Applied Modeling & Simula1on Seminar Series, August 6, 2014 NASA Ames Research Center
Adap%ve Aeroelas%c Wing Shape Op%miza%on for High-‐Li; Configura%ons
Mentor: Nhan T. Nguyen, PhD NASA Ames -‐ Intelligent Systems Division
Gustavo E. C. Fujiwara M.S. Aerospace Engineering -‐ University of Illinois at Urbana-‐Champaign PhD Candidate Aeronau1cs and Astronau1cs -‐ University of Washington
2014 Summer Intern at NASA Ames
Funded by NASA ARMD – Fixed Wing Project
AGENDA
2
① MOTIVATION
② INTRODUCTION
③ FRAMEWORK
④ METHOD
⑤ RESULTS
⑥ FUTURE WORK
⑦ AKNOWLEDGEMENTS
AGENDA
3
① MOTIVATION
② INTRODUCTION
③ FRAMEWORK
④ METHOD
⑤ RESULTS
⑥ FUTURE WORK
⑦ AKNOWLEDGEMENTS
4
• Global demands for more sustainable technologies ð more energy-‐efficient airframes
• New genera1on aircra] concepts feature: ✈ Be_er engine performance (quieter, cleaner) ✈ Higher aerodynamic efficiency ✈ Lighter materials (advanced composites)
① MOTIVATION
THE CHALLENGES • The use of composite materials increase airframe flexibility for same load capacity
• More flexible structures ð can change aircra] op1mal shape during flight
ð can degrade the aerodynamic efficiency
• Reduced rigidity can also ð affect flight dynamic characteris1cs
ð reduce structural safety margins (flu_er, etc)
• Boeing 787 wing at 0-‐g load
5
① MOTIVATION
Example of flexibility of current composite aircra]
Photo credits: YK , Kenneth Low (Airliners.net)
6
① MOTIVATION
12-‐] wing 1p deflec1on à ~12% of wing semispan
Example of flexibility of current composite aircra] • Boeing 787 wing at 1-‐g load
Photo credits: YK , Kenneth Low (Airliners.net)
AGENDA
7
① MOTIVATION
③ FRAMEWORK
④ METHOD
⑤ RESULTS
⑥ FUTURE WORK
⑦ AKNOWLEDGEMENTS
② INTRODUCTION
8
• A current research effort to address the aerodynamic efficiency/flight control issues of flexible wings:
ð Variable Camber Con1nuous Trailing-‐Edge Flap (VCCTEF)
② INTRODUCTION
VCCTEF CONCEPT IDEA ð Tailor the wing’s spanwise load distribu1on
ð Aeroelas1cally re-‐adapt it back t o t he op1ma l s hape , a s condi1ons change during flight
Courtesy of The Boeing Co.
VCCTEF Anima1on
Courtesy of Michael A]osmis and David Rodriguez
② INTRODUCTION
VCCTEF Anima1on
② INTRODUCTION
CRUISE Configura1on HIGH-‐LIFT Configura1on
Courtesy of Michael A]osmis and David Rodriguez
• Cruise Configura1on Model
Computa%onal Work in Drag Reduc%on Op%miza%on ¥ -‐ Sonia Lebofsky, Eric Ting, Nhan Nguyen (Vorview) -‐ Michael A]osmis, David Rodriguez (Cart3D)
Experimental Work (Wind Tunnel Tests) * -‐ University of Washington (UW): Prof. Eli Livne, Nathan Precup -‐ Boeing: James Umes, Sr., Chester Nelson -‐ Model based on the Generic Transport Model (GTM) – B757 devised -‐ Designed to match flexibility of about 10% wing 1p deflec1on
11
② INTRODUCTION
¥Lebofsky, S., Ting, E., Nguyen, N., “Aeroelas1c Modeling and Drag Op1miza1on of Aircra] Wing with Variable Camber Con1nuous Trailing Edge Flap”, AIAA Avia1on 2014, 32nd AIAA Applied Aerodynamics Conference
*Nguyen, N., Precup, N., Umes, J., Nelson, C., Lebofsky, S., Ting, E., Livne, E., “Experimental Inves1ga1on of a Flexible Wing with a Variable Camber Con1nuous Trailing Edge Flap Design”, AIAA Avia1on 2014, 32nd AIAA Applied Aerodynamics Conference
Courtesy of University of Washington
• High-‐Li] Configura1on Model
12
② INTRODUCTION
Computa%onal Op%miza%on Framework -‐ Currently under development at NASA Ames -‐ Michael A]osmis, David Rodriguez – OVERFLOW -‐ Challenges: Grid deforma1on tool (surface/volume meshes) Computa1onal cost ro run RANS solvers
Experimental Work (Wind Tunnel Tests) -‐ UW: Prof. Eli Livne, Nathan Precup -‐ Currently being tested -‐ Includes a variable camber Krueger (VCK) Flap -‐ Single element slo_ed inboard flap -‐ 3-‐chordwise-‐segment VCCTEF elsewhere
Courtesy of University of Washington
• High-‐Li] Configura1on Model
13
② INTRODUCTION
Courtesy of University of Washington
• Objec1ve
14
Develop an aeroelas1c framework ð quick turnaround mul1disciplinary op1miza1on of a flexible wing with variable camber con1nuous trailing edge flap (VCCTEF) for high-‐li] configura1ons (takeoff and landing)
② INTRODUCTION
§ Develop a low-‐fidelity 3D aerodynamic code capable of handling stall characteris1cs (Cl, Cm) § Couple structural code to calculate sta1c wing deflec1on § Allow fast computa1on for op1miza1on purposes
• Requirements
• Poten1al Modeling Complica1ons
Ø Flap deflec1ons increase not only the loads but also the nose-‐down pitching moment Ø Nose-‐down moment loads tend to twist the wing downwards, thereby decreasing angle of a_ack and li] (similar to control reversal)
Ø Op1mizing flexible wings for high-‐li] can be counterintui1ve
AGENDA
15
① MOTIVATION
② INTRODUCTION
④ METHOD
⑤ RESULTS
⑥ FUTURE WORK
⑦ AKNOWLEDGEMENTS
③ FRAMEWORK
16
③ FRAMEWORK Mul%disciplinary Op%miza%on Framework Strategy: • Coupled aerodynamics/structures • Sta1c aeroelas1c calcula1ons • Op1mizes flap deflec1on schedule for:
Ø CLmax, or Ø (CL/CD)max
OPTIMIZER
GEOMETRY GENERATOR
AERODYNAMICS (Nonlinear 3D code) STRUCTURES
CONVERGENCE ?
CLmax (CL/CD)max
YES
NO
TASK 1
TASK 2 OPTIMIZER
GEOMETRY GENERATOR AERODYNAMICS STRUCTURES
CONVERGENCE ?
CLmax
(CL/CD)max
YES
NO
17
Low-‐fidelity aerodynamic code structure:
AERODYNAMICS 2D Viscous Data
MSES
2D RANS
XFOIL
Wind Tunnel
3D Code VLM
Panel Method
Weissinger
③ FRAMEWORK
AGENDA
18
① MOTIVATION
② INTRODUCTION
③ FRAMEWORK
⑤ RESULTS
⑥ FUTURE WORK
⑦ AKNOWLEDGEMENTS
④ METHOD
19
• Baseline Airfoil example: ‘bacj.dat’ (supercri1cal) publicly available at UIUC – Airfoil Database
④ METHOD
2D solver to obtain sec%onal viscous data (XFOIL)
TASK 1 2D Viscous Data
MSES
2D RANS
Wind Tunnel
XFOIL
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
x
z
VCCTEF
Supercritical Airfoil δ1 = 5°, δ2 = 5°, δ3 = 5°
δ1 =10°, δ2 =10°, δ3 =10°
δ1 =15°, δ2 =15°, δ3 =15°
δ1 =20°, δ2 =20°, δ3 =20°
• Build a 2D Viscous Aerodynamic Data Bank
20
α = -‐5˚ α = -‐4˚ α = -‐3˚ ...
Re = 100k Cl, Cd, Cm
Re = 200k ...
Re = 300k ...
... ...
α
Re
④ METHOD
Offline Calcula1ons -‐ XFOIL • Re = 100k -‐ 1.1M (UWAL model, chords) • AoA = -‐5˚ – 25˚ (Cl, Cd, Cm)
TASK 1 2D Viscous Data XFOIL
21
TASK 2 3D Code VLM
Panel Method
Weissinger
Vortex La^ce Method code chosen • AVL – Athena Vortex La�ce* • Publicly available (open source) • Validated
*Mark Drela, Harold Youngreen – MIT Based on the classic work of Lamar (NASA codes), E. Lan and L. Miranda (VORLAX)
④ METHOD
22
Nonlinear Vortex La^ce Method • Lower computa1onal cost and lower fidelity compared to a Navier-‐Stokes solver • Itera1vely changes wing’s local incidence/camber to match Cl/Cm from 2D viscous data
④ METHOD TASK 2 3D Code VLM
Decambering Method Inspira%on • XFOIL – Boundary Layer Thickness Growth
23
α = 4˚ α = 0˚
α = 12˚
α = 18˚
④ METHOD TASK 2 3D Code VLM
24 2003 – Funded by NASA Langley – North Carolina State University *Mukherjee, R., Gopalarathnam, A., Kim. S., “AN ITERATIVE DECAMBERING APPROACH FOR POST-‐STALL PREDICTION OF WING CHARACTERISTICS USING KNOWN SECTION DATA”, AIAA 2003, 41st AIAA Aerospace Sciences Mee1ng
④ METHOD TASK 2 3D Code VLM
DETAILS: THE DECAMBERING APPROACH
2) Get Cl, Cm and downwash angles (ω) along span
6) Change incidence δ1 and camber δ2 in each sta1on and rerun STOP CRITERION
| Cl (sec1on) -‐ Cl 2D Visc (αeff) | < 0.001 | Cm(sec1on) -‐ Cm 2D Visc(αeff) | < 0.001
3) For each sta1on (strip) calculate local αeff
4) Calculate gradients/sensi1vi1es (Jacobian): -‐ ∂ Cl in each sta1on i due to incidence (δ1) perturba1on p at sta1on j -‐ ∂ Cm in each sta1on i due to incidence (δ1) perturba1on p at sta1on j -‐ ∂ Cl in each sta1on i due to camber (δ2) perturba1on p at sta1on j -‐ ∂ Cm in each sta1on i due to camber (δ2) perturba1on p at sta1on j
1) Input Aircra] Geometry and Run AVL (Linear VLM code)
Incidence
δ1 δ2
Camber
5) Mul1variable Newton-‐Raphson Itera1on to compute δ1 and δ2 ~ 2π
α
Cl
2D viscous data
ΔCl Residual
α effec1ve
Linear VLM sec1on
α
Cm
α effec1ve
ΔCm Residual
25 *Mukherjee, R., Gopalarathnam, A., Kim. S., “AN ITERATIVE DECAMBERING APPROACH FOR POST-‐STALL PREDICTION OF WING CHARACTERISTICS USING KNOWN SECTION DATA”, AIAA 2003, 41st AIAA Aerospace Sciences Mee1ng
④ METHOD TASK 2 3D Code VLM
DETAILS: THE DECAMBERING APPROACH
Jacobian Matrix (Gradients/Sensi1vi1es)
Residuals (ERROR)
Incidence/ Camber
Correc1ons
Aircra; Geometry Input
Run VLM Get Cl, Cm, ω along span
At each sta%on:
Cl2D VLM (i) = Cl2D visc (αeff) ? Cm2D VLM (i) = Cm2D visc (αeff) ?
YES
NO Change Incidence/Camber
Mul%variable Newton-‐Raphson
END
START
αeff
~ 2π
α
Cl
α effec1ve
Linear VLM sec1on
2D viscous data
α
Cm
α effec1ve
26
For Improved Efficiency…
• All decambering variables can be dealt with using the RHS only • Avoid matrix inversion every itera1on
[AIC][Γ] = [V∞.n+Vind⊥]
④ METHOD
Incidence
δ1 δ2
Camber
TASK 2 3D Code VLM
AGENDA
27
① MOTIVATION
② INTRODUCTION
③ FRAMEWORK
④ METHOD
⑥ FUTURE WORK
⑦ AKNOWLEDGEMENTS
⑤ RESULTS
28
⑤ RESULTS TASK 1 2D Viscous Data XFOIL
Matlab automated tool to run XFOIL in Batch
29
TASK 1 2D Viscous Data XFOIL
Effect of Boundary Layer Transi1on – Ncri1cal Method
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
-0.05
0
0.05
x
z
VCCTEF
Supercritical Airfoil δ1 =5°, δ2 =5°, δ3 =5°,
-5 0 5 10 15 20
0.6
0.8
1
1.2
1.4
1.6
1.8
α [°]
Cl
0.6 0.8 1 1.2 1.4 1.6
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Cl
Cd
-5 0 5 10 15 20 25-0.28
-0.26
-0.24
-0.22
-0.2
-0.18
-0.16
-0.14
-0.12
-0.1
α [°]
Cm
Re=0.662M - Ncrit=0.5
Re=0.662M - Ncrit=9
Re=0.662M - Ncrit=12
⑤ RESULTS
30
⑤ RESULTS TASK 2 3D Code VLM
Matlab automated tool to run AVL
0 2 4 6 8 10 12 14 16 18 200
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
α [deg]
CL
Wing-BodyWing-Body-Botton-Top-WallsWing-Body-All-Walls
Streamwise Cuts Conversion
31 2 3 4 5 6 7 8
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6UWAL - VCCTEF Model
X [ft]
Y [ft
]
⑤ RESULTS TASK 2 3D Code VLM
Half Wing-‐Body Symmetry Condi1on
32
⑤ RESULTS TASK 2 3D Code VLM
Half Wing-‐Body Symmetry Condi1on + Top/Bo_om Walls Only
33
⑤ RESULTS TASK 2 3D Code VLM
Half Wing-‐Body Symmetry Condi1on + All Walls
34
⑤ RESULTS TASK 2 3D Code VLM
0 2 4 6 8 10 12 14 16 18 200
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
α [deg]
CL
Wing-BodyWing-Body-Botton-Top-WallsWing-Body-All-Walls
35
Agrees with UWAL Rigid Wing Data from Nhan’s Paper (see next slide)
CLα = 4.33-‐4.53
CLα = 4.47-‐4.68
CLα = 4.64 – 4.83
⑤ RESULTS
LINEAR AVL TASK 2 3D Code VLM
36
UWAL RIGID -‐ Paper CLα = 4.49
• Reconstructed data for a rigid wing based on flexible wing tunnel data • Data corrected for wind tunnel walls
⑤ RESULTS
*Nguyen, N., Precup, N., Umes, J., Nelson, C., Lebofsky, S., Ting, E., Livne, E., “Experimental Inves1ga1on of a Flexible Wing with a Variable Camber Con1nuous Trailing Edge Flap Design”, AIAA Avia1on 2014, 32nd AIAA Applied Aerodynamics Conference
UWAL RIGID -‐ Paper CLα = 4.31
TASK 2 3D Code VLM
NON-‐LINEAR AVL
37
⑤ RESULTS
• Code Modifica1ons to Include: – Maximum 2D Cl and Non-‐lineari1es in li] (near stall)
– Viscous drag (profile + skin fric1on)
TASK 2 3D Code VLM
Valida1on with Experimental Data
38
⑤ RESULTS
*Mukherjee, R., Gopalarathnam, A., Kim. S., “AN ITERATIVE DECAMBERING APPROACH FOR POST-‐STALL PREDICTION OF WING CHARACTERISTICS USING KNOWN SECTION DATA”, AIAA 2003, 41st AIAA Aerospace Sciences Mee1ng
TASK 2 3D Code VLM
39
Aircra; Geometry Input
Run VLM Get Cl, Cm, ω along span
At each sta%on:
Cl2D VLM (i) = Cl2D visc (αeff) ? Cm2D VLM (i) = Cm2D visc (αeff) ?
YES
NO Change Incidence/Camber
Mul%variable Newton-‐Raphson
END
START
αeff
⑤ RESULTS TASK 2 3D Code VLM
Valida1on with Experimental Data
40
-10 0 10 20 30 40 50 60-0.5
0
0.5
1
1.5
2
2.5
α [°]
CL
2D Viscous Data3D Exp. - AR=12 - λ=1.0AVL (3D linear)AVL-Modified (3D nonlinear)
⑤ RESULTS TASK 2 3D Code VLM
Valida1on with Experimental Data
41 0 0.5 1 1.5 2 2.5-0.12
-0.1
-0.08
-0.06
-0.04
-0.02
0
0.02
0.04
2y / b
Cm
Lin-α=0Nonlin-α=0Lin-α=5Nonlin-α=5Lin-α=10Nonlin-α=10Lin-α=15Nonlin-α=15Lin-α=19Nonlin-α=19Lin-α=27Nonlin-α=27
0 0.5 1 1.5 2 2.50
0.5
1
1.5
2
2.5
3
2y / b
Cl
Cl Max 2DLin-α=0Nonlin-α=0Lin-α=5Nonlin-α=5Lin-α=10Nonlin-α=10Lin-α=15Nonlin-α=15Lin-α=19Nonlin-α=19Lin-α=27Nonlin-α=27
⑤ RESULTS TASK 2 3D Code VLM
42
0 1 2 3 4 5 60
1
2
3
c.C
l
0 1 2 3 4 5 6
0
0.5
1
b/2
Cl
α =0α =2.5α =5α =7.5α =10α =12.5α =15α =17.5α =20
2D viscous sec1onal stall limit
• UWAL Model Geometry with NACA0012 sec1onal data • The developed Nonlinear Vortex La�ce code results:
• CLα: slope agreement with the linear prediciton for the finite wing • Cl x span: good agreement with theory. As angle of a_ack increases, the sta1ons increase local li] coefficient un1l they reach the maximum 2D Cl from the viscous data, yielding the 3D stall behavior and propaga1on characteris1cs of the CLxα curve
0 5 10 15 20 25
0
0.2
0.4
0.6
0.8
1
1.2
AoA [deg]
CL
NACA0012 - 2D Viscous Data - XFOILAVL - 3D linearAVL-Modified - 3D nonlinear
⑤ RESULTS TASK 2 3D Code VLM
43
• Established a mul1disciplinary op1miza1on framework strategy
• Developed a low-‐fidelity nonlinear 3D aerodynamic tool Ø Using known sec1onal data Ø Capable of capturing CLmax and handling stall Ø Modified an exis1ng Vortex La�ce Method code (AVL)
• Results showed successful predic1on of 3D nonlinear aerodynamic characteris1cs, while maintaining low computa1onal cost
This summer so far …
⑤ RESULTS
AGENDA
44
① MOTIVATION
② INTRODUCTION
③ FRAMEWORK
④ METHOD
⑤ RESULTS
⑦ AKNOWLEDGEMENTS
⑥ FUTURE WORK
45
⑥ FUTURE WORK
Program the structural code
FEM
STRUCTURES Galerkin Method
46
AERODYNAMICS 2D Viscous Data 2D RANS (OVERFLOW)
XFOIL
3D Code VLM (AVL)
Panel (Panair)
VLM (Vorview)
Modify the aerodynamic tool
⑥ FUTURE WORK
47
Integrate aero/structural code with the op%mizer
OPTIMIZER
GEOMETRY GENERATOR
AERODYNAMICS (Nonlinear 3D code) STRUCTURES
⑥ FUTURE WORK
48
⑦ AKNOWLEDGEMENTS
• NASA ARM – Fixed Wing Project • Adap1ve Aeroelas1c Shape Control (AASC) under Aerodynamic Efficiency sub-‐project
• Dr. Ce1n Kiris • NASA AMS Division: Michael A]osmis, David Rodriguez, others
• Dr. Nhan Nguyen • NASA Intelligent Systems Division: Ezra Tal, Sonia Lebofsky, Eric Ting, others
• Dr. Michael Bragg, Dr. Eli Livne at the University of Washington
THANK YOU
BACKUP SLIDES
49
Nonlinear Weissinger • Swept Wing 2D and 3D CFD data
50
The results are from CFD computed at a Re = 3.0 M for NACA 4415 airfoil Wing: AR = 12, Taper =1 (constant-‐chord wing)
Source: Paul, R. C., Gopalarathnam, A., "Itera1on schemes for rapid post-‐stall aerodynamic predic1on of wings using a decambering approach", INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, Int. J. Numer. Meth. Fluids (2014), Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/fld.3931
CFD Data
51 Source: Paul, R. C., Gopalarathnam, A., "Itera1on schemes for rapid post-‐stall aerodynamic predic1on of wings using a decambering approach", INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, Int. J. Numer. Meth. Fluids (2014), Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/fld.3931
52 Source: “Itera1on schemes for rapid post-‐stall aerodynamic predic1on of wings using a decambering approach” -‐ 2014 -‐ Interna1onal Journal for Numerical Methods in Fluids -‐ Wiley Online Library