EXPERIMENTAL FLOW CHARACTERIZATION AND HEAT...
Transcript of EXPERIMENTAL FLOW CHARACTERIZATION AND HEAT...
EXPERIMENTAL FLOW CHARACTERIZATION AND HEAT FLUX AUGMENTATION ANALYSIS OF A
HYPERSONIC TURBULENT BOUNDARY LAYER ALONG A ROUGH SURFACE
D. Neeb, D. Saile, A. GülhanSupersonic and Hypersonic Technology Department, Institute of Aerodynamics and Flow Technology, DLR Germany
8th European Symposium on Aerothermodynamics for Space Vehicles2 - 6 March 2015, Lisbon, Portugal
Outline
Motivation
Theory
Numerical tools
Analytical correlations
Numerical prediction
Experimental tools
Wind tunnel
Model
Measurement techniques
Analysis
Boundary layer
Heat flux
Summary and Outlook
> Neeb, Saile, Gülhan > 7th ESASV > 03.03.2015Slide 2
Surface roughness increases skin friction drag and convective heat transfer above the turbulent level on aircrafts, missiles, re-entry vehicles and propulsion systems
Careful consideration in the prediction of the resulting heat load levels is required for the design of a vehicle
Better understanding is required
Motivation
> Neeb, Saile, Gülhan > 7th ESASV > 03.03.2015Slide 3
IR of FS wind tunnel test
ESA ExoMars
IR of material wind tunneltests
Surface roughness has measureable effect on
Velocity
Turbulence
Skin friction
Heat flux
…
Key parameter to scale is the (equivalent) sand grain roughness height ks
Theory
> Neeb, Saile, Gülhan > 7th ESASV > 03.03.2015Slide 4
ks+ [-]
St r
/St s
[-]
100 101 102 103 104
1
1.2
1.4
1.6
1.8
2
2.2
2.4
2.6
2.8 Hill et al (sand grain)
Young (v-grooves)
Holden (sand grain/spheres)
model1
model2
model5
model2013.1
model2013.2
Seidman
Powars
FCII
FCIII
FCV
FCVII
FCVIII
(Neeb, Merrifield, Gülhan, 2014)(Sahoo,2009)
Generic cone model enables the use of analytical and numerical predictions to support analysis
Several analytical smooth Van Driest and via Reynolds analogy St
and rough wall heat flux predictionsPowars (St based on Passive Nosetip Technology (PANT) data)
Boundary layer code (Harris, NASA, 1982) enables prediction of smooth & rough heat flux and boundary layer parameters
Compressible modified Krogstad model for rough surfaces implemented (based on equivalent sand grain roughness) as proposed by FGE
CFD code (DLR TAU) enables prediction of smooth heat flux and boundary layer parameters
Numerical toolsSmooth and rough wall predictions
> Neeb, Saile, Gülhan > 7th ESASV > 03.03.2015Slide 5
Mach number
Rey
nold
snu
mbe
r[1
06 ·m
-1]
4 5 6 7 8 9 10 11 120
5
10
15
20
25
Rough cone 7deg
5 bar
2 kg/s
5 kg/s
10 kg/s
15 kg/s
20 kg/s
45 bar10 bar 30 bar20 bar Total pressureMass flow
Experimental toolsDLR Hypersonic wind tunnel H2K
> Neeb, Saile, Gülhan > 7th ESASV > 03.03.2015Slide 6
Blow down facility
Exchangeable contoured nozzles Ma= 5.3; 6.0; 7.0; 8.7; 11.2
Electrical heaters with capacity of up to 5 MW
Testing time: ~30s
FlowCond.
MaReL(106)
p0(bar)
T0(K)
p(bar)
T(K)
u(m/s)
inflow 6.1 11.5 20.0 500 0.0119 75 940edge 5.4 15.1 19.8 500 0.0236 73 926
λ
k
w
i
roughness
L = 0.73 m
x = 0.44 L
Sharp 7° cone configuration
Smooth sharp metallic nose
Smooth PEEK middle and end segment to be assessed by infrared thermography
Rough PEEK end segment with technical roughness
2D square bar topology with λ/w = (w + i)/w = 4
Existing literature data to compare in incompressible (ks / k = 2.0-6.0) and compressible regime (ks / k = 0.7-1.9)
Experimental toolsModel
> Neeb, Saile, Gülhan > 7th ESASV > 03.03.2015Slide 7
Global quantitative infrared thermography
Heat flux derived by solving the one dimensional heat equation
Particle Image Velocimetry (PIV)
Solid particle seeding (dp,mean=3.5µm, tp=2.6µs)
Velocities are derived within the boundary layer in specific regions of interest (9x12mm²)
Experimental toolsMeasurement techniques
> Neeb, Saile, Gülhan > 7th ESASV > 03.03.2015Slide 8
ROI at x/L=0.68
~200 images per run post-processed (leveling, rotation)
Amount of particles nearly equally distributed
Post-processing with different validation parameters to ensure well correlated results
Different interrogation window sizes tested
Best results with iw64x96 (0.48x0.72 mm²)
Mean streamwise normalized velocities show clear boundary layer structure with
Increased rough BL height
Lower velocities above rough surface
Slight waviness in rough case
AnalysisBoundary layer topology
> Neeb, Saile, Gülhan > 7th ESASV > 03.03.2015Slide 9
u [m/s]
y[m
]
0 200 400 600 800 10000
0.002
0.004
0.006
0.008
TAU - turb. - Tu nom.TAU - turb. - Tu0.01TAU - turb. - Tu0.02BLcode turb.Run7 iw256x256Run7 iw128x128Run7 iw96x96Run7 iw256x256 to 96x96Run7 iw256x256 to 64x96Run7 iw256x256 to 48x96Run7 iw256x256 to 24x96
AnalysisBoundary Layer profiles
> Neeb, Saile, Gülhan > 7th ESASV > 03.03.2015Slide 10
Smooth surface profiles with mean streamwise velocities in between 0.494<x<0.500m
Different interrogation window sizes show good agreement
Good agreement to numerical data for fully turbulent flow
u/u e
y/δ
0 0.2 0.4 0.6 0.8 1 1.20
0.2
0.4
0.6
0.8
1
1.2
Run7 smooth iw64x96Run8 smooth iw64x96Run12 rough iw64x96Run14 rough iw64x96BLcode turb.TAU turb. Tu nom.BLcode Krogstad ks=0.5mmBLcode Krogstad ks=1.0mm
AnalysisBoundary Layer normalized smooth and rough profiles
> Neeb, Saile, Gülhan > 7th ESASV > 03.03.2015Slide 11
Smooth and rough surface profiles with mean velocities in between 0.494<x<0.500m scaled with corresponding δ and ue
Good repeatability in smooth and rough case
Good agreement of smooth results to fully turbulent numerical profiles
Rough wall velocity shift clearly detectable
Good agreement of computed rough profile by Krogstad model with ks = 0.5mm (ks / k = 1)
AnalysisHeat flux smooth surface
> Neeb, Saile, Gülhan > 7th ESASV > 03.03.2015Slide 12
Ree,x
St e
0 5E+06 1E+07 1.5E+070
0.0005
0.001
0.0015
0.002TAU lam.TAU turb Tu nom.TAU turb. Tu0.02BLcode lam.BLcode turb.Korkegi lam.VanDriestII turb.Fenter turb. smoothRun3Run4Run5Run6Run7Run8Run11
Very good repeatability of laminar level, transition onset and turbulent level
Seeding has clear influence on transition onset but not on turbulent level
Turbulent level in good agreement with TAU 2% inflow turb. intensity
Analytical and BL code predictions overestimate
Ree,x
St e,
r/St e,
s
5E+06 1E+07 1.5E+070.8
1
1.2
1.4
1.6
1.8
2
2.2
2.4
2.6
2.8
3BLcode Krogstad ks=0.5mmBLcode Krogstad ks=1.0mmBLcode Krogstad ks=1.5mmPowars ks=0.5mmPowars ks=1.0mmPowars ks=1.5mmRun12 to Run3Run13 to Run3
AnalysisHeat flux rough wall augmentation
> Neeb, Saile, Gülhan > 7th ESASV > 03.03.2015Slide 13
Rough wall augmentation slightly oscillates around mean of approx. 20% due to topology
Powars and Krogstadoverpredicts
Ree,x
St e,
r/St e,
s
5E+06 1E+07 1.5E+070.8
1
1.2
1.4
1.6
1.8
2
2.2
2.4
2.6
2.8
3BLcode Krogstad ks=0.5mmBLcode Krogstad ks=1.0mmBLcode Krogstad ks=1.5mmPowars ks=0.5mmPowars ks=1.0mmPowars ks=1.5mmRun16 to Run3
Results in increased augmentation with oscillations of higher amplitude and higher mean level of approx. 30%
Peak values are captured by Krogstad model with ks = 0.5 -1.0mm (ks / k = 1 - 2), which is in the same range as compressible literature data and velocity profile prediction
AnalysisHeat flux rough augmentation zoomed
> Neeb, Saile, Gülhan > 7th ESASV > 03.03.2015Slide 14
AnalysisHeat flux rough augmentation zoomed
> Neeb, Saile, Gülhan > 7th ESASV > 03.03.2015Slide 15
x [m]
∆Tin
it[K
]
z[m
]
0.324 0.328 0.332 0.336 0.34 0.344-50
-40
-30
-20
-10
0
10
20
30
40
50
0.04
0.05
contourRun_0003Run12Run13Run14Run16
Results in increased augmentation with oscillations of higher amplitude and higher mean level of approx. 30%
Peak values are captured by Krogstad model with ks = 0.5 -1.0mm (ks / k = 1 - 2), which is in the same range as compressible literature data and velocity profile prediction
Zoomed view of the rough surface with increased resolution of ~2pixel per square bar
PIV successfully applied for the first time in the hypersonic regime of H2K
Velocity profiles along smooth cone in good agreement to predictions
Roughness velocity shift clearly detectable and profiles along rough cone
Direct sand grain roughness height extraction via fitting very sensitive and non-unique
higher resolved profiles near the wall with highly stretched interrogation windows will be tested
cumulative rough data analysis of several runs to extract fluctuations for skin friction velocity extraction
2nd profile position data exploitation
Pitot pressure profile measurements
Heat flux augmentation along rough cone sensitive to resolution
With highest possible resolution mean level of augmentation is 30%
higher resolution will be tested to see if result is converged
3D heat flux calculation based on IR data
Both velocity shift and heat flux augmentation are predicted by compressible Krogstadmodel ks = 0.5 -1.0mm (ks / k = 1 - 2), which is in the same range as compressible literature data
Summary and Outlook
> Neeb, Saile, Gülhan > 7th ESASV > 03.03.2015Slide 16