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Computational Sciences Center High-Speed Flow Research

2014

Dr. Jonathan Poggie Senior Aerospace Engineer

AFRL/RQHF Air Force Research Laboratory

Hypersonic Technology

High-‐Speed Strike Weapon

Scramjet Mach 5-‐8

Space Access

Rocket Mach 20+

Penetra6ng Regional ISR / Strike

Scramjet Mach ≈5

Tac6cal Boost-‐Glide / Prompt Global Strike

Rocket Mach = 10-‐20

2 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. 88ABW-2014-3059

High-Speed Flow Research Team Computational Sciences Center

•  Government Employees –  Dr. Nicholas Bisek –  Dr. Ryan Gosse –  Mr. Eswar Josyula –  Dr. Joel Malo-Molina –  Dr. Jonathan Poggie

•  Basic Research Contractors –  Dr. Jon Burt –  Dr. Timothy Leger

•  Application Support Contactors –  Mr. D. Galbraith –  Mr. W. Humphrey

•  AFRL Partners –  Dr. Mark Hagenmaier (scramjet flows) –  Dr. Roger Kimmel (transition, SWBLI unsteadiness, flight tests)

•  CCAS Partners –  Dr. Datta Gaitonde (FDL3DI code, SWBLI unsteadiness, BL trips) –  Dr. Graham Candler (US3D code, thermophysics)


Main Research Themes

1.  LES and DES as engineering tools 2.  Fatigue loading 3.  Inlet flows 4.  Selected topics in aerothermal prediction


1. LES and DES as Engineering Tools

•  Large-scale computations •  Computational methodology •  Verification and validation

5 Gosse, US3D, HIFiRE-‐6 Sherer, HIFiRE-‐6

DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. 88ABW-2014-3059

DoD HPCMP Frontier Project

6

Year CPU-‐Hrs (millions)

2014 90

2015 150

2016 211

2017 890*

2018 890*

Title: Unsteady Pressure and HeaPng Environment on High-‐Speed Vehicles with Responding Structures PI: Dr. Ryan Gosse, AFRL/RQHV

•  MulP-‐disciplinary simulaPons •  Disparate space and Pme scales •  UPlize significant fracPon of machine

(10k – 100k cores) •  Develop procedures for large jobs,

large data

Garnet / ERDC: 150k cores


Hybrid Parallelism

7

•  Exploit fine-‐grained parallelism with vectorizaPon and threads (OpenMP)

•  Support domain decomposiPon with MPI

•  Minimum block size set by compact difference numerics

•  Use threads to scale to more cores at same accuracy

Scaling with Blocks and Threads


Turbulent Boundary Layer Flow

8

Density contours, ρ/ρ∞

M = 2.3, 2.9 Reθi = 2000, 2500 106 to 109 cells Up to 32k cores Up to 8 threads Poggie et al., AIAA Paper 2014-‐0423

Poggie, AIAA 2014-‐xxxx


Comparison to Experiment: Boundary Layer Profiles

9

Mean Profiles Streamwise Fluctua6ons


Comparison to Experiment: Spatial Correlations

10

Data

Computation

Thus, the pseudo-intermittency can be expressed as:

c ! I1 " I y# $I1 " I2

! "#5$

Figure 7 shows the pseudo-intermittency profile cal-culated in this manner. An intermittency profile (Seliget al. 1989) derived using a VITA-based threshold tech-nique from hotwire data obtained in the same boundarylayer flow is included for comparison. The shape of theprofiles is quite similar, but they are offset by about 0.3d.The offset may well be due to differences in the thresholdfor defining turbulent fluid.

4.1.2CorrelationsA spatial correlation function was calculated over a set ofimages between the scattering intensity recorded at eachpixel in the field of view and the scattering intensity at areference location. The correlation coefficient was definedby the following formula:

R x; r# $ !PN

i!1 Ii x# $ " !I% & Ii x' r# $ " !I% &#########################################################################PNi!1 Ii x# $ " !I% &2

PNi!1 Ii x' r# $ " !I% &2

q #6$

where I is the intensity of scattered light recorded by thevideo camera, x is a reference location, and r is a relativedisplacement vector. This type of correlation mimics thecross-correlation between a pair of point probes at zerotime delay.

Figure 8 shows contour plots of the two-point cross-correlation for several heights in the boundary layer. Thedomain corresponds to the central third of the field of viewin Fig. 4. A set of N=400 images was used to calculate thecorrelation in each case. The correlation contours are seento be approximately elliptical in shape. Large regions ofhigh correlation are seen near the middle of the boundarylayer (Fig. 8b, c). In contrast, lower correlation levels andshorter length scales occur close to the freestream(Fig. 8a), where only the tips of the bulges reach, and nearthe wall (Fig. 8d), where fingers of freestream fluid rarelyreach.

Another feature of the plots is the changing inclinationof the principal axis of the ellipse-like correlation con-tours. This angle (h) was measured graphically from thecorrelation plots like the examples in Fig. 8, and is plottedin Fig. 9 as a function of height in the boundary layer. Theorientation of the principal axis varies from horizontal atthe mean boundary layer edge to about 45! at 0.6d, whereit stays constant until scatter is introduced due to lack ofresolution in the flow visualization near the wall.

Quite similar structure angles were observed (Spinaet al. 1991b) in space–time correlations of hotwire massflux data for small wire separations. For a wire separationof 0.09d, a structure angle of 45!–50! was obtained in therange 0.2 £ y/d £ 0.8 (Fig. 10).

A similar correlation analysis has been done of theplan-view images (Poggie 1991), but is omitted here for thesake of brevity. The results showed elliptical correlationcontours, elongated in the streamwise direction, withprincipal axes on the order of 1.0d and 0.5d at y/d=0.5.These results are also consistent with hotwire space–timecorrelations (Spina et al. 1991b).

Fig. 7. Pseudo-intermittency profiles through the Mach 2.9turbulent boundary layer. Solid line is image data, open circles ishotwire data from Selig et al. (1989)

Fig. 8. Two-point cross-corre-lations of side-view images ofthe turbulent boundary layer.Horizontal line indicates posi-tion of wall

444

D. Smith et al., AIAA 91-0651 Poggie et al., Experiments in Fluids, 2004

Flow

Experiment


kz

+

E(k

z)

10-3

10-2

10-1

10-9

10-7

10-5

10-3

10-1

101

Lz/!

0 = 5

Lz/!

0 = 10

Lz/!

0 = 20

Boundary Layer Fluctuations: Effect of Domain Width

Density 20 δ0

10 δ0

5 δ0

Mass Flux (ρu)’ z-‐Wavenumber Spectrum

Increasing Lz

x/δ0 = 100 y/δ = 0.5

Downstream View y-‐z-‐plane, x/δ0 = 100

ResoluPon: Δx+ = 6 Δy+ = 0.5-‐9 Δz+ = 5 Δt+ = 0.05


2. Fatigue Loading

12

Shock Interaction on Flat Panel

•  Fatigue loading from unsteady SWBLI very serious design concern

•  Typical for Mach 5 Cruise: 147 dB, 800 K (Zuchowski, AFRL TR, 2012)


Separation Bubble as Amplifier

13

AAmplifier

Input:Turbulence

!(f)Filter

Output:Shock Motion

•  Plotkin (AIAA J, 1975) •  Linearly-‐damped Brownian

moPon •  Time scales: τu << τR •  Predicts spectrum,

autocorrelaPon, fluctuaPon intensity

•  Frequency-‐selecPve amplifier

•  Cut-‐off frequency set by separaPon bubble characterisPcs

•  Input set by TBL t/! R

Rp(t

)

0 2 4 6 8 10

0

0.2

0.4

0.6

0.8

1

2!f" R

Gp(f

)/("

R#

p2)

fGp(f

)/#

p2

10-2

10-1

100

101

102

10-4

10-3

10-2

10-1

100

101

0

0.1

0.2

0.3

0.4

0.5

Spectrum AutocorrelaPon Wall Pressure Sta6s6cs


Flight Test Experiments

HIFiRE-1

•  HIFiRE Flight 1: Woomera Range, March 22, 2010 •  Documentation: Stanfield, Kimmel, and Adamczak,

AIAA Papers (2012-2013)

14

Kulite Transducer Stations


2!f"R

G(f

)/("

R#

p2)

10-2

10-1

100

101

102

103

10-4

10-3

10-2

10-1

100

101

102

x/$0 = 67.0x/$0 = 68.0x/$0 = 69.0Plotkin (1975)

Wall Pressure Spectra

Computational Results Experimental Results

Various configurations, M = 2-5 Compression ramp, M = 2.3, 24 deg

HIFiRE-1 Flight Data Large-Scale

Shock Motion

Boundary Layer Turbulence

15

LES captures large-scale unsteadiness J. Poggie, N. J. Bisek, R. L. Kimmel, and S. A. Stanfield, “Spectral CharacterisPcs of SeparaPon Shock Unsteadiness,” AIAA Journal, in press, 2014.


Effect of Control

Reduced: •  Size of separation bubble •  Energy in low-frequency wall pressure fluctuations •  Fatigue loading

Baseline Case Control Case PSD of TKE

0.001 0.01 0.1 1 1010

!5

10!4

10!3

10!2

10!1

100

PowerSpectralDensityofTKE

Normalized Frequency, f l/U!

baselineperfectpulsedrealBaseline

Control

24° Ramp Mach 2.3 Reθ = 2000

16

Bisek, Rizzetta, and Poggie, AIAA Journal, 2013


Fluid / Hot Structure Interaction

Pressure

Density

•  Non-‐equilibrium LES with moving grid •  Non-‐linear structural response

Supersonic Flow over Deflected Panel Large-‐Scale Separa6on Shock Oscilla6on Drives Structural Dynamics

M = 5

Panel Responds

Data: Maestrello and Linden (1970)

R. Gosse US3D


3. Inlet Flows

•  Corner flow interactions •  RANS modeling •  LES

18

R. Gosse, US3D Code, HIFiRE-‐6

X-‐51


Corner Interactions in Inlet Flows

19

x=0

x=-‐1/2

x=0

x=-‐1/2

Inviscid flow

incident sho

ck

impingement lin

e

Benek, Suchyta, and Babinsky (2014) RANS SimulaPons (Overflow, non-‐eq. k-‐ω) M = 2.9, α = 13°, W/H = 1, δ/W = 0.07

Mean Flow Streamlines


Effect of Boundary Layer Blockage on Separation Scale

20

0.00

1.00

2.00

3.00

4.00

5.00

6.00

0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000

Δx/fδ

δ/gW

Scaled M=2.9 W=13

M=2.9 W=10

M=2.9 W=8

M=2.5 W=13

M=2.5 W=10

M=2.5 W=8

M=2.9 W=13

M=2.9 W=10

M=2.9 W=8

M=2.5 W=13

M=2.5 W=10

M=2.5 W=8

Δx DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. 88ABW-2014-3059

LES for Corner Flows

M = 2.3, Reθ = 2400 HFILES, FDL3DI Code 1.06x109 cells 13240 processors ~2 weeks

N. Bisek, AIAA Paper 2014-‐0558

Mach 2.3 Turbulent Corner Flow


Corner-Ramp Configuration

22

M = 2.3, Reθ = 2400 HFILES, FDL3DI Code 750 Mcells 47040 processors (5880 tasks x 8 threads)


4. Selected Topics in Aerothermal Prediction

Pressure Contours RANS for 3D Interac6ons 23° Sharp Fin M = 5.0, Reθ = 7400 DLR Experiments: Schülein et al. (2001)

US3D Code

Streamwise Profile of Wall Heat Flux

23

Leger and Poggie, AIAA 2014-‐0951


Direct Numerical Simulation of Laminar-Turbulent Transition

24

Wall Heat Flux

Trip

“Pizza-‐Box” Trip

Flight Test ArPcle

Trip

Cost/run: 3200 processors for 120 hours

HIFiRE-‐1 Boundary Layer Trip Experiment

Gronvall, Bisek, and Poggie, AIAA Paper 2014-‐0433 Kimmel and Adamczak, AIAA Paper 2011-‐3413


State-to-State Kinetics: Influence on Wall Heat Flux

25

( )[ ] ciciccici VVvt

ωρρ =++•∇+∂

∂ ~

DVEMDTDHC qqqqq +++=

Ø  Master equation for V-V/V-T ü 48 quantum levels for nitrogen"ü FHO rates"ü Self-diffusion in vibrational states"

Ø Transport coeffs from Wang-Chang Uhlenbeck eqn (Boltzmann equation with internal energy) Ø  Contributions to heat transfer: heat conductivity (HC), thermal diffusion (TD), mass diffusion (MD), and diffusion of vibrational energy (DVE)

Contribution of Heat Conduction to Total Heat Flux

Josyula et al, 2014


Unsteady Scramjet Flameholder

26 Instantaneous fuel mole fracPon for 56SLPM and 99SLPM cases

Mean fuel mole fracPon for 56SLPM and 99SLPM cases

Streamwise velocity profiles for RANS (red) hybrid RANS and LES (black) and experiment (symbols)

• Mach 2 cavity flameholder simulated using hybrid RANS/LES (US3D)

• Compared to PIV data

• Characterization of turbulence and mixing in cavity for non-reacting cases

Numerical InvesPgaPon of a Supersonic Cavity Flameholder, David M. Peterson, Ezeldin A. Hassan, Steven G. Tuxle, Mark A. Hagenmaier, Campbell D. Carter, AIAA 2014-‐1158.


Summary

1.  LES and DES as engineering tools 2.  Fatigue loading 3.  Inlet flows 4.  Selected topics in aerothermal prediction


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