perspective on turbulence modeling using reynolds...
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Perspective on Turbulence Modeling using Reynolds Stress Models : Modification for pressure gradients
Tobias Knopp, Bernhard Eisfeld
DLR Institute of Aerodynamics and Flow Technology
Experimental data shown were obtained in cooperation withDaniel Schanz, Matteo Novara, Erich Schülein, Andreas Schröder DLR AS
Nico Reuther, Nicolas Buchmann, Rainer Hain, Christian Cierpka, Christian KählerUniv Bundeswehr München, Institute for Fluid Mechanics and Aerodynamics
IMPULSE progress meeting
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slide 2Slide 2
Motivation: Digital aerospace products
Numerical simulation based future aircraft design
Challenges§A/δ99 extremely large: large Re and large A§#simulations large for design and optimization
=> RANS, not LES
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slide 3Slide 3
Optimism to describe “first order” steady state aerodynamic flow phenomena within the RANS concept
Separation in the wing-body junction at APG
Interaction of wake flow and boundary layer at APG
Interaction of strake vortex and boundary layer at APG
DLR strategy for RANS model improvement
Turbulent boundary layers at APG Separation and reattachment Wake flow at APG
[28] Hoffenberg & Sullivan, 1998
[21] Liu et al., 2002
[19] Roos, 1997
[22] Tummers et al., 2007
[20] Driver & Mateer, 2002
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DLR strategy for RANS model improvement
High-quality data base (exp., DNS/LES)
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slide 5Slide 5
DLR strategy for RANS model improvement
High-quality data base (exp., DNS/LES)
(Empirical) laws of turbulence
IMPULSE progress meeting
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slide 6Slide 6
DLR strategy for RANS model improvement
High-quality data base (exp., DNS/LES)
(Empirical) laws of turbulence
Physics-based improvement of RANS
IMPULSE progress meeting
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DLR strategy for RANS model improvement
High-quality data base (exp., DNS/LES)
(Empirical) laws of turbulence
Physics based improvement of RANS
Experiments by Nikuradze
Surface roughness
IMPULSE progress meeting
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DLR strategy for RANS model improvement
High-quality data base (exp., DNS/LES)
(Empirical) laws of turbulence
Physics-based improvement of RANS
kr+ = kr uτ / νΔu+=f(kr
+)
Surface roughness
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DLR strategy for RANS model improvement
High-quality data base (exp., DNS/LES)
(Empirical) laws of turbulence
Physics-based improvement of RANS νt = κuτ (y + 0.03kr)
Surface roughness
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DLR strategy for RANS model improvement
High-quality data base (exp., DNS/LES)
(Empirical) laws of turbulence
Physics-based improvement of RANS
Surface roughness
IMPULSE progress meeting
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DLR strategy for RANS model improvement
High-quality data base (exp., DNS/LES)
(Empirical) laws of turbulence
Physics-based improvement of RANS
Turbulent boundary layer at APG
IMPULSE progress meeting
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DLR strategy for RANS model improvement
High-quality data base (exp., DNS/LES)
(Empirical) laws of turbulence
Physics-based improvement of RANS
Relevant parameter space?
Turbulent boundary layer at APG
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Reynolds number
Pressure gradientparameter
Data base set-up: The parameter space
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Reynolds number
Pressure gradientparameter
Data base set-up: The parameter space
A320 flap
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A380 flap
Data base set-up: The parameter space
A320 flap
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A380 flap A380 wingA350 wing
Data base set-up: The parameter space
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Data base set-up: The parameter space
DLR institute AS decision 2009:New data needed!Only experiments can give large Re!
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Exp I.Assess PIV at moderate Re
Data base set-up: The parameter space
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Experiment #1 (2011): RETTINA I exp. (DLR + UniBw Munich)
Large scale overview 2D2C PIV
LR μPTV
2D3C PIV
U∞= 6 … 12m/s Reθ up to 10000 and δ99 =0.1mThick boundary layers enable PIV
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Exp I.Assess PIVat moderate Re
Data base set-up: The parameter space
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Exp II.Higher Re
Data base set-up: The parameter space
STELAR 2. Projekttreffen > DLR Göttingen > 23.04.2012
Slide 22
Experiment #2 (2015): RETTINA II exp. (DLR + UniBw Munich)
Flow relaxation
ZPGLog-law
APG region
Defined inflow condition. Defined
outflow condition
U∞=10m/s, 23m/s, 36m/s
Funded by DLR institute AS and by DFG
STELAR 2. Projekttreffen > DLR Göttingen > 23.04.2012
Slide 23
ZPGLog-law
APG region
U∞=36m/s
Reθ=41000
Experiment #2 (2015): RETTINA II exp. (DLR + UniBw Munich)
STELAR 2. Projekttreffen > DLR Göttingen > 23.04.2012
Slide 24
Large-scale 2D2C-PIV 9 cams2D3C PIVmicro 2D2C PTV3D3C PTV STB (shake the box)
Experiment #2 (2015): RETTINA II exp. (DLR + UniBw Munich)
STELAR 2. Projekttreffen > DLR Göttingen > 23.04.2012
Slide 25
Challenge: Large range of scale of turbulent boundary layer
Solution approach: Multi-resolution multi-camera PIV
Experiment #2 (2015): RETTINA II exp. (DLR + UniBw Munich)
U=36m/sReθ=41000
ν/uτ = 20μmδ99 = 0.2m
STELAR 2. Projekttreffen > DLR Göttingen > 23.04.2012
Slide 26
Challenge: Large range of scale of turbulent boundary layer
Solution approach: Multi-resolution multi-camera PIV
Experiment #2 (2015): RETTINA II exp. (DLR + UniBw Munich)
U=36m/sReθ=41000
ν/uτ = 20μmδ99 = 0.2m
STELAR 2. Projekttreffen > DLR Göttingen > 23.04.2012
Slide 27
Challenge: Large range of scale of turbulent boundary layer
Solution approach: Multi-resolution multi-camera PIV
Experiment #2 (2015): RETTINA II exp. (DLR + UniBw Munich)
U=36m/sReθ=41000
ν/uτ = 20μmδ99 = 0.2m
STELAR 2. Projekttreffen > DLR Göttingen > 23.04.2012
Slide 28
Challenge: Large range of scale of turbulent boundary layer
Solution approach: Multi-resolution multi-camera PIV
Experiment #2 (2015): RETTINA II exp. (DLR + UniBw Munich)
U=36m/sReθ=41000
ν/uτ = 20μmδ99 = 0.2m STB=„shake-the-box“
Particle-tracking-approachSchanz, Schröder, Novara@ DLR-AS
STELAR 2. Projekttreffen > DLR Göttingen > 23.04.2012
Slide 29
Measurement technique. Skin friction Clauser chart (y+-range depends on ZPG, FPG, APG)
μPTV: Viscous sublayer mean velocityOil film interferometry
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Mild separation and reattachment
Experiment #3 (August 2017) within DLR internal project
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slide 31Slide 31
DLR strategy for RANS model improvement
High-quality data base (exp., DNS/LES)
(Empirical) laws of turbulence
Physics-based improvement of RANS
IMPULSE progress meeting
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Empirical wall law at APG
Mean velocity profiles2D2C Reynolds stresses
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U=36m/sΔpx
+=0.015
Aim: Empirical wall-law at APG for y<0.1δ99
y<10%δ99
Empirical wall law at APG
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U=36m/sΔpx
+=0.015
Aim: Empirical wall-law at APG for y<0.1δ99
y<10%δ99
Empirical wall law at APG
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slide 35Slide 35
U=36m/sΔpx
+=0.015
Hypothesis #1: Resilience of a small log-region
y<0.1δ99
Empirical wall law at APG
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slide 36Slide 36
U=36m/sΔpx
+=0.015
Hypothesis #1: Resilience of a small log-region
y<0.1δ99
Empirical wall law at APG
Galbraith et al. (1977),Granville (1985), Bradshaw (1995), Perry & Schofield (1973), Durbin & Belcher (1992)Skare & Krogstad (1994)Spalart & Coleman …
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Results and analysis
y+Ξ-1 =
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U=36m/sΔpx
+=0.015
y<0.1δ99
Empirical wall law at APG
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U=36m/sΔpx
+=0.015
Hypothesis #2: square-root (sqrt)-law above the log-region
y<0.1δ99
Empirical wall law at APG
Szablewski (1954), Townsend (1961), McDonald (1969),Brown & Joubert…
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Results and analysis
U=36m/sReθ=41000
Δpx+=0.015
Hypothesis #2: square-root (sqrt)-law above the log-region
y<0.1δ99
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Results and analysis
y+log,max
Composite profile Brown & Joubert 1969
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Results and analysis
Hypothesis #3: transition from log-law to sqrt-law depends on Δpx+
(probably more complex …)
y+log,max
Composite profile Brown & Joubert 1969
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Results and analysis
Hypothesis #3: transition from log-law to sqrt-law depends on Δpx+
(probably more complex …)
y+log,max
Composite profile Brown & Joubert 1969
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Results and analysis
Hypothesis #3: transition from log-law to sqrt-law depends on Δpx+
(probably more complex …)
y+log,max
Composite profile Brown & Joubert 1969
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Results and analysis
Hypothesis #3: transition from log-law to sqrt-law depends on Δpx+
(probably more complex …)
ZPG Δpx
+→0
Pure log-law
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Results and analysis
Hypothesis #3: transition from log-law to sqrt-law depends on Δpx+
(probably more complex …)
Separation Δpx
+→ ∞
Pure sqrt-law(Stratford)
• SlopecoefficientK
ZPG
„vonKarmanconstant“κ=0.40+/-0.1
Hypothesis #4: decrease of log-law slope parameter Ki at APG
Empirical wall law at APG
• SlopecoefficientK
ZPG
„vonKarmanconstant“κ=0.40+/-0.1
Hypothesis #4: decrease of log-law slope parameter Ki at APG
Empirical wall law at APG
APG
• SlopecoefficientK
„vonKarmanconstant“κ=0.40+/-0.1
Hypothesis #4: decrease of log-law slope parameter Ki at APG
Empirical wall law at APG
APG
• SlopecoefficientK
„vonKarmanconstant“κ=0.40+/-0.1
Hypothesis #4: decrease of log-law slope parameter Ki at APG
Empirical wall law at APG
APG
• SlopecoefficientK
ZPG
Hypothesis #4: decrease of log-law slope parameter Ki at APG
Empirical wall law at APG
• SlopecoefficientK
ZPG
Hypothesis #4: decrease of log-law slope parameter Ki at APG
Empirical wall law at APG
Increasing Δpx+
APG
• SlopecoefficientK
ZPG
Hypothesis #4: decrease of log-law slope parameter Ki at APG
Empirical wall law at APG
APG
• SlopecoefficientK
ZPG
Hypothesis #4: decrease of log-law slope parameter Ki at APG
Empirical wall law at APG
APG
STELAR 2. Projekttreffen > DLR Göttingen > 23.04.2012
Slide 55
DLR strategy for RANS model improvement
High-quality data base (exp., DNS/LES)
(Empirical) laws of turbulence
Physics-based improvement of RANS
STELAR 2. Projekttreffen > DLR Göttingen > 23.04.2012
Slide 56
DLR strategy for RANS model improvement
High-quality data base (exp., DNS/LES)
(Empirical) laws of turbulence
Physics-based improvement of RANS
Assumptions:§ Blended log-law + sqrt-law§ linear shear stress
STELAR 2. Projekttreffen > DLR Göttingen > 23.04.2012
Slide 57
DLR strategy for RANS model improvement
High-quality data base (exp., DNS/LES)
(Empirical) laws of turbulence
Physics-based improvement of RANS
Assumptions:§ Blended log-law + sqrt-law§ linear shear stress
Blending for eddy visc. νt
STELAR 2. Projekttreffen > DLR Göttingen > 23.04.2012
Slide 58
Idea: A composite wall-law for the turbulent viscosity
log-law at APG
sqrt-law at APG
(Galbraith et al. 1977)
Δpx+ = 0.012
Exp. by Skare & Krogstad
STELAR 2. Projekttreffen > DLR Göttingen > 23.04.2012
Slide 59
RANS modification
HGR01 airfoil at Rec=25Mio, incidence angle α=10°
STELAR 2. Projekttreffen > DLR Göttingen > 23.04.2012
Slide 60
RANS modification
HGR01 airfoil at Rec=25Mio, incidence angle α=10°
STELAR 2. Projekttreffen > DLR Göttingen > 23.04.2012
Slide 61
Idea #1: New terms with Δpx+-dependent local blending
Modifications should be activated only in parts of the boundary layer
Blending for y<0.15δ99 Activate only in the sqrt-region
Pressure diffusion at APG (Rao & Hassan)
Idea#2:RANSmodelcoefficientssensitizedtopressuregradientΔpx+
Standard calibration is for ZPG κ=0.41 = const
Coefficient γ controls the slope of the log-law
Idea#2:RANSmodelcoefficientssensitizedtopressuregradientΔpx+
• TheRANSmodelcoefficientwhichcontrolsthelog-lawslope(“Karman-constant”)becomesafunctiondependingonlocalflow
STELAR 2. Projekttreffen > DLR Göttingen > 23.04.2012
Slide 64
Data structure of wall-normal lines for Δpx+
Surface pointΔpx
+ from dp/dx and uτ
δ99, δ*, θ, H12
Field point
Extension of unstructured flow solver TAUWall-normal linesMethod to determine δ99, δ*, θ, H12
Coding efforts vs. Improvements in predictive accuracy
RANSmodelsensitizedtopressuregradientΔpx+
Surfacequantities Δpx+fromdp/dxanduτ
Maptocorrespondingfieldpoints
FielddistributionofΔpx+
RANSmodelsensitizedtopressuregradientΔpx+
RANSmodelcoefficientγ becomesafunctionofΔpx+
STELAR 2. Projekttreffen > DLR Göttingen > 23.04.2012
Slide 67
Validation of modified RANS
U=36m/s
STELAR 2. Projekttreffen > DLR Göttingen > 23.04.2012
Slide 68
Validation of modified RANS
U=36m/s
STELAR 2. Projekttreffen > DLR Göttingen > 23.04.2012
Slide 69
Validation of RANS
U=36m/s
STELAR 2. Projekttreffen > DLR Göttingen > 23.04.2012
Slide 70
Questions to the audience
STELAR 2. Projekttreffen > DLR Göttingen > 23.04.2012
Slide 71
Turbulent Boundary Layer at ZPG
-www.DLR.de • Folie 71
STELAR 2. Projekttreffen > DLR Göttingen > 23.04.2012
Slide 72
Turbulent Boundary Layer at ZPG
-www.DLR.de • Folie 72
Δcf=5%!
Conclusions
Conclusions
- DLR strategy: Physics based improvement of RANS using new laws of turbulence- First step: Wall law at APG- Composite wall law at APG (Brown & Joubert)
- Small log-region around y+~100- Sqrt-law region above log-region
- Machine learning for further tuning of first theoretical ideas
- Optimism to improve RANS models for aerodynamic flows during next decades- Great potential for future research- DNS/LES at relevant Re possible
- New smart DNS flows (e.g., work of Spalart & Coleman, Soria & Jimenez)- Improved measurement techniques (e.g. advances in PIV/PTV)
End of the presentation§ Experimental data shown in cooperation with
§ Daniel Schanz, Matteo Novara, Erich Schülein, Andreas Schröder DLR AS
§ Nico Reuther, Nicolas Buchmann, Rainer Hain, Christian Cierpka, Christian Kähler, UniBw München
§ Funding of measurement campaign by DFG within „Investigation ofturbulent boundary layers with pressure gradient at high Reynolds numbers with high resolution multi-camera techniques“
Validation RETTINA II
U=36m/s
Validation RETTINA II
U=36m/s
Validation RETTINA II
U=23m/s
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Measuring technique
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Modification of the k-omega model
Modified consistent model
Modification of the k-equation:Pressure diffusion term (Rao & Hassan)
Modification of the ω-equation:Pressure diffusion term
Negative cross-diffusion term
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slide 81Slide 81
Consistency with wall-law
Summary of boundary layer analysis
Consistency with log-law at APG
Consistency with sqrt-law at APG
k-equation No No
ω-equation Yes No
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Consistency with wall-law
Summary of boundary layer analysis
Consistency with log-law at APG
Consistency with sqrt-law at APG
Modified k-equation Yes Yes
Modified ω-equation Yes Yes
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Consistency with wall-law
Summary of boundary layer analysis
Blending active in log-law region at APG
Blending active in sqrt-law region at APG
Modified k-equation Yes Yes
Modified ω-equation No Yes
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The ω-equation in the sqrt-law region
≠
Following ideas by Rao & Hassan, Catris & Aupoix
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The ω-equation in the sqrt-law region
≠
Following ideas by Rao & Hassan, Catris & Aupoix
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The ω-equation in the sqrt-law region
≠
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The ω-equation in the sqrt-law region
Conclusion: The ω-equation is not consistent with the assumed solution in the sqrt-law region at APG
≠
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Measurement technique combining different PIV systems
1.5m
6.38m
1.5m0.78m
0 x [m]7.88 8.66 10.166.38 11.66
2D2C-PIV§ Large scale PIV§ 8 PCO 4000 cams side-by-side 2D3C stereo PIV overview
2D3C stereo PIV detail
1.5m
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Requirements/Design criteria on a new flow experiment
1. Large Reθ for sufficiently thick overlap region2. Log-law established before entering the APG-section3. Thick BL at low flow velocity so that PIV in viscous sublayer possible4. Obtain large values for Δpx
+ , i.e. Δpx+>0.06
6.38m
U∞= 9m/s and U∞= 12m/s
At x=6mReθ up to 8000 and δ99 =0.1m
Design of the test case using RANS-CFD with the DLR TAU code by DLR
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Data for mean velocity down to y+=1 using LR-PIV with PTV
1.5m6.38m
1.5m0.78m
x [m]7.88 8.66 10.166.38 11.66
2D2C PIV Long-range microscopic PIV with PTV
Particle-tracking velocimetry algorithm (PTV) applied to the LR-PIV data see also Kähler et al., Exp. Fluids (2012)
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Boundary layer theory.An approximative model for the
shear stress
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Turbulent boundary layer equation
Consider the equation for wall-parallel mean velocity component U
Integration in wall-normal direction from y‘=0 to wall-distance y
Or in viscous inner units
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Review: Modeling the total shear stress (van den Berg 1973)
Aim: Relate the total shear stress and the mean velocity profile for U
by approximating the integrated convective (or: mean inertial) terms
Van den Berg makes the modeling assumption that
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Review: Modeling the total shear stress (van den Berg 1973)
Modeling assumption
Then
For the V-velocity and using the continuity equation
This gives for the mean inertial term
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Review: Modeling the total shear stress (van den Berg 1973)
This gives the model by van den Berg (1973)
Short notation:
With
Pressure gradient parameter
Wall shear-stress gradient parameter
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Extended modeling for the total shear stress
Modeling assumption
For the chain rule of differentiation we need
Then we obtain, e.g.
With an additional parameter taking into account d²P/dx²
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Extended modeling for the total shear stress
This gives the extended model
With integrals
and with parameters
Pressure gradient parameter
Wall shear-stress gradient parameter
Cross parameter
d²P/dx² -parameter
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Characteristic non-dimensional parametersComparison of the characteristic non-dimensional parameters
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Turbulent shear stress in non-equilibrium flow
Mean inertial terms cause a significant reduction of the turbulent shear stress τ+=1+Δpx+y+
λ
Present exp.at x=8.18mΔpx+=0.0473
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Systematic trend/quantitative formula for variation of Ko
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Conclusion
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Conclusion.
Design of a flow experiment suitable to study the law-of-the-wall for flows at a substantial adverse pressure gradient
Sufficiently large overlap-layer due to large Reθ
Significant adverse pressure gradient Δpx+ up to Δpx
+ =0.065
Combination of different PIV techniques gives high-quality data setLarge number of velocity profilesHigh quality gradients and slope diagnostic functionsComparison of direct and indirect method for uτ locally
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Conclusion.
The log-law does no longer describe the overlap layer 300 < y+ < 0.2 δ99+
A generalized half-power law (or modified log-law) gives a good description
Idea of a composite wall-law by Brown & Joubert (1969) is supportedLog-law-fit region is thin 60 < y+ < 130
1/slope Ki decreasing with increasing Δpx+ as devised by Nickels (2004)
Modified log-law region for 300-400 < y+ < 0.2 -0.4 δ99+
1/slope Ko decreasing with increasing Δpx+
But mean inertial terms need to be accounted for Ko
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Characterization of the flow
ZPGlog-law
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Characterization of the flow
Favourable dp/dsLFPG ~ 5 δref
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Characterization of the flow
Focus region:
Adverse dp/dsLAPG ~ 5 δref
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Universal log-law in zero-pressure gradient region
Universal log-law at the inlet of the adverse pressure gradient section
6.38m
At x=6mReθ=8000
U∞=12m/s
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Systematic trend/quantitative formula for variation of Bi
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Systematic trend/quantitative formula for variation of Bi
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Measurement technique using PIV
Quanta Ray 400
Innolas Spitlight 1000
8 PCO 4000
Superelliptical nose
Flat plate of length 6.38m
Flap
APG region
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slide 111Slide 111
Large-scale 2D2C PIV. Instantaneous flow field
Cam 5Cam 6
Cam 7Cam 8
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Motivation: Digital aerospace products
Numerical simulation based future aircraft design
Challenges§A/δ99 extremely large: large Re and large A§#simulations large for design and optimization
=> RANS, not LES
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slide 113Slide 113
Motivation: Turbulent boundary layers at adverse pressure gradient
During take-off and landingSignificant adverse pressure gradient (APG)
Goals:Þ Wall-laws at APGÞ Improvement RANS models