scholarly journals Large-scale motions from a direct numerical simulation of a turbulent boundary layer

2018 ◽  
Vol 98 (3) ◽  
Author(s):  
Ilkay Solak ◽  
Jean-Philippe Laval
Keyword(s):  
Numerical Simulation ◽  
Boundary Layer ◽  
Turbulent Boundary Layer ◽  
Direct Numerical Simulation ◽  
Large Scale

Direct numerical simulation of the turbulent boundary layer over a cube-roughened wall

2011 ◽  
Vol 669 ◽  
pp. 397-431 ◽  
Author(s):  
JAE HWA LEE ◽  
HYUNG JIN SUNG ◽  
PER-ÅGE KROGSTAD
Keyword(s):  
Numerical Simulation ◽  
Boundary Layer ◽  
Turbulent Boundary Layer ◽  
Direct Numerical Simulation ◽  
Outer Layer ◽  
Large Scale ◽  
Roughness Height ◽  
Wall Region ◽  
Momentum Thickness ◽  
Reynolds Stresses

Direct numerical simulation (DNS) of a spatially developing turbulent boundary layer (TBL) over a wall roughened with regularly arrayed cubes was performed to investigate the effects of three-dimensional (3-D) surface elements on the properties of the TBL. The cubes were staggered in the downstream direction and periodically arranged in the streamwise and spanwise directions with pitches of px/k = 8 and pz/k = 2, where px and pz are the streamwise and spanwise spacings of the cubes and k is the roughness height. The Reynolds number based on the momentum thickness was varied in the range Reθ = 300−1300, and the roughness height was k = 1.5θin, where θin is the momentum thickness at the inlet, which corresponds to k/δ = 0.052–0.174 from the inlet to the outlet; δ is the boundary layer thickness. The characteristics of the TBL over the 3-D cube-roughened wall were compared with the results from a DNS of the TBL over a two-dimensional (2-D) rod-roughened wall. The introduction of cube roughness affected the turbulent Reynolds stresses not only in the roughness sublayer but also in the outer layer. The present instantaneous flow field and linear stochastic estimations of the conditional averaging showed that the streaky structures in the near-wall region and the low-momentum regions and hairpin packets in the outer layer are dominant features in the TBLs over the 2-D and 3-D rough walls and that these features are significantly affected by the surface roughness throughout the entire boundary layer. In the outer layer, however, it was shown that the large-scale structures over the 2-D and 3-D roughened walls have similar characteristics, which indicates that the dimensional difference between the surfaces with 2-D and 3-D roughness has a negligible effect on the turbulence statistics and coherent structures of the TBLs.

scholarly journals Direct numerical simulation of conical shock wave–turbulent boundary layer interaction

2019 ◽  
Vol 877 ◽  
pp. 167-195 ◽  
Author(s):  
Feng-Yuan Zuo ◽  
Antonio Memmolo ◽  
Guo-ping Huang ◽  
Sergio Pirozzoli
Keyword(s):  
Numerical Simulation ◽  
Boundary Layer ◽  
Shock Wave ◽  
Turbulent Boundary Layer ◽  
Direct Numerical Simulation ◽  
Pressure Gradient ◽  
Stokes Equations ◽  
Shear Stresses ◽  
Mean Flow ◽  
Conical Shock

Direct numerical simulation of the Navier–Stokes equations is carried out to investigate the interaction of a conical shock wave with a turbulent boundary layer developing over a flat plate at free-stream Mach number $M_{\infty }=2.05$ and Reynolds number $Re_{\unicode[STIX]{x1D703}}\approx 630$, based on the upstream boundary layer momentum thickness. The shock is generated by a circular cone with half opening angle $\unicode[STIX]{x1D703}_{c}=25^{\circ }$. As found in experiments, the wall pressure exhibits a distinctive N-wave signature, with a sharp peak right past the precursor shock generated at the cone apex, followed by an extended zone with favourable pressure gradient, and terminated by the trailing shock associated with recompression in the wake of the cone. The boundary layer behaviour is strongly affected by the imposed pressure gradient. Streaks are suppressed in adverse pressure gradient (APG) zones, but re-form rapidly in downstream favourable pressure gradient (FPG) zones. Three-dimensional mean flow separation is only observed in the first APG region associated with the formation of a horseshoe vortex, whereas the second APG region features an incipient detachment state, with scattered spots of instantaneous reversed flow. As found in canonical geometrically two-dimensional wedge-generated shock–boundary layer interactions, different amplification of the turbulent stress components is observed through the interacting shock system, with approach to an isotropic state in APG regions, and to a two-component anisotropic state in FPG. The general adequacy of the Boussinesq hypothesis is found to predict the spatial organization of the turbulent shear stresses, although different eddy viscosities should be used for each component, as in tensor eddy-viscosity models, or in full Reynolds stress closures.

Direct numerical simulation of the drag-reducing turbulent boundary layer of viscoelastic fluid

2007 ◽  
Vol 19 (7) ◽  
pp. 075106 ◽  
Author(s):  
Shinji Tamano ◽  
Motoyuki Itoh ◽  
Kenichi Hoshizaki ◽  
Kazuhiko Yokota
Keyword(s):  
Numerical Simulation ◽  
Boundary Layer ◽  
Turbulent Boundary Layer ◽  
Direct Numerical Simulation ◽  
Viscoelastic Fluid

Identification of Bypass Transition Onset Markers Using Direct Numerical Simulation

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
Shanti Bhushan ◽  
D. Keith Walters ◽  
S. Muthu ◽  
Crystal L. Pasiliao
Keyword(s):  
Numerical Simulation ◽  
Boundary Layer ◽  
Direct Numerical Simulation ◽  
Large Scale ◽  
General Purpose ◽  
Navier Stokes ◽  
Bypass Transition ◽  
Flow Parameters ◽  
Rans Models ◽  
Large Scale Flow

Efficacy of several large-scale flow parameters as transition onset markers are evaluated using direct numerical simulation (DNS) of boundary layer bypass transition. Preliminary results identify parameters (k2D/ν) and u′/U∞ to be a potentially reliable transition onset marker, and their critical values show less than 15% variation in the range of Re and turbulence intensity (TI). These parameters can be implemented into general-purpose physics-based Reynolds-averaged Navier–Stokes (RANS) models for engineering applications.

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