Download presentation

Presentation is loading. Please wait.

Published byLucas Underwood Modified over 2 years ago

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 y + 4

6
Bursts K i m, K l i n e a n d R e y n o l d s ( ) 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=cRe 2 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 (45 o ) Modern View = Theodorsen + Quasi-Streamwise Vortex

9
Streaks Lift-Up and Form Hairpin Vortices H e a d a n d B a n d y o p a d h y a y ( ) Hairpins Inclined at 45 deg. (Principal Axis) First Evidence of Theodorsens Hairpins Re θ = 1700

10
Streaks Lift-Up and Form Hairpin Vortices H e a d a n d B a n d y o p a d h y a y ( ) For Increasing Re, Hairpin Elongates and Thins Streamwise Vortex Forms the Hairpin Legs

11
Forests of Hairpins Perry and Chong (1982) Theodorsens 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 Chongs 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) LES 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 45 o 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
Spalarts 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 Spalarts 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) Re_D = 5300Re_D = (r) x 1024( θ ) x 2048(z)256(r) x 512( θ ) x 512(z)

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

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

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

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

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

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

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

31
Isotropic Inflow Condition

32
Validation of Boundary Layer Growth Blasius Monkewitz et al

33
Validation of Skin Friction Blasius

34
Validation of Mean Velocity Murlis et al Spalart Re θ = 900

35
Validation Mean Flow Through Transition Re θ = 200 Re θ = 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 Re θ = 800 Re θ = 200

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

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

40
Total stress through transition Plus: Re θ = 200 Solid Line: Re θ = 800

41
Near-Wall Stresses Circle: Spalart Viscous Stress Total 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

46

47

48

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 Spalarts 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

Similar presentations

© 2016 SlidePlayer.com Inc.

All rights reserved.

Ads by Google