical simulations (DNS) are performed to investigate the spatial evolution of flat-plate zero-pressure-gradient turbulent boundary layers over long streamwise domains ( ${>}300\delta _i$ , with $\delta _i$ the inflow boundary-layer thickness) at three different Mach numbers, $2.5$ , $4.9$ and $10.9$ , with the surface temperatures ranging from quasiadiabatic to highly cooled conditions. The settlement of turbulence statistics into a fully developed equilibrium state of the turbulent boundary layer has been carefully monitored, either based on the satisfaction of the von Kármán integral equation or by comparing runs with different inflow turbulence generation techniques. The generated DNS database is used to characterize the streamwise evolution of multiple important variables in the high-Mach-number, cold-wall regime, including the skin friction, the Reynolds analogy factor, the shape factor, the Reynolds stresses, and the fluctuating wall quantities. The data confirm the validity of many classic and newer compressibility transformations at moderately high Reynolds numbers (up to friction Reynolds number $Re_\tau \approx 1200$ ) and show that, with proper scaling, the sizes of the near-wall streaks and superstructures are insensitive to the Mach number and wall cooling conditions. The strong wall cooling in the hypersonic cold-wall case is found to cause a significant increase in the size of the near-wall turbulence eddies (relative to the boundary-layer thickness), which leads to a reduced-scale separation between the large and small turbulence scales, and in turn to a lack of an outer peak in the spanwise spectra of the streamwise velocity in the logarithmic region.

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“…for higher Re * τ (Yao & Hussain 2020). Figure 8 shows the distribution of the normalized intensity of wall pressure fluctuations as a function of the Reynolds number.…”

Section: Spatial Evolution Of Mean and Turbulence Propertiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Direct numerical simulation of hypersonic turbulent boundary layers: effect of spatial evolution and Reynolds number

2022

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“…The most successful MVT that accounts for the near-wall viscosity gradient was independently developed by Trettel et al (3) and Patel et al (7). However, despite their initial success in isothermal CTBL cases (15,16) with low semi-local Reynolds, Re * , the scaling remains unsuccessful for cases with increasing Re * where multiple studies (14,17) report a large scatter in the log layer intercept and the slope for such cases. Where Re * = Reτ ρ/ρw/(µ/µw), Reτ = ρwuτ δ/µw is the frictionvelocity based Reynolds number, ρ is density, uτ = τw/ρw is the friction-velocity, δ is the boundary layer thickness, τw = µ ∂u ∂z |w is the wall shear stress, µ is the dynamic viscosity, u is the streamwise velocity, and z is the wall normal coordinate.…”

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confidence: 99%

Compressible Boundary Layer Velocity Transformation Based on a Generalized Form of the Total Stress

Lee¹,

Martin²,

Williams³

2021

Preprint

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A new mean velocity transformation for compressible boundary layer flow is derived. We identify the effects of density fluctuations and integrate these effects into the derivation of the transformation. Indepth analysis of compressibility effects from density fluctuations is enabled by direct numerical simulation data existing in the literature and from the CRoCCo laboratory. The compressible and incompressible flow data include wall-cooling, semi-local Reynolds numbers ranging from 800 to 34000 and Mach numbers ranging from subsonic to 12. The role, significance and physical mechanisms connecting density fluctuations to the momentum balance and to the viscous, turbulent and total stresses are presented. Density-fluctuation corrected formulations are derived from these insights. We identify the significant properties that thus-far are neglected in the derivation of velocity transformations: (1) the Mach-invariance of the nearwall momentum balance of the density-fluctuation-corrected total stress, and (2) the Mach-invariance of the relative contributions by the density-fluctuation-corrected viscous and Reynolds stresses to the total stress. The proposed velocity transformation integrates both properties into a single transformation equation and successfully demonstrates a collapsing of all currently considered compressible cases onto to the incompressible law of the wall. Analyses of the log-layer intercept and the slope demonstrate that the transformation parameters are well within the bounds reported for incompressible data. Based on the physics embedded in the two scaling properties, the success of the newly proposed transformation is attributed to considering the effect of the viscous stress, Reynolds stress, mean density and, lastly, density fluctuations, in a single transformation form.

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“…Many aspects of the complex interactions between compressibility effects and turbulence structures are still not thoroughly understood. Traditional free shear layers (such as jets, wakes and mixing layers) and Poiseuille/Couette type flows (such as channel flows) have been utilized to study compressible turbulence [13,15,16]. Spatiallydeveloping mixing layer simulations are computationally expensive and are sensitive to farfield and inflow/outflow boundary conditions [16,17], but the basic compressibility effects are captured in temporal simulation as, for example, discussed by Vreman et al [18].…”

mentioning

confidence: 99%

“…Traditional free shear layers (such as jets, wakes and mixing layers) and Poiseuille/Couette type flows (such as channel flows) have been utilized to study compressible turbulence [13,15,16]. Spatiallydeveloping mixing layer simulations are computationally expensive and are sensitive to farfield and inflow/outflow boundary conditions [16,17], but the basic compressibility effects are captured in temporal simulation as, for example, discussed by Vreman et al [18]. A drawback of the temporal approach is that the shear layer thickens as time progresses and large structures rapidly fill the computational domain.…”

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confidence: 99%

Direct numerical simulation of compressible turbulence in a counter-flow channel configuration

et al. 2021

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Counter-flow configurations, whereby two streams of fluid are brought together from opposite directions, are highly efficient mixers due to the high turbulence intensities that can be maintained.In this paper, a simplified version of the problem is introduced that is amenable to direct numerical simulation. The resulting turbulent flow problem is confined between two walls, with one non-zero mean velocity component varying in the space direction normal to the wall, corresponding to a simple shear flow. Compared to conventional channel flows, the mean flow is inflectional and the maximum turbulence intensity relative to the maximum mean velocity is nearly an order of magnitude higher. The numerical requirements and turbulence properties of this configuration are first determined. The Reynolds shear stress is required to vary linearly by the imposed forcing, with a peak at the channel centreline. A similar behaviour is observed for the streamwise Reynolds stress, the budget of which shows an approximately uniform distribution of dissipation, with large contributions from production, pressure-strain and turbulent diffusion. A viscous sublayer is obtained near the walls and with increasing Reynolds number small-scale streaks in the streamwise momentum are observed, superimposed on the large-scale structures that buffet this region. When the peak local mean Mach number reaches 0.55, turbulent Mach numbers of 0.6 are obtained, indicating that this flow configuration can be useful to study compressibility effects on turbulence. I. INTRODUCTIONCounter-flow shear layers, also known as counter-current flows, have been previously recognised as efficient flow configurations for mixing [1-4], thrust vectoring (flow control)[5-7] and combustion [8,9]. Strykowski and Wilcoxon [2] showed that counter-current configurations could be used to control axisymmetric jet flows and enhance their mixing characteristics using global oscillations produced by self-excitation within shear layers. Strykowski and colleagues [8,9] also showed that a counter-current flow configuration can be used in swirl combustors to efficiently control the turbulent burning velocity (flame speed) and reduce pollutant emissions. The use of counter-flow configurations for fundamental flow studies was first recognised by Humphrey and Li [1] and later by various researchers including most notably Forliti et al. [3] who studied planar shear layers. A shear layer develops

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Turbulence statistics and coherent structures in compressible channel flow

Cited by 40 publications

References 63 publications

Direct numerical simulation of hypersonic turbulent boundary layers: effect of spatial evolution and Reynolds number

Direct numerical simulation of hypersonic turbulent boundary layers: effect of spatial evolution and Reynolds number

Compressible Boundary Layer Velocity Transformation Based on a Generalized Form of the Total Stress

Direct numerical simulation of compressible turbulence in a counter-flow channel configuration

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