Top PDF Large-eddy Simulation of Turbulent Boundary Layers with Spatially Varying Roughness

Large-eddy Simulation of Turbulent Boundary Layers with Spatially Varying Roughness

Large-eddy Simulation of Turbulent Boundary Layers with Spatially Varying Roughness

an iteration method to ensure that the virtual origin of both the boundary-layer growth and the roughness-scale variation coincide, for given α, the LES produces a statistically steady boundary-layer flow. At sufficiently large Reynolds number Re x , the displacement, momentum and the ninety-nine percent velocity thicknesses all grow approximately linearly with streamwise distance while U ∞ + becomes con- stant. Comparison of the LES and semi-empirical model results for some mean measures of the boundary-layer development show similar qualitative and quan- titative trends. Both the mean-velocity profiles and the streamwise mean-square velocity-fluctuations obtained from the LES show self-similar scaling on the length scale k s = α x with dependence on α. The velocity defect profiles show reason- able collapse using the Rotta-Clauser length scale independent of α. In the sense that U ∞ + , or equivalently the surface skin-friction coefficient, remains streamwise constant, both the semi- empirical model and the LES can be interpreted as repre- senting the fully-rough limit of a Moody-like diagram for the zero-pressure gradient boundary-layer flow. Since the present model indicates a range of admissible values of m, the present work suggests that existence of a class of Moody-like diagrams for turbulent boundary-layer flows in the presence of linear streamwise variation in surface roughness.
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Implicit large eddy simulation of acoustic loading in supersonic turbulent boundary layers

Implicit large eddy simulation of acoustic loading in supersonic turbulent boundary layers

For better interpretation of these figures, the reader is referred to the web (colored) version of this article. In the near-wall region that is critical to our acoustic loading study, the results of the two higher-order iLES show a very good agreement with each other, while the lower-order iLES over-predicts the intensity of the pressure fluctuations (FIG. 8a). Further away from the wall, but still inside the boundary layer, iLES5 produces results closer to the DNS results than iLES9 does. This finding can be a result of the dispersive nature of the highest-order variant, but further examination is necessary before drawing a definitive conclusion.
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Wall-modeled large-eddy simulation of non-equilibrium turbulent boundary layers

Wall-modeled large-eddy simulation of non-equilibrium turbulent boundary layers

4. Conclusions We conducted WMLES to examine the performance of a simple and widely used ODE- based equilibrium wall model in a spatially-developing 3D TBL inside a bent square duct (Schwarz & Bradshaw 1994) and 3D separated flows behind a skewed bump (Ching et al. 2018a,b; Ching & Eaton 2019). From the square duct simulation, the mean velocity profiles and crossflow angles in the outer region were predicted with high accuracy for all the considered mesh resolutions. Some disagreement was observed in the crossflow angles in the bend region where the non-equilibrium effect is most significant. Also, the simulation for the wall-mounted skewed bump showed that this simple ODE-based equilibrium wall model along with an adequate grid resolution around the 3D separation point resulted in reasonable predictions of 3D separating and reattaching flows, including mean velocity distributions, separation bubbles, and vortex structures in the bump wake.
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Implicit large eddy simulation of acoustic loading in supersonic turbulent boundary layers

Implicit large eddy simulation of acoustic loading in supersonic turbulent boundary layers

For better interpretation of these figures, the reader is referred to the web (colored) version of this article. In the near-wall region that is critical to our acoustic loading study, the results of the two higher-order iLES show a very good agreement with each other, while the lower-order iLES over-predicts the intensity of the pressure fluctuations (FIG. 8 a). Further away from the wall, but still inside the boundary layer, iLES5 produces results closer to the DNS results than iLES9 does. This finding can be a result of the dispersive nature of the highest-order variant, but further examination is necessary before drawing a definitive conclusion.
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Large Eddy Simulation of the Flat Plate Turbulent Boundary Layer at High Reynolds Numbers

Large Eddy Simulation of the Flat Plate Turbulent Boundary Layer at High Reynolds Numbers

The DNS studies have addressed several important issues for the numerical simulation of the TBL including high-order nonspectral methods and boundary conditions suitable for spatially developing flows with only one, as opposed to two, homogeneous directions. Due to the additional inhomogeneity in the streamwise direction, progress in the DNS of turbulent boundary layers has been slower, in terms of the Reynolds numbers achieved, compared to the various numerical computations of canonical turbulent flows performed to date. In the case of the transitional flow whose state is laminar in the inflow region, the inflow velocity conditions can be specified relatively easily, but the computational domain needs to be sufficiently long to develop the flow into turbulence. To avoid using an exceeding long domain that includes full transition, it is necessary to provide inflow conditions with realistic turbulence properties at each time step of the simulation. The velocities specified at the inflow should represent the contribution of energy-containing eddies as reviewed by Keating et al. (2004). Previous works can be assessed on the basis of their methods designed to tackle this inflow issue. Readers are referred to Keating et al. (2004) for a brief history review of generating inflow conditions. For the purpose of completeness, a review is also provided here.
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Large-eddy simulation of the zero-pressure-gradient turbulent boundary layer up to Re_θ = O(10^(12))

Large-eddy simulation of the zero-pressure-gradient turbulent boundary layer up to Re_θ = O(10^(12))

the instantaneous local ‘K´arm´an constant’, K 1 , as part of the integrated SGS-model coupled to the LES. 3. Flat-plate turbulent boundary layers: background Research on turbulent boundary layers (TBL) has a long history. Unlike channel or pipe flow, the thickness of the turbulent zone, or TBL thickness δ(x), and the wall shear stress τ w (x) vary with streamwise distance and are not fixed in advance by the channel height or the applied, favourable pressure gradient. They must be computed as part of the simulation. Moreover, the flow outside the TBL may be either smooth or contain free-stream turbulence, and may also contain wall-normal transpiration velocities which are related to the pressure gradient and which must be accurately represented in any simulation. Nonetheless the near-wall regions of channel/pipe flow and that of the TBL are similar, even though the scaling may not be identical (e.g.
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Large Eddy Simulation of Spatially Developing Turbulent Reacting Shear Layers with the One-Dimensional Turbulence Model

Large Eddy Simulation of Spatially Developing Turbulent Reacting Shear Layers with the One-Dimensional Turbulence Model

ν 2 − Z. (2.104) If the discriminant under the root is negative, the sampled eddy will be rejected. The role of Z is to impose a threshold eddy Reynolds number that must be exceeded to allow eddy occurrence. In near-wall flow, the transition from the viscous layer to the buffer layer is sensitive to this threshold and hence to Z. For Z > 0, eddies are suppressed entirely when local values of the eddy Reynolds number are sufficiently small. For Z = 0, the argument of the square root is a scaled form of the net available energy. Thus, for given x j,0 and l e , with Z = 0 is simply the dimensionally consistent relation between the net available energy and the length and time scales of eddy motion, where the associated time scale is the inverse of the (approximately normalized) eddy rate, λ e . Thus, Eq. (2.99) may be viewed as a representation of a mixing-length phenomenology within the ODT framework. This phenomenology is the basis of many turbulence modeling approaches.
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Large-eddy simulation of plume dispersion under various thermally stratified boundary layers

Large-eddy simulation of plume dispersion under various thermally stratified boundary layers

5 Conclusions We performed LESs of plume dispersion under various thermally-stratified boundary layers and examined the use- fulness of our approach by comparing the simulated results with wind tunnel experimental data. For the NBL case, the turbulent boundary layer is reasonably produced. For the SBL case, the turbulence statistics such as the vertical com- ponent turbulence intensity, r.m.s. temperature, and vertical heat flux especially in the lower part of the boundary layer are quantitatively different from the experimental data. It is pointed out that the Smagorinsky model cannot reproduce near-wall turbulent behaviors by Moin and Kim (1982) and Basu et al. (2008). These differences are due to the use of the static Smagorinsky model. However, the shape of the distri- bution patterns corresponding to that of experimental data for a weak stability flow is obtained. This indicates that a weak- type SBL flow is successfully produced. For the CBL case, the turbulence characteristics are generally reproduced well.
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Flow Reversal in Turbulent Boundary Layers with Varying Pressure Gradients

Flow Reversal in Turbulent Boundary Layers with Varying Pressure Gradients

Wall bounded flows submitted to an adverse pressure gradient (APG) are common in many engineering applications, especially in transportation vehicles, such as on the suction side of airfoils. When the strength of the APG is high enough, it can lead to a flow separation, which decreases the performance (increased drag, decreased lift). In the frame of improving the performance of vehicle/aircraft, flow control strategies are tested but most of them try to completely reorganize the flow. At this time, this requires a significant amount of energy which often is not optimal or sometimes even unrealistic. It is therefore important to improve the understanding of the organization of APG flows and to understand the physics of the flow separation which is the basis for new concepts of flow control. The objective of the present investigation is to bring further insights about APG flow organization. In this context, the rare reverse flow events which appear very close to the wall of turbulent boundary layers (TBL) are investigated in detail in order to understand their possible connection with large scale structures which develop in the external region. These events were firstly evidenced by direct numerical simulations (DNS) of channel flow and zero pressure gradient (ZPG) TBL and was characterized in detail by Lenaers et al. (2012), through a channel flow simulation at low Reynolds numbers and more recently by Jalalabadi and Sung (2018) for DNS of a pipe flow.
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Large-eddy simulation of NACA65 compressor cascade with roughness

Large-eddy simulation of NACA65 compressor cascade with roughness

Guidelines for grid resolution for this type of roughness cannot be fully established until a systematic grid refinement study has been performed. One can infer that if roughness height is significant and elements are densely populated, then resolving the shear layer and the roughness element scale will be more relevant than the requirements for resolving the near-wall turbulent boundary layer over a smooth surface, which are represented in wall units. Since cases 1 to 2 have high roughness elements and relatively large streamwise spacing, the current resolution of cases 1 and 2, which are well suited for smooth surface turbulence boundary layer, is not expected to be too coarse. Case 3 has overall very fine grids and in terms of inter-roughness resolution, the number of grid points used is similar to that in case 1. All wall-normal grid size is based on the smooth grid criteria such that y+ is targeted to about 0.3.
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Large eddy simulation of a Mach 0.9 turbulent jet

Large eddy simulation of a Mach 0.9 turbulent jet

the nozzle (i.e., synthetic turbulence, wall modeling, both, or none), the details of the development of the turbulence inside the nozzle are different. However, the internal flow field within the last 1D from the nozzle exit look similar in all cases, much like the exit profiles. All the nozzle-exit boundary layers now exhibit turbulent mean and RMS ve- locity profiles, with much larger fluctuation levels near the wall than in the baseline 10M case with the thin laminar boundary layer. Overall, the grid adaptation has the most significant impact on the nozzle interior flow field for the present configuration.
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Large eddy simulation of shock wave/turbulent boundary layer interaction with and without SparkJet control

Large eddy simulation of shock wave/turbulent boundary layer interaction with and without SparkJet control

Abstract The efficiency and mechanism of an active control device ‘‘SparkJet ” and its application in shock-induced separation control are studied using large-eddy simulation in this paper. The base flow is the interaction of an oblique shock-wave generated by 8 ° wedge and a spatially-developing Ma = 2.3 turbulent boundary layer. The Reynolds number based on the incoming flow property and the boundary layer displacement thickness at the impinging point without shock-wave is 20000. The detailed numerical approaches were presented. The inflow turbulence was generated using the digital filter method to avoid artificial temporal or streamwise periodicity. The numerical results including velocity profile, Reynolds stress profile, skin friction, and wall pressure were sys- tematically validated against the available wind tunnel particle image velocimetry (PIV) measure- ments of the same flow condition. Further study on the control of flow separation due to the strong shock-viscous interaction using an active control actuator ‘‘SparkJet ” was conducted. The single-pulsed characteristic of the device was obtained and compared with the experiment. Both instantaneous and time-averaged flow fields have shown that the jet flow issuing from the actuator cavity enhances the flow mixing inside the boundary layer, making the boundary layer more resis- tant to flow separation. Skin friction coefficient distribution shows that the separation bubble length is reduced by about 35% with control exerted.
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Adjustment of Turbulent Boundary-Layer Flow to Idealized Urban Surfaces: A Large-Eddy Simulation Study

Adjustment of Turbulent Boundary-Layer Flow to Idealized Urban Surfaces: A Large-Eddy Simulation Study

boundary-layer (ABL) flow through idealized urban canopies represented by uniform arrays of cubes in order to better understand atmospheric flow over rural-to-urban surface tran- sitions. The LES framework is first validated with wind-tunnel experimental data. Good agreement between the simulation results and the experimental data are found for the verti- cal and spanwise profiles of the mean velocities and velocity standard deviations at different streamwise locations. Next, the model is used to simulate ABL flows over surface transitions from a flat homogeneous terrain to aligned and staggered arrays of cubes with height h. For both configurations, five different frontal area densities (λ f ), equal to 0.028, 0.063, 0.111, 0.174 and 0.250, are considered. Within the arrays, the flow is found to adjust quickly and shows similar structure to the wake of the cubes after the second row of cubes. An internal boundary layer is identified above the cube arrays and found to have a similar depth in all different cases. At a downstream location where the flow immediately above the cube array is already adjusted to the surface, the spatially-averaged velocity is found to have a logarithmic profile in the vertical. The values of the displacement height are found to be quite insensitive to the canopy layout (aligned vs. staggered) and increase roughly from 0 .65h to 0.9h as λ f increases from 0.028 to 0.25. Relatively larger values of the aerodynamic roughness length
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Large eddy simulation of shock wave/turbulent boundary layer interactions and its control using Sparkjet

Large eddy simulation of shock wave/turbulent boundary layer interactions and its control using Sparkjet

(a) (b) Fig. 12. (a) Skin friction coefficient, and (b) wall pressure profiles. 5. Conclusions Large-Eddy Simulation of a Mach 2.3 shock-wave generated by an 8° sharp wedge impinging onto a spatially-developing turbulent boundary layer along a flat plate is carried out. The numerical approaches and the simulation results are validated with experimental measurements and other LES results in the same flow condition. Based on this, a “SparkJet” control technique is further studied using LES. The configuration of the control device is modeled in reference to the previous experiments with similar configuration parameters. The single-pulse characteristics of the control mechanism are analyzed. The maximum jet velocity time history agrees qualitatively well with the experiments and a maximum jet velocity of 446m/s is predicted, close to that of experimental measurement. By exerting the control device, the flow separation is delayed noticeably and the size of the separation bubble is also reduced significantly by about 35%, and this proves the effectiveness of the SparkJet control technique on suppressing the flow separation occurred in SWTBLI flows.
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Large-eddy simulation and wall modelling of turbulent channel flow

Large-eddy simulation and wall modelling of turbulent channel flow

In unbounded flows, the large eddies, carrying most of the turbulent kinetic energy, set the length and time scales that describe the rest of the small-scale turbulence. This picture is reversed near the wall, where the most energetically productive eddies are necessarily part of the small-scale motion. Further, both these descriptions are present in wall-bounded flows, and both contribute significantly to the overall turbulent flow field. This was demonstrated by Hutchins & Marusic (2007), who plotted velocity spectra of high-Re boundary layers at various wall distances and showed the existence of two distinct energetic peaks: one that scales with viscous units, another that scales with boundary layer thickness. A related complication is that, given sufficiently high Re, even the mean velocity gradient is too steep to be resolved on the coarse LES grid, appearing as a numerical discontinuity. The jump conditions across this discontinuity depend on unclosed turbulent stresses, which themselves require reliable models.
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Large eddy simulation of a three dimensional hypersonic shock wave turbulent boundary layer interaction of a single fin

Large eddy simulation of a three dimensional hypersonic shock wave turbulent boundary layer interaction of a single fin

At the surfaces of the bottom wall and fin, as shown in Figure 1 b), the isothermal no-slip condition with wall temperature 𝑇 𝑊 = 4.39𝑇 0 and adiabatic no-slip condition are used, respectively. The pressure gradient in the wall normal direction is set to be zero. The boundaries of outlet plane, upper surface and lateral surface 1 in Figure 1 b), are treated with perfectly non- reflective boundary condition, in which all the flow variables at the boundaries are extrapolated from the inside of the domain. To reduce the influences of generated numerical errors at the boundaries, these boundaries are posited far from the effective region by using sponge layers with stretched meshes, and the low-order spatial filter 错误 ! 未找到引用
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A Large-Eddy Simulation Study of Turbulent Flow Over Multiscale Topography

A Large-Eddy Simulation Study of Turbulent Flow Over Multiscale Topography

Previous studies (e.g., Wood and Mason 1993) suggest that the effective displacement height is equal to the mean height of surface elevation for hilly terrains. In the present study, all surfaces under consideration have a mean height of zero, making it reasonable for us to choose an effective displacement height of zero. Due to the constant pressure gradient exerted on the flow, the normalized simulated area-averaged total turbulent stress changes linearly with height, from a value of −1 at the surface to a value of zero at the top of the boundary layer. Consistent with that, the non-dimensional effective friction velocity u eff ∗ has a value of 1.0 for all the simulations. Taking advantage of the known effective friction velocity u eff ∗ as well as the area-averaged velocity u obtained from the simulations, and applying Eq. 7, we are able to determine the effective roughness length for all the different terrains under consideration.
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Direct and large-eddy simulation of inert and reacting compressible turbulent shear layers

Direct and large-eddy simulation of inert and reacting compressible turbulent shear layers

At higher convective Mach number the fluid particles that start entering the mixing layer stay close to its edges for a longer time and are carried along before they penetrate inside. This can be seen from the elapsed times between the crossing of the lower and upper thresholds which are larger for the higher convective Mach numbers. In contrast to the times, the displacements in the first two lines of each test case do not change much with compressibility. While the particles at higher M c are convected downstream near the edges of the mixing layer they are slowed down to a variable degree. This becomes clear when looking at the pdf of the local Mach number assembled at the time the upper vorticity threshold is crossed (Fig. 2.168) While it is narrow with a mean close to 0.15 when M c = 0.15, the distribution at M c = 0.7 is much broader and extends from slightly above 0.7 down to about 0.2. The whole mechanism can be seen as some kind of resistance that the compressible mixing layer as compared to the incompressible mixing layer offers towards fluid particles before they are penetrating the mixing layer. Entrainment, the process of ambient fluid acquiring vorticity, takes place at the very periphery of the turbulent region. Then, the growth rate is diminished as fluid packets spend a longer period at the turbulence Table 2.7: Statistics of displacements and elapsed times for growth of vorticity and mixture frac- tion along particle pathlines
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Distributed Roughness Effects on Transitional and Turbulent Boundary Layers

Distributed Roughness Effects on Transitional and Turbulent Boundary Layers

Nagabhushana Rao Vadlamani 1 · Paul G. Tucker 1 · Paul Durbin 2 Received: date / Accepted: date Abstract A numerical investigation is carried out to study the transition of a subsonic boundary layer on a flat plate with roughness elements distributed over the entire surface. Post-transition, the effect of surface roughness on a spatially developing turbulent boundary layer (TBL) is explored. In the transi- tional regime, the onset of flow transition predicted by the current simulations is in agreement with the experimentally based correlations proposed in the literature. Transition mechanisms are shown to change significantly with the increasing roughness height. Roughness elements that are inside the bound- ary layer create an elevated shear layer and alternating high and low speed streaks near the wall. Secondary sinuous instabilities on the streaks destabilize the shear layer promoting transition to turbulence. For the roughness topology considered, it is observed that the instability wavelengths are governed by the streamwise and spanwise spacing between the roughness elements. In contrast, the roughness elements that are higher than the boundary layer create turbu- lent wakes in their lee. The scale of instability is much shorter and transition occurs due to the shedding from the obstacles. Post-transition, in the spatially developing TBL, the velocity defect profiles for both the smooth and rough walls collapsed when non dimensionalized in the outer units. However, when compared to the smooth wall, deviation in the Reynolds stresses are observ- able in the outer layer; the deviation being higher for the larger roughness elements.
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Perturbation of a turbulent boundary layer by spatially-impulsive dynamic roughness

Perturbation of a turbulent boundary layer by spatially-impulsive dynamic roughness

The dynamic roughness appears to develop a thinner internal layer than the static roughness of the same height, although no minima corresponding to the δ 1 layer could be located with any confidence for the dynamic case. Figure 8 shows the variation of skin friction up- and down-stream of the roughness element obtained by oil film interferometry for both static and dynamic roughness elements. Prandtl’s smooth wall curve is shown for comparison and the error bars represent the repeatability of the current oil film measurements. It is clear that for both roughness types, there is a skin friction reduction of order 10% immediately downstream of the perturbation, and that this is smaller for the dynamic roughness (as would be expected from consideration of the root-mean-square of roughness height). In addition, the skin friction appears to overshoot the smooth wall curve. Further mean velocity profiles will investigate the relaxation of the boundary layer in both cases. The estimates of skin friction obtained by using a Clauser fit to the velocity profiles are also shown in figure 8 as lone symbols. Clearly very large errors are inferred by assuming an equilibrium form for the mean velocity.
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