1. Mechanics of Wall Turbulence
Parviz Moin
Center for Turbulence Research
Stanford University

2. Classical View of Wall Turbulence
Mean Velocity Gradients  Turbulent Fluctuations
Predicting Skin Friction was Primary Goal

3. Classical View of Wall Turbulence
Eddy Motions Cover a Wide Range of Scales
Energy Transfer from Large to Smaller Scales
Turbulent Energy Dissipated at Small Scales

4. Major Stepping Stones Visualization & Discovery of Coherent Motions
Low-Speed Streaks in “Laminar Sub-Layer”
Kline, Reynolds, Schraub and Runstadler (1967)
Kim, Kline and Reynolds (1970)
Streaks Lift-Up and Form Hairpin Vortices
Head and Bandyopadhyay (1980)
Large Eddies in a Turbulent Boundary Layer with Polished Wall, M. Gad-el-Hak

5. Low-Speed Streaks in “Laminar Sub-Layer” Kline, Reynolds, Schraub and Runstadler (1967)
Three-Dimensional, Unsteady Streaky Motions
“Streaks Waver and Oscillate Much Like a Flag”
Seem to “Leap Outwards” into Outer Regions

6. Bursts Kim, Kline and Reynolds (1970)
Streaks “Lift-Up” Forming a Streamwise Vortex
Near-Wall Reynolds Shear Stress Amplified
Vortex + Shear  New Streaks/Turbulence

7. Major obstacle for LES
Streaks and wall layer vortices are important to the dynamics of wall turbulence
Prediction of skin friction depends on proper resolution of these structures
Number of grid points required to capture the streaks is almost like DNS, N=cRe2
SGS models not adequate to capture the effects of missing structures (e.g., shear stress).

8. Early Hairpin Vortex Models Theodorsen (1952)
Spanwise Vortex Filament Perturbed Upward (Unstable)
Vortex Stretches, Strengthens, and Head Lifts Up More (45o)
Modern View = Theodorsen + Quasi-Streamwise Vortex

9. Streaks Lift-Up and Form Hairpin Vortices Head and Bandyopadhyay (1980)
Spanwise Separation of Hairpins λ+ ≈ 100 y+
Hairpins Inclined at 45 deg. (Principal Axis)
First Evidence of Theodorsen’s Hairpins

10. Streaks Lift-Up and Form Hairpin Vortices Head and Bandyopadhyay (1980)
For Increasing Re, Hairpin Elongates and Thins
Streamwise Vortex Forms the Hairpin “Legs”

11. Forests of Hairpins Perry and Chong (1982)
Theodorsen’s Hairpin Modeled by Rods of Vorticity
Hairpins Scattered Randomly in a Hierarchy of Sizes
Reproduces Mean Velocity, Reynolds Stress, Spectra
Has Difficulty at Low-Wavenumbers

12. Packets of Hairpins Kim and Adrian (1982)
VLSM Arise From Spatial Coherence of Hairpin Packets
Hairpin Packets Align & Form Long Low-Speed Streaks (>2δ)

13. Packets of Hairpins Kim and Adrian (1982)
Extends Perry and Chong’s Model to Account for Correlations Between Hairpins in a Packet; this Enhanced Reynolds Stress Leads to Large-Scale Low-Speed Flow

14. Major Stepping Stones Computer Simulation of Turbulence (DNS/LES)
A Simulation Milestone and Hairpin Confirmation
Moin & Kim (1981,1985), Channel Flow
Rogers & Moin (1985), Homogeneous Shear
Zero Pressure Gradient Flat Plate Boundary Layer (ZPGFPBL)
Spalart (1988), Rescaling & Periodic BCs
Spatially Developing ZPGFPBL
Wu and Moin (2009)

15. A Simulation Milestone Moin and Kim (1981,1985)
ILLIAC-IV

16. A Simulation Milestone Moin and Kim (1981,1985)
Experiment

17. A Simulation Milestone Moin and Kim (1981,1985)

18. Hairpins Found in LES Moin and Kim (1981,1985)
“The Flow Contains an Appreciable Number of Hairpins”
Vorticity Inclination Peaks at 45o
But, No Forest!?!

19. Shear Drives Hairpin Generation Rogers and Moin (1987)
Homogeneous Turbulent Shear Flow Studies Showed that Mean Shear is Required for Hairpin Generation
Hairpins Characteristic of All Turbulent Shear Flows
Free Shear Layers, Wall Jets, Turbulent Boundary Layers, etc.

20. Spalart’s ZPGFPBL and Periodicity Spalart (1988)
TBL is Spatially-Developing, Periodic BCs Used to Reduce CPU Cost
Inflow Generation Imposes a Bias on the Simulation Results
Bias Stops the Forest from Growing!

21. Analysis of Spalart’s Data Robinson (1991)
“No single form of vortical structure may be considered representative of the wide variety of shapes taken by vortices in the boundary layer.”
Identification Criteria and Isocontour Subjectivity
Periodic Boundary Conditions Contaminate Solution

22. Computing Power 5 Orders of Magnitude Since 1985!
Advanced Computing has Advanced CFD (and vice versa)

23. DNS of Turbulent Pipe Flow Wu and Moin (2008)
256(r) x 512(θ) x 512(z)
300(r) x 1024(θ) x 2048(z)
Re_D = 5300
Re_D = 44000

24. Very Large-Scale Motions in Pipes Wu and Moin (2008)
DNS at ReD = 24580, Pipe Length is 30R
(Black) -0.2 < u’ < 0.2 (White)
Log Region (1-r)+ = 80
Buffer Region (1-r)+ = 20
Core Region (1-r)+ = 270

25. Experimental energy spectrum
Experiment, using T.H.
Perry & Abell (1975)‏
Energy
Wavelength

26. Energy Spectrum from Simulations
Experiment, using T.H.
Perry & Abell (1975)‏
Simulation, true spectrum
del Álamo & Jiménez (2009)‏
Energy
Wavelength

27. Artifact of Taylor's Hypothesis
Experiment, using T.H.
Perry & Abell (1975)‏
Simulation, true spectrum
del Álamo & Jiménez (2009)‏
Energy
Simulation, using T.H.
del Álamo & Jiménez (2009)‏
Wavelength

28. Artifact of Taylor's Hypothesis
Aliasing
Experiment, using T.H.
Perry & Abell (1975)‏
Simulation, true spectrum
del Álamo & Jiménez (2009)‏
Energy
Simulation, using T.H.
del Álamo & Jiménez (2009)‏
Wavelength

29. Simulation of spatially evolving BL Wu and Moin (2009)
Simulation Takes a Blasius Boundary Layer from Reθ = 80 Through Transition to a Turbulent ZPGFPBL in a Controlled Manner
Simulation Database Publically Available:

30. Blasius Boundary Layer + Freestream Turbulence
t = 100.1T
t = 100.2T
4096 (x), 400 (y), 128 (z)
t = T

31. Isotropic Inflow Condition

32. Validation of Boundary Layer Growth
Blasius
Monkewitz et al
Blasius

33. Validation of Skin Friction
Blasius

34. Validation of Mean Velocity
Murlis et al
Spalart
Reθ = 900

35. Validation Mean Flow Through Transition
Reθ = 200
Reθ = 800
Circle: Spalart

36. Validation of Velocity Gradient
Circles: Spalart (Exp.)
Triangles: Smith (Exp.)
Dotted Line: Nagib et al. (POD)
Solid Line: Wu & Moin (2009)

37. Validation of RMS Through Transition
Circle: Spalart
Reθ = 800
Reθ = 200

38. Validation of RMS fluctuations
circle: Purtell et al
other symbols: Erm & Joubert
Reθ = 900

39. Validation of RMS Fluctuations
Circle: Purtell et al
Plus: Spalart
Lines: Wu & Moin

40. Total stress through transition
Plus: Reθ = 200
Solid Line: Reθ = 800

41. Near-Wall Stresses
Total Stress
Circle: Spalart
Viscous Stress

42. Shear Stresses Circle: Honkan & Andreopoulos Diamond: DeGraaff & Eaton
Plus: Spalart

43. Immediately before breakdown
t = T
u/U∞ = 0.99

44. Hairpin Packet at t = T
Immediately Before Breakdown

45. Winner of 2008 APS Gallery of Fluid Motion

49. Summary Preponderance of Hairpin-Like Structures is Striking!
A Number of Investigators Had Postulated The Existence of Hairpins
But, Direct Evidence For Their Dominance Has Not Been Reported in Any Numerical or Experimental Investigation of Turbulent Boundary Layers
First Direct Evidence (2009) in the Form of a Solution of NS Equations Obeying Statistical Measurements

50. Summary-II
Forests of Hairpins is a Credible Conceptual Reduced Order Model of Turbulent Boundary Layer Dynamics
The Use of Streamwise Periodicity in channel flows and Spalart’s Simulations probably led to the distortion of the structures
In Simulations of Wu & Moin (JFM, 630, 2009), Instabilities on the Wall were Triggered from the Free-stream and Not by Trips and Other Artificial Numerical Boundary Conditions
Smoke Visualizations of Head & Bandyopadhyay Led to Striking but Indirect Demonstration of Hairpins Large Trips May Have Artificially Generated Hairpins

51. Conclusion
A renewed study of the time-dependent dynamics of turbulent boundary layer is warranted. Helpful links to transition and well studied dynamics of of isolated hairpins.
Calculations should be extended to Re>4000
would require 3B mesh points.
Potential application to “wall modeling” for LES