Turbulent kinetic energy equation for a transpired turbulent boundary layer

AIAA Journal ◽  
1971 ◽  
Vol 9 (3) ◽  
pp. 527-529 ◽  
Author(s):  
L. K. ISAACSON ◽  
R. J. CHRISTENSEN
Author(s):  
J. C. Kaimal ◽  
J. J. Finnigan

Turbulent flows like those in the atmospheric boundary layer can be thought of as a superposition of eddies—coherent patterns of velocity, vorticity, and pressure— spread over a wide range of sizes. These eddies interact continuously with the mean flow, from which they derive their energy, and also with each other. The large “energy-containing” eddies, which contain most of the kinetic energy and are responsible for most of the transport in the turbulence, arise through instabilities in the background flow. The random forcing that provokes these instabilities is provided by the existing turbulence. This is the process represented in the production terms of the turbulent kinetic energy equation (1.59) in Chapter 1. The energy-containing eddies themselves are also subject to instabilities, which in their case are provoked by other eddies. This imposes upon them a finite lifetime before they too break up into yet smaller eddies. This process is repeated at all scales until the eddies become sufficiently small that viscosity can affect them directly and convert their kinetic energy to internal energy (heat). The action of viscosity is captured in the dissipation term of the turbulent kinetic energy equation. The second-moment budget equations presented in Chapter 1, of which (1.59) is one example, describe the summed behavior of all the eddies in the turbulent flow. To understand the conversion of mean kinetic energy into turbulent kinetic energy in the large eddies, the handing down of this energy to eddies of smaller and smaller scale in an “eddy cascade” process, and its ultimate conversion to heat by viscosity, we must isolate the different scales of turbulent motion and separately observe their behavior. Taking Fourier spectra and cospectra of the turbulence offers a convenient way of doing this. The spectral representation associates with each scale of motion the amount of kinetic energy, variance, or eddy flux it contributes to the whole and provides a new and invaluable perspective on boundary layer structure. The spectrum of boundary layer fluctuations covers a range of more than five decades: millimeters to kilometers in spatial scales and fractions of a second to hours in temporal scales.


1996 ◽  
Vol 326 ◽  
pp. 151-179 ◽  
Author(s):  
Junhui Liu ◽  
Ugo Piomelli ◽  
Philippe R. Spalart

The interaction between a zero-pressure-gradient turbulent boundary layer and a pair of strong, common-flow-down, streamwise vortices with a sizeable velocity deficit is studied by large-eddy simulation. The subgrid-scale stresses are modelled by a localized dynamic eddy-viscosity model. The results agree well with experimental data. The vortices drastically distort the boundary layer, and produce large spanwise variations of the skin friction. The Reynolds stresses are highly three-dimensional. High levels of kinetic energy are found both in the upwash region and in the vortex core. The two secondary shear stresses are significant in the vortex region, with magnitudes comparable to the primary one. Turbulent transport from the immediate upwash region is partly responsible for the high levels of turbulent kinetic energy in the vortex core; its effect on the primary stress 〈u′v′〉 is less significant. The mean velocity gradients play an important role in the generation of 〈u′v′〉 in all regions, while they are negligible in the generation of turbulent kinetic energy in the vortex core. The pressure-strain correlations are generally of opposite sign to the production terms except in the vortex core, where they have the same sign as the production term in the budget of 〈u′v′〉. The results highlight the limitations of the eddy-viscosity assumption (in a Reynolds-averaged context) for flows of this type, as well as the excessive diffusion predicted by typical turbulence models.


1995 ◽  
Vol 117 (4) ◽  
pp. 564-570
Author(s):  
M. J. Donnelly ◽  
O. K. Rediniotis ◽  
S. A. Ragab ◽  
D. P. Telionis

Laser-Doppler velocimetry is employed to measure the periodic field created by releasing spanwise vortices in a turbulent boundary layer. Phase-averaged vorticity and turbulence level contours are estimated and presented. It is found that vortices with diameter of the order of the boundary layer quickly diffuse and disappear while their turbulent kinetic energy spreads uniformly across the entire boundary layer. Larger vortices have a considerably longer life span and in turn feed more vorticity into the boundary layer.


2000 ◽  
Vol 423 ◽  
pp. 175-203 ◽  
Author(s):  
CHANDRASEKHAR KANNEPALLI ◽  
UGO PIOMELLI

A three-dimensional shear-driven turbulent boundary layer over a flat plate generated by moving a section of the wall in the transverse direction is studied using large-eddy simulations. The configuration is analogous to shear-driven boundary layer experiments on spinning cylinders, except for the absence of curvature effects. The data presented include the time-averaged mean flow, the Reynolds stresses and their budgets, and instantaneous flow visualizations. The near-wall behaviour of the flow, which was not accessible to previous experimental studies, is investigated in detail. The transverse mean velocity profile develops like a Stokes layer, only weakly coupled to the streamwise flow, and is self-similar when scaled with the transverse wall velocity, Ws. The axial skin friction and the turbulent kinetic energy, K, are significantly reduced after the imposition of the transverse shear, due to the disruption of the streaky structures and of the outer-layer vortical structures. The turbulent kinetic energy budget reveals that the decrease in production is responsible for the reduction of K. The flow then adjusts to the perturbation, reaching a quasi-equilibrium three-dimensional collateral state. Following the cessation of the transverse motion, similar phenomena take place again. The flow eventually relaxes back to a two-dimensional equilibrium boundary layer.


2015 ◽  
pp. 239-242
Author(s):  
Jordi Vila-Guerau de Arellano ◽  
Chiel C. van Heerwaarden ◽  
Bart J. H. van Stratum ◽  
Kees van den Dries

2017 ◽  
Vol 835 ◽  
pp. 217-251 ◽  
Author(s):  
Blair A. Johnson ◽  
Edwin A. Cowen

We perform an experimental study to investigate the turbulent boundary layer above a stationary solid glass bed in the absence of mean shear. High Reynolds number $(Re_{\unicode[STIX]{x1D706}}\sim 300)$ horizontally homogeneous isotropic turbulence is generated via randomly actuated synthetic jet arrays (RASJA – Variano & Cowen J. Fluid Mech. vol. 604, 2008, pp. 1–32). Each of the arrays is controlled by a spatio-temporally varying algorithm, which in turn minimizes the formation of secondary mean flows. One array consists of an $8\times 8$ grid of jets, while the other is a $16\times 16$ array. Particle image velocimetry measurements are used to study the isotropic turbulent region and the boundary layer formed beneath as the turbulence encounters a stationary wall. The flow is characterized with statistical metrics including the mean flow and turbulent velocities, turbulent kinetic energy, integral scales and the turbulent kinetic energy transport equation, which includes the energy dissipation rate, production and turbulent transport. The empirical constant in the Tennekes (J. Fluid Mech. vol. 67, 1975, pp. 561–567) model of Eulerian frequency spectra is calculated based on the dissipation results and temporal frequency spectra from acoustic Doppler velocimetry measurements. We compare our results to prior literature that addresses mean shear free turbulent boundary layer characterizations via grid-stirred tank experiments, moving-bed experiments, rapid-distortion theory and direct numerical simulations in a forced turbulent box. By varying the operational parameters of the randomly actuated synthetic jet array, we also find that we are able to control the turbulence levels, including integral length scales and dissipation rates, by changing the mean on-times in the jet algorithm.


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