Kein Folientitel

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Optimisation in Aeronautics Results from the
INGENET Aeronautics Area
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Task/Application Summary

• Single wing ONERA-M6
• Multi-point airfoil
• Multi-element airfoil at high lift
• Drag reduction for RAE2822
• Optimisation of high-lift multi-element airfoil
• 3-element high-lift airfoil
• Nozzle reconstruction
• Domain decomposition with GAs
• Decentralized Nash strategies to coordinate
  subdomains
• Multi-objective / Multi-disciplinary (CFD/CEM)
  Airfoil Design

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Transonic single-wing optimization case with
inviscid flow
ONERA-M6

• Objective function Minimize drag on
  ONERA M6 wing
• Physical Constraints - Constant Lift, CL0.3,
  at M0.84 - Euler Flow Equations
  Computational grids SAAB 192x32x48 295 000
  grid cells CIRA 144x36x40 207 000 grid cells
• Geometrical Constraints - Prescribed maximum
  wing thickness - Leading edge radius -
  Trailing edge angle

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Transonic single-wing optimization case with
inviscid flow
ONERA-M6
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Transonic single-wing optimization case with
inviscid flow
ONERA-M6

• SAAB The airfoil shape was modified at 48 wing
  span sections using a linear
  combination of 12 Aerofunctions at
  each section
• CIRA 1 The airfoil shape was modified at 4
  wing span sections using a linear
  combination of 12 Aerofunctions at each
  section. Intermediate sections
  were obtained by linear interpolation.
• CIRA 2 Same shape modification as in CIRA 1 a
  linear twist variation from the
  root to the tip section.

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Transonic single-wing optimization case with
inviscid flow
ONERA-M6
SAAB

• Flow solver 3D Euler solver
• Optimization Algorithm - Function and gradient
  computation - Gradients computed by means of
  Euler and Adjoint Euler solutions - Constrained
  steepest descend method

CIRA

• Flow solver Non conservative transonic full
  potential solver
• Optimization Algorithm - Genetic algorithm
  (gradient is not computed) - Selection 2 step
  random walk - Crossover extended intermediate
  recombination with prob.1 - Mutation carried
  out at word level with prob. 0.05 - Population
  size 32

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Transonic single-wing optimization case with
inviscid flow
ONERA-M6
CD convergence history
SAAB
CIRA
30 flow solutions
800 flow solutions
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Transonic single-wing optimization case with
inviscid flow
ONERA-M6
Results with pitching moment constraint CM
-0.132
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Transonic single-wing optimization case with
inviscid flow
ONERA-M6
CD, CM convergence history
SAAB
CIRA
80 flow solutions
1200 flow solutions
Pareto frontier (min CD, max CM)
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Multi-point 2D airfoil design
Multi-point airfoil
Minimisation of an objective function being the
difference between computed/optimised pressure
distribution at two different design points and
pre-defined target pressures for an airfoil The
objective function reads
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Multi-point 2D airfoil design

• Test case bases on a suggestion by T. Labruyere
  (NLR) for the European ECARP project some years
  ago. This test case is currently used for
  validation purposes in three other EC projects.
• The two different design conditions (i1,2) are
  (see table below)
• i1 Typical high-lift airfoil at subsonic
  conditions
• i2 Typical high-speed airfoil at transonic
  conditions

Multi-point airfoil
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Multi-point 2D airfoil design
NTUA Multi-point optimization with
GAs Navier-Stokes analysis tool (turb. mod.
with wall functions) Low-cost inexact
pre-evaluation using local neural networks
Multi-point airfoil
ICD Using ES (1,?) with de-randomized mutation
step size Randomly chosen airfoils as starting
conditions No. of analysis steps less than
1000 Results are directly comparable with
EADS-Ms results
EADS-M Parametrization on both pressure
re-design and multi-point Optimization with 8
Bezier points, two of them are not fixed in
x-direction. Test cases have been run with
different numbers of individuals per
generations. Navier-Stokes on (relatively)
coarse meshes Multi-objective GA (FRONTIER
technology) in use
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Multi-point 2D airfoil design
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Multi-point airfoil
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EADS-M Results Pareto GA 32 x 32
5
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Multi-point 2D airfoil design
Multi-point airfoil
NTUA Low-drag profile
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Multi-point 2D airfoil design
NTUA Results
Multi-point airfoil
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Multi-element airfoil optimization in high-lift
conditions
CIRAThe flow field around the single element
airfoil in cruise conditions is evaluated through
a full potential approximation of the Navier
-Stokes equations, solved using a finite
difference scheme.High lift flow is
evaluated,instead, using an Euler/boundary layer
interaction method. Use of GAs (Genetic
Algorithms)Multi-objective approach Viscous
approach by Euler/boundary layer
coupling B-Spline approach Maximization of cl
at Ma0.2 for two-component airfoil Maximization
of cl at Ma0.2 for three-component
airfoil Maximization of cl at Ma0.2 for
two-component airfoil plus pitch
control Maximization of cl at Ma0.2 and cd,wake
reduction at Ma0.85
Multi-element airfoil
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Multi-element airfoil optimization in high-lift
conditions
CIRA
The initial configuration used for the high-lift
and transonic runs was defined assembling some of
the components reported in the figure. The
N1BT configuration was chosen as base airfoil
for the transonic single-element design point of
the optimization runs reported here.
Configurations N1BC1F and S1N2BC1F were
instead used for high-lift design points.
Multi-element airfoil
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Multi-element airfoil optimization in high-lift
conditions
CIRA
cl maximization at M0.2 with atwo-component
airfoil and control on pitching moment
Multi-element airfoil
32 individuals evolved for 40 generations 8 bits
used for variable encoding mutation set at bit
level with a probability of 2 extended
intermediate crossover with 100 activation
probability
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Multi-element airfoil optimization in high-lift
conditions
CIRA
cl maximization at M0.2 with atwo-component
airfoil and control on pitching moment
Multi-element airfoil
Note that the modified shape is not suitable for
cruise conditions and, therefore, a three-element
airfoil is optimized in the next exercise.
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Optimization of a 3-element high-lift (2D)
configuration
CIRA
cl maximization at M0.2 with a three-component
airfoil
3-element airfoil
Here the shape of each component is kept
unchanged, and the design variables are the
relative positions and rotations of flap and slat.
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Optimization of a 3-element high-lift (2D)
configuration
INRIA Optimize the relative position of slat,
flap and airfoil Standard binary coded GA Euler
approach (finite volume, van Leer MUSCL
approach) Re-meshing with linear-spring
method Lift improvement from 4.9 to 5.13 (after
40 generations) Future Additional optimization
of shape
3-element airfoil
Trieste Star-CD as analysis tool, running on PC
cluster Navier-Stokes with k-? turbulence
model Unstructured grid 120433 grid points,
14530 cells FRONTIER optimizer, parallel
classical GA, 30 ind. x 40 gen. Crossover prob.
0.9, Mutation prob. 0.1 Lift improvements from
2.89 to 3.79 30
NTUA New sensitivity analysis coupled with GAs
and artificial neural networks (RBF networks,
with sensitivity analysis) in order reduce no. of
analysis steps Euler method - unstructured mesh
- same as INRIA
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Optimization of a 3-element high-lift (2D)
configuration
Dassault RA16 geometry Problem to solve related
to overlap, angle and gap (no shape changes so
far), i.e. three design variables per slat and
flap (6 in total) GAs with Nash Equilibrium (2
"players" - for slat and flap) Panel method
coupled with boundary layer approach Lift 5.29
(Nash) versus 4.84 (GA) Future Improvement on
solver (Navier-Stokes?) Domain
decomposition Parallel technique
3-element airfoil
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Optimization of a 3-element high-lift (2D)
configuration
Dassault/LIP6Convergence 600 evaluations Lift
improvement 8
3-element airfoil
Initial Configuration
Optimal Configuration
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Optimization of a 3-element high-lift (2D)
configuration
3-element airfoil
INRIA Slat and flap configuration of initial and
optimised shapes
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Drag reduction on a 2D RAE2822 airfoil Direct
optimization
Task Reduce shock-induced drag for the RAE2822
airfoil at Ma0.73 and ?2o based on an Euler
approach Constraint Lift should be equal to
the original lift
Drag reduction RAE2822
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Drag reduction on a 2D RAE2822 airfoil Direct
optimization
EADS-M results Mach number contoursSingle-objec
tive run 56 reduction in drag(generally no
shock means no drag ... !?)
Drag reduction RAE2822
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Drag reduction on a 2D RAE2822 airfoil Direct
optimization
956
656
EADS-M results Multi-objective
optimization GA 32x32 CL,956 0.8360 CD,956
0.00400 (53) CL,656 0.8345 CD,656
0.00392 (55) CL,initial 0.8364 CD,initial
0.00862
Drag reduction RAE2822
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Drag reduction on a 2D RAE2822 airfoil Direct
optimization
Drag reduction RAE2822
INRIA
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Drag reduction on a 2D RAE2822 airfoil Direct
optimization
INRIA
Drag reduction RAE2822
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Reconstruction of a nozzle with multiple models
Optimization of the shape of a convergent-divergen
t nozzle for transonic flow (involving shocks).

• LIP6/Dassault
• Hierarchical GAs with Multiple Models and
  Multiple Solvers
• Application of multiple model approach results in
  2/7th of CPU time
• Method is based on the same algorithmic
  principle, i.e. using parallel genetic algorithms
  with a hierarchical topology for the exchanges
  when migration takes place.
• Trying to keep accuracy of fine mesh for running
  analysis on coarser mesh(es) in order to reduce
  computation time.
• Synchronous and asynchronous evaluation of
  individuals
• Space marching for CFD, viscous and inviscid
  approach

Nozzle reconstruction
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Reconstruction of a nozzle with multiple models
LIP6/Dassault
Goal rebuild a symmetric convergent/divergent
nozzle with 1-D transonic shocked flows Use a
Hierarchical Parallel GA with multiple models
(from most expensive to cheapest) Try to rebuild
the shape of the nozzle by matching the mach
number distribution generated for the target
nozzle with different grid sizes.
Hierarchical models
Nozzle reconstruction
Target Nozzle
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Reconstruction of a nozzle with multiple models
LIP6/Dassault
Nozzle reconstruction
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Reconstruction of a nozzle with multiple models
LIP6/Dassault
Multiple models, mixed Viscous/Inviscid
Solvers Inviscid, viscous, and mixed
inviscid/viscous
Nozzle reconstruction
HGAs with multiple models the best answer to
the lack of speed dilemma in engineering
environment (3 times faster than other
approaches) Using models of different complexity
significantly is speeding up optimization GAs can
handle approximate models within a hierarchical
topology
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Decentralized Nash strategies to coordinate
subdomains using partial volume information
Linear potential flow in a duct is divided into
two sub-domains of a divergent-convergent nozzle
by least-square minimization in portions of the
overlap.
INRIA Convergent-divergent nozzle Full
potenial method as basic analysis tool Domain
decompositions (2 domains with two sub-solutions
in particular) Nash algorithm based on partial
volume integrals GA optimizer used (optimizing
the generation) Future Use more complex models
(high fidelity models)
Decentralized Nash strategies
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Decentralized Nash strategies to coordinate
subdomains using partial volume information
INRIA
Decentralized Nash strategies
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Multi-objective / Multi-disciplinary (CFD/CEM)
Airfoil Design
Dassault/LIP6 Trade-off solution obtained by a
Nash/GA game based on the red/blue split of the
control points in a Bezier representation One
player for CFD and one player for CEM CFD
method transonic full potential CEM method
Time harmonic Maxwell solver Optimal solution
is a non-trivial solution Experiments
involving a PC cluster
Multi-disciplinary (CFD/CEM) Airfoil
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Multi-objective / Multi-disciplinary (CFD/CEM)
Airfoil Design

• Goal minimize the RCS of an airfoil by
  optimizing the repartition of active antennas
  over the surface of the airfoil
• GA with a repair mechanism and binary
  representationFor a NACA0012 illuminated with a
  0o incident wave repartition of 7 actives
  antennas among 17 sites

Associated polar RCS
Optimal distribution
Multi-disciplinary (CFD/CEM) Airfoil
Dassault/LIP6
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Multi-objective / Multi-disciplinary (CFD/CEM)
Airfoil Design
Goal minimize the RCS for 2 different angular
sectors Pareto GA with a niching mechanism For a
Bi-NACA0012 illuminated by a 45o and 45
incident waves Optimal repartition to minimize
RCS over -55,-35 and 35,55 angular
sectors
Multi-disciplinary (CFD/CEM) Airfoil
Dassault/LIP6
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Multi-objective / Multi-disciplinary (CFD/CEM)
Airfoil Design
Goal minimize the RCS for a multi-element
airfoil For a 3-element airfoil illuminated by a
45o and 45 incident waves Optimal repartition
to minimize RCS over -55,-35 and 35,55
angular sectors
Associated polar RCS
Optimal distribution
Multi-disciplinary (CFD/CEM) Airfoil
Dassault/LIP6
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Multi-objective / Multi-disciplinary (CFD/CEM)
Airfoil Design
INRIA/Dassault
Multi-disciplinary (CFD/CEM) Airfoil
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Multi-objective / Multi-disciplinary (CFD/CEM)
Airfoil Design
INRIA/ Dassault
Multi-disciplinary (CFD/CEM) Airfoil
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Multi-objective / Multi-disciplinary (CFD/CEM)
Airfoil Design
INRIA/ Dassault
Multi-disciplinary (CFD/CEM) Airfoil
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Multi-objective / Multi-disciplinary (CFD/CEM)
Airfoil Design
INRIA/ Dassault
Multi-disciplinary (CFD/CEM) Airfoil
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Conclusions

• 10 test cases have been treated in aerodynamics
• Most of the partners worked with so-called tandem
  partners
• Results have been stored on INGENET data base
• Results can be accessed via the INGENET Web
  site(s)
• INGENET can be seen as a very successful
  network(depending on partner efforts and funding
  criteria)
• Researchers from the Outside World were invited
  to participate in running test cases on
  themselves and reporting on them

Conclusions
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Conclusions (continued)

• Potential of evolutionary computing has been
  demonstrated for design problems of industrial
  interest, such as multi-point and
  multi-disciplinary applications
• The aerodynamic data-base of INGENET can now be
  used to assess new developments and to test new
  evolutionary algorithms for aerodynamic
  applications
• The presented results evidenced a progress
  towards more robust and accurate optimization and
  attention towards industrial needs
• The computational complexity of some examples is
  remarkable and evidences how further progresses
  in the application of evolutionary algorithms to
  aerodynamic and multi-disciplinary design tasks
  will require also the development of algorithms
  capable of taking advantage of decentralized and
  asynchronous computational resources.

Conclusions