Optimization Framework for Design of Morphing Wings Jian Yang, Raj Nangia & Jonathan Cooper Department of Aerospace Engineering, University of Bristol, UK & John Simpson Fraunhofer IBP, Germany AIAA Aviation 2014 Atlanta 16-20 July 2014
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Optimization Framework for Design of Morphing Wings
Jian Yang, Raj Nangia & Jonathan Cooper
Department of Aerospace Engineering, University of Bristol, UK
&
John Simpson Fraunhofer IBP, Germany
AIAA Aviation 2014 Atlanta 16-20 July 2014
Traditional Aircraft Design • Single point for the
design • Minimum weight • Maximum CL/CD • Suboptimal elsewhere
Aircraft Morphing Morphing, when applied to aerospace vehicles, is a technology or set of technologies applied to a vehicle that allow its characteristics to be changed to achieve better performance or to allow the vehicle to complete tasks it could not otherwise do.
Jason Bowman, AFRL/VSSV
3
Configuration Morphing • Change in planform
– Aircraft control
– Aircraft performance
• Change in mission
– High aspect-ratio glide
– Attack mode
4
MFX 1, Span & Sweep
Configuration Morphing
5
Baseline
(Joshi et al., 2004, AIAA)
Performance Morphing
• Change in structural properties
– Stiffness
– Camber
– Leading / trailing edge shape
• Aircraft control
• Aircraft performance
– Lift / drag
– Roll control
– Loads
6 NASA, Active Aeroelastic Wing (AAW)
NASA, Active Flexible Wing (AFW)
Variable Stiffness Morphing
• Range of different adaptive stiffness methodologies to be explored
• Vary EIx, EIy, GJ, flexural axis using changes in internal structure
• Changes to spar orientation, rib position, spar cap position – used in several previous EU projects
– 3AS, SMorph, NOVEMOR
7 7
Aims • Develop an inverse design approach
- optimise internal wing stiffness distribution - morphing capability.
• Meet optimal aerodynamic shape throughout the
flight envelope • Define the required changes in stiffness / elastic axis • Minimize the amount of morphing • Minimize the weight • Satisfy any other constraints – to be defined
progressively
Inverse Design Approach
9
• Define required aerodynamic distributions
• Define required wing shapes – Twist / camber / bending
• Optimise weight to determine stiffness distributions
• Evaluate required stiffness changes
• Determine morphing concept(s) to achieve stiffness changes
0 0.5 1 0
2
4
6
8
10
0
0.5
1
y
x/cchor
dwis
e lo
adin
g Wing Aerodynamic shape
Via Lifting Surface VL or DL (lattice) (Lamar) mean camber for minimum drag
wing
Aspect ratio (AR) 12
Taper ratio 0.5
Sweep angle 10 degrees
Semi-span 20 meters
• Planform dimensions
0 0.2 0.4 0.6 0.8 1
0
0.05
0.1
x/c
z/c
Mean camber lines
0 5 10 15 20
0
5
x
y
vortex panels
Camber lines required to achieve M0.74 loading
Aerodynamic shape design
- Lifting Surface, Mach Effect
0 0.25 0.4 0.6 0.74 10
0.5
1
1.5
2
Mach number
Required C
L
CL vs Mach
• ΔCp distributions
0
0.5
1 0 0.2 0.4 0.6 0.8 1
0
0.5
1
1.5
2
2.5
3
y/sx/c
C
p M=0.74
00.5
1 0
0.5
10
1
2
3
y/sx/c
C
p M=0.6
00.5
1 0
0.5
1
0
1
2
3
y/s
x/c
C
p
M=0.4
Design for 4 different test cases – thin lifting surface
Aerodynamic shape – Super-Critical (twist & Camber)
- Panel & Euler (AR=12, Ma=0.74)
0 5 10 15 20
0
5
x
y
vortex panels Non-D
0
0.5
1 0 0.2 0.4 0.6 0.8 1
0
0.5
1
1.5
2
2.5
3
y/sx/c
Cp
Euler
Panel
Lifting
Surface x/s
Aerodynamic shape Different approaches, 0.5 CL
01
23 0
5
10
-2
-1
0
1
y
M0.74, pressure distribution
x
Cp
Structural model
Cross section
𝑡𝐿
𝑡𝑅
𝑏𝑐
𝑑
𝑡𝐻
Weight Optimisation Minimize weight
• Consider deflection/stress constraints & minimum gauge of
design variables.
• Use optimality criteria method
Application: Aero design
16
camber ∆Cp Cp√x
Mach 0.74
Mach 0.6
Mach 0.4
Mach 0.25
∆Cp (using M0.74 camber)
17
∆Cp
Cp√x
Mach 0.6 Mach 0.4 Mach 0.25
Spanwise loadings - effect of M
18
Xcp
Enlarged
Non-D by CL
CLL c/cav
0.74M camber. Evaluated at different test cases
Local AoA required to achieve reference loading of M0.74
with M0.74 camber
19
∆Cp Distributions
before --- & after adding twist ---
20
Mach 0.25
-30%
Mach 0.6 Mach 0.4
∆Cp√x
∆Cp
Note -30%
Structural Optimisation, 0.74M solution process
21
0 50
2
4
6
8
10
12
14
16
18
20
x
y
(b) (a)
1 2 3 4 5 6 7 8 9 103000
3500
4000
4500
5000
Iteration number
weig
ht
(kg)
Convergence
Initial design
Flex. axis varies as function of tR / tL
Weight Convergence - fast
Predicted Jig shapes & Twist, different M
22
Mach 0.6
Mach 0.25
Mach 0.74
Mach 0.4
Using M0.74 camber Achieving M0.74 pressure dist using wingbox twist only
Morphing required (Twist only example from half span)
23
GJ Elastic twist ∆GJ
Morphing required (Twist & camber morphing example)
24
GJ Elastic twist ∆GJ
Morphing required (Twist & camber morphing example)
25
GJ Elastic twist ∆GJ
GJ Elastic twist ∆GJ
Twist & camber
Twist
Note Strong Reduction
Camber morphing case • TE Morph: to achieve desired spanwise loading and reduce
RBM
26
0 5 10 15 20
0
2
4
6
x
y
LE area
TE area
Wingbox area
LE morphing effects
• LE Morph: to reduce leading edge suction and flow separation
27
LE morphing
28
Mean camber shape
• Variations from M 0.74 to M 0.25 case
29
Conclusions • Multiple point design useful for enhancing aircraft
efficiency
• Adaptive wing / morphing concepts reviewed
• An inverse design approach to optimise the internal stiffness distribution & morphing capabilities
• Application to a regional aircraft wing
• Weight reductions achieved via structural optimisation
• Required morphing examined to meet the multi-design points
• Need a lot of twist morphing to achieve required shape over flight envelope
• Use of camber morphing reduces the amount of required twist
30 30
Ongoing Work
• Continue with aerodynamic shape determination – Configuration / flight conditions (Panel, Euler)
– Winglet
– Controls
• Implement inverse approach on coupled-beam model, using more advanced Aero – Panel & Euler, Bodies
• Determine required stiffness variations in terms of morphing approaches
• Investigate reasonable morphing requirements
31 31
0
1
2
30
510
-0.2
-0.1
0
0.1
0.2
yx
z
PANAIR Results
• M0.25, varying twist
0
1
2
3 0
5
10
-3
-2
-1
0
1
yx
Cp
Euler results
33
Design M 0.74 Off-design, Mach 0.6
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