scholarly journals Influence of Surface Anisotropy on Turbulent Flow Over Irregular Roughness

2019 ◽  
Vol 104 (2-3) ◽  
pp. 331-354 ◽  
Author(s):  
Angela Busse ◽  
Thomas O. Jelly
Keyword(s):  
Reynolds Stress ◽  
Surface Generation ◽  
Anisotropy Ratio ◽  
Surface Anisotropy ◽  
Outer Flow ◽  
Correlation Lengths ◽  
Stress Anisotropy ◽  
Dispersive Stress ◽  
The Mean ◽  
Near Wall

AbstractThe influence of surface anisotropy upon the near-wall region of a rough-wall turbulent channel flow is investigated using direct numerical simulation (DNS). A set of nine irregular rough surfaces with fixed mean peak-to-valley height, near-Gaussian height distributions and specified streamwise and spanwise correlation lengths were synthesised using a surface generation algorithm. By defining the surface anisotropy ratio (SAR) as the ratio of the streamwise and spanwise correlation lengths of the surface, we demonstrate that surfaces with a strong spanwise anisotropy (SAR < 1) can induce an over 200% increase in the roughness function ΔU+, compared to their streamwise anisotropic (SAR > 1) equivalent. Furthermore, we find that the relationship between the roughness function ΔU+ and the SAR parameter approximately follows an exponentially decaying function. The statistical response of the near-wall flow is studied using a “double-averaging” methodology in order to distinguish form-induced “dispersive” stresses from their turbulent counterparts. Outer-layer similarity is recovered for the mean velocity defect profile as well as the Reynolds stresses. The dispersive stresses all attain their maxima within the roughness canopy. Only the streamwise dispersive stress reaches levels that are comparable to the equivalent Reynolds stress, with surfaces of high SAR attaining the highest levels of streamwise dispersive stress. The Reynolds stress anisotropy also shows distinct differences between cases with strong streamwise anisotropy that stay close to an axisymmetric, rod-like state for all wall-normal locations, compared to cases with spanwise anisotropy where an axisymmetric, disk-like state of the Reynolds stress anisotropy tensor is observed around the roughness mean plane. Overall, the results from this study underline that the drag penalty incurred by a rough surface is strongly influenced by the surface topography and highlight its impact upon the mean momentum deficit in the outer flow as well as the Reynolds and dispersive stresses within the roughness layer.

The autonomous cycle of near-wall turbulence

1999 ◽  
Vol 389 ◽  
pp. 335-359 ◽  
Author(s):  
JAVIER JIMÉNEZ ◽  
ALFREDO PINELLI
Keyword(s):  
Reynolds Numbers ◽  
Wall Turbulence ◽  
Wall Region ◽  
Streamwise Vortices ◽  
Outer Flow ◽  
Turbulent Fluctuations ◽  
The Mean ◽  
Turbulence Generation ◽  
Near Wall Turbulence ◽  
Near Wall

Numerical experiments on modified turbulent channels at moderate Reynolds numbers are used to differentiate between several possible regeneration cycles for the turbulent fluctuations in wall-bounded flows. It is shown that a cycle exists which is local to the near-wall region and does not depend on the outer flow. It involves the formation of velocity streaks from the advection of the mean profile by streamwise vortices, and the generation of the vortices from the instability of the streaks. Interrupting any of those processes leads to laminarization. The presence of the wall seems to be only necessary to maintain the mean shear. The generation of secondary vorticity at the wall is shown to be of little importance in turbulence generation under natural circumstances. Inhibiting its production increases turbulence intensity and drag.

Mean dynamics and transition to turbulence in oscillatory channel flow

2019 ◽  
Vol 880 ◽  
pp. 864-889
Author(s):  
Alireza Ebadi ◽  
Christopher M. White ◽  
Ian Pond ◽  
Yves Dubief
Keyword(s):  
Reynolds Stress ◽  
Channel Flow ◽  
Layer Structure ◽  
Development Stage ◽  
Transition To Turbulence ◽  
Nonlinear Development ◽  
The Mean ◽  
Balance Of Forces ◽  
Turbulent Wall ◽  
Near Wall

The mean dynamics in oscillatory channel flow is examined to investigate the dynamical mechanisms underlying the transition to turbulence in oscillatory wall-bounded flow. The analyses employ direct numerical simulation data acquired at three Stokes Reynolds numbers: $Re_{s}=648$, 801 and 1009, where the lower $Re_{s}$ flow is transitional over the entire cycle and the two higher $Re_{s}$ flows exhibit flow characteristics similar to steady turbulent wall-bounded flow during part of the cycle. The flow evolution over a half-period of oscillation for all three $Re_{s}$ is as follows: near-wall streamwise velocity streaks develop during the early accelerating portion of the cycle; then at some later point in the cycle that depends on $Re_{s}$, the near-wall streaks breakdown (demarking the onset of the nonlinear development stage), and the near-wall Reynolds stress grows explosively; the Reynolds stress remains elevated for part of the cycle before diminishing (yet remaining finite) during the late decelerating portion of the cycle. This process is then repeated indefinitely. The present findings demonstrate that transition to turbulence occurs when the nonlinear development stage begins during the accelerating portion of the cycle. This crucially leads to the diminishing importance of the centreline momentum source, the emergence of a locally accelerating/decelerating internal layer centred about the edge of the Stokes layer and the wall-normal rearrangement of the mean forces prior to the start of the decelerating portion of the cycle. The rearrangement of mean forces culminates in a four layer structure in the mean balance of forces. This is significant on a number of accounts since empirical and theoretical evidence suggests that the formation of a four layer structure is an important characteristic of a self-similar hierarchal structure that underlies logarithmic dependence of the mean velocity profile in steady turbulent wall-bounded flows (Klewicki et al., J. Fluid Mech., vol. 638, 2009, pp. 73–93). When the nonlinear development stage begins during the decelerating portion of the cycle (i.e. at $Re_{s}=648$), a four layer structure is not observed in the mean balance of forces and the flow remains weakly transitional over the entire cycle.

Direct numerical simulation of turbulent Taylor–Couette flow

2007 ◽  
Vol 579 ◽  
pp. 227-270 ◽  
Author(s):  
M. BILSON ◽  
K. BREMHORST
Keyword(s):  
Numerical Simulation ◽  
Direct Numerical Simulation ◽  
Reynolds Stress ◽  
Velocity Fluctuation ◽  
Stagnation Points ◽  
The Mean ◽  
Taylor Couette ◽  
Secondary Vortices ◽  
Near Wall ◽  
Stress Budgets

Direct numerical simulation (DNS) is used to investigate turbulent Taylor–Couette (TC) flow. A simulation was run for a Reynolds number of 3200 in an apparatus with a radius ratio of η = 0.617 and an aspect ratio of 4.58, which assumed a vortex pair wavelength of 2.29. Results reported include the mean velocity, velocity fluctuation intensities, Reynolds stress budgets, and visualizations of the instantaneous velocity fluctuation field. Secondary near-wall vortex pairs are observed near to the cylinder in addition to the Taylor vortex (TV) motion. Weaker evidence of secondary vortices is found at the outer cylinder where a banded structure has been identified. The azimuthal wall shear stress component shows large peaks and valleys at stagnation points on the surface of both cylinders where flow from neighbouring vortices impacts on the respective wall. These stagnation points correspond to locations where the secondary vortices have been identified. The effect of the mean TV motion is reflected in the Reynolds stress budgets which are similar to but more complex than those of two-dimensional boundary layers. Visualization of the turbulent velocity fluctuations reveals near-wall streaks at the inner cylinder.

An experimental investigation of a three-dimensional turbulent boundary layer in an ‘S’-shaped duct

1999 ◽  
Vol 393 ◽  
pp. 175-213 ◽  
Author(s):  
J. M. BRUNS ◽  
H. H. FERNHOLZ ◽  
P. A. MONKEWITZ
Keyword(s):  
Boundary Layer ◽  
Turbulent Boundary Layer ◽  
Reynolds Stress ◽  
Test Section ◽  
Three Dimensional ◽  
Hot Wire ◽  
Freestream Velocity ◽  
Wire Probe ◽  
The Mean ◽  
Near Wall

This paper describes the evolution of an incompressible turbulent boundary layer on the flat wall of an ‘S’-shaped wind tunnel test section under the influence of changing streamwise and spanwise pressure gradients. The unit Reynolds number based on the mean velocity at the entrance of the test section was fixed to 106 m−1, resulting in Reynolds numbers Reδ2, based on the streamwise momentum thickness and the local freestream velocity, between 3.9 and 11 × 103. The particular feature of the experiment is the succession of two opposite changes of core flow direction which causes a sign change of the spanwise pressure gradient accompanied by a reversal of the spanwise velocity component near the wall, i.e. by the formation of so-called cross-over velocity profiles. The aim of the study is to provide new insight into the development of the mean and fluctuating flow field in three-dimensional pressure-driven boundary layers, in particular of the turbulence structure of the near-wall and the cross-over region.Mean velocities, Reynolds stresses and all triple correlations were measured with a newly developed miniature triple-hot-wire probe and a near-wall hot-wire probe which could be rotated and traversed through the test plate. Skin friction measurements were mostly performed with a wall hot-wire probe. The data from single normal wires extend over wall distances of y+ [gsim ] 3 (in wall units), while the triple-wire probe covers the range y+ [gsim ] 30. The data show the behaviour of the mean flow angle near the wall to vary all the way to the wall. Then, to interpret the response of the turbulence to the pressure field, the relevant terms in the Reynolds stress transport equations are evaluated. Finally, an attempt is made to assess the departure of the Reynolds stress profiles from local equilibrium near the wall.

scholarly journals Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

2016 ◽  
Vol 807 ◽  
pp. 155-166 ◽  
Author(s):  
Julia Ling ◽  
Andrew Kurzawski ◽  
Jeremy Templeton
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Reynolds Stress ◽  
Network Architecture ◽  
Eddy Viscosity ◽  
Deep Neural Networks ◽  
Test Cases ◽  
Neural Network Architecture ◽  
Stress Anisotropy ◽  
Anisotropy Tensor

There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS) turbulence models that are informed by and can represent a richer set of turbulence physics. This paper presents a method of using deep neural networks to learn a model for the Reynolds stress anisotropy tensor from high-fidelity simulation data. A novel neural network architecture is proposed which uses a multiplicative layer with an invariant tensor basis to embed Galilean invariance into the predicted anisotropy tensor. It is demonstrated that this neural network architecture provides improved prediction accuracy compared with a generic neural network architecture that does not embed this invariance property. The Reynolds stress anisotropy predictions of this invariant neural network are propagated through to the velocity field for two test cases. For both test cases, significant improvement versus baseline RANS linear eddy viscosity and nonlinear eddy viscosity models is demonstrated.

Asymptotic properties of the overall sound pressure level of subsonic jet flows using isotropy as a paradigm

2010 ◽  
Vol 664 ◽  
pp. 510-539 ◽  
Author(s):  
M. Z. AFSAR
Keyword(s):  
Reynolds Stress ◽  
Sound Pressure Level ◽  
Small Angle ◽  
Sound Pressure ◽  
Asymptotic Properties ◽  
Jet Noise ◽  
Pressure Level ◽  
Mean Flow ◽  
The Mean ◽  
Jet Axis

Measurements of subsonic air jets show that the peak noise usually occurs when observations are made at small angles to the jet axis. In this paper, we develop further understanding of the mathematical properties of this peak noise by analysing the properties of the overall sound pressure level with an acoustic analogy using isotropy as a paradigm for the turbulence. The analogy is based upon the hyperbolic conservation form of the Euler equations derived by Goldstein (Intl J. Aeroacoust., vol. 1, 2002, p. 1). The mean flow and the turbulence properties are defined by a Reynolds-averaged Navier–Stokes calculation, and we use Green's function based upon a parallel mean flow approximation. Our analysis in this paper shows that the jet noise spectrum can, in fact, be thought of as being composed of two terms, one that is significant at large observation angles and a second term that is especially dominant at small observation angles to the jet axis. This second term can account for the experimentally observed peak jet noise (Lush, J. Fluid Mech., vol. 46, 1971, p. 477) and was first identified by Goldstein (J. Fluid Mech., vol. 70, 1975, p. 595). We discuss the low-frequency asymptotic properties of this second term in order to understand its directional behaviour; we show, for example, that the sound power of this term is proportional to the square of the mean velocity gradient. We also show that this small-angle shear term does not exist if the instantaneous Reynolds stress source strength in the momentum equation itself is assumed to be isotropic for any value of time (as was done previously by Morris & Farrasat, AIAA J., vol. 40, 2002, p. 356). However, it will be significant if the auto-covariance of the Reynolds stress source, when integrated over the vector separation, is taken to be isotropic in all of its tensor suffixes. Although the analysis shows that the sound pressure of this small-angle shear term is sensitive to the statistical properties of the turbulence, this work provides a foundation for a mathematical description of the two-source model of jet noise.

scholarly journals Measurements and Characterization of Turbulence in the Tip Region of an Axial Compressor Rotor

2017 ◽  
Vol 139 (12) ◽  
Author(s):  
Yuanchao Li ◽  
Huang Chen ◽  
Joseph Katz
Keyword(s):  
Strain Rate ◽  
Shear Layer ◽  
Turbulent Flows ◽  
Reynolds Stress ◽  
Suction Side ◽  
Stress And Strain ◽  
Spatial And Temporal Variability ◽  
Mean Flow ◽  
The Mean ◽  
Stress And Strain Rate

Modeling of turbulent flows in axial turbomachines is challenging due to the high spatial and temporal variability in the distribution of the strain rate components, especially in the tip region of rotor blades. High-resolution stereo-particle image velocimetry (SPIV) measurements performed in a refractive index-matched facility in a series of closely spaced planes provide a comprehensive database for determining all the terms in the Reynolds stress and strain rate tensors. Results are also used for calculating the turbulent kinetic energy (TKE) production rate and transport terms by mean flow and turbulence. They elucidate some but not all of the observed phenomena, such as the high anisotropy, high turbulence levels in the vicinity of the tip leakage vortex (TLV) center, and in the shear layer connecting it to the blade suction side (SS) tip corner. The applicability of popular Reynolds stress models based on eddy viscosity is also evaluated by calculating it from the ratio between stress and strain rate components. Results vary substantially, depending on which components are involved, ranging from very large positive to negative values. In some areas, e.g., in the tip gap and around the TLV, the local stresses and strain rates do not appear to be correlated at all. In terms of effect on the mean flow, for most of the tip region, the mean advection terms are much higher than the Reynolds stress spatial gradients, i.e., the flow dynamics is dominated by pressure-driven transport. However, they are of similar magnitude in the shear layer, where modeling would be particularly challenging.

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