Transcript No Slide Title

ADVENT
ADVanced EvolutioN Team
Aim: To Develop advanced numerical tools and apply them
to optimisation problems in aeronautics.
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Outline
Why are we interested in evolutionary
optimisation?
How does it work?
What have we solved so far?
We are we going now?
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Why Evolution?
 Traditional optimisation methods will fail to find the real
answer in most real engineering applications.
 Techniques such as Evolution Algorithms can explore
large variations in designs. They also handle errors and
deceptive sub-optimal solutions with aplomb.
 They are extremely easy to parallelise.
 They can provide optimal solutions for single and multiobjective problems.
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Some Applications to Date…
Evolution is being applied in thousands of fields
right now. Some examples in aviation are:
Whole wing design for drag reduction.
Radar cross-section minimisation.
Whole turbofan layout and blade design.
Formation flight optimisation for maximum
engagement success.
Autopilot design and trajectory optimisation.
As well as combinations of the above.
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What Are Evolution Algorithms?
 Based on the Darwinian theory of
evolution  Populations of individuals
evolve and reproduce by means of
mutation and crossover operators and
compete in a set environment for survival
of the fittest.
Evolution
Crossover
Mutation
Fittest
 Computers perform this evolution process as a mathematical
simplification.
 EAs move populations of solutions, rather than ‘cut-and-try’ one to
another.
 EAs applied to sciences, arts and engineering.
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Hierarchical Topology-Multiple Models
Model 1
precise model
Exploitation
Model 2
intermediate
model
Model 3
approximate
model
Exploration
Evolution Algorithm
We use a technique that
finds optimum solutions by
using many different
models, that greatly
accelerates the optimisation
process.
Interactions of the layers:
solutions go up and down
the layers.
Time-consuming solvers
only for the most promising
solutions.
Evaluator
Asynchronous Parallel
Computing
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Results So Far…
 The new technique is
approximately three times faster
than other similar EA methods.
Evaluations
CPU Time
Traditional
2311  224
152m  20m
New
Technique
504  490
(-78%)
48m  24m
(-68%)
 A testbench for single and multiobjective problems has been
developed and tested
 We have successfully coupled the optimisation code to different
compressible and incompressible CFD codes and also to some aircraft
design codes
CFD
Aircraft Design
HDASS
MSES
FLO22
Nsc2ke
XFOIL
Flight Optimisation Software (FLOPS)
ADS (In house)
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Results So Far… (2)
 Constrained aerofoil design for transonic transport
aircraft  3% Drag reduction

UAV aerofoil design
-Drag minimisation for high-speed transit and
loiter conditions.
-Drag minimisation for high-speed transit and
takeoff conditions.
 Exhaust nozzle design for minimum losses.
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Results So Far… (3)
Three element aerofoil reconstruction from
surface pressure data.
 UCAV MDO
Whole aircraft multidisciplinary design.
Gross weight minimisation and cruise efficiency
Maximisation. Coupling with NASA code FLOPS
2 % improvement in Takeoff GW and Cruise Efficiency
AF/A-18 Flutter model validation.
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An Example: Aerofoil Optimisation
Property
Mach
Reynolds
Lift
Flt. Cond. 1
Flt Cond.2
0.75
0.75
9 x 106
9 x 106
0.65
0.715
Constraints:
• Thickness > 12.1% x/c
(RAE 2822)
• Max thickness position
= 20% ® 55%
To solve this and other problems standard
industrial flow solvers are being used.
Aerofoil
Traditional Aerofoil
RAE2822
Conventional
Optimiser [Nadarajah
[1]]
New Technique

cd
[cl = 0.65 ]
cd
[cl = 0.715 ]
0.0147
0.0185
0.0098
(-33.3%)
0.0130
(-29.7%)
0.0094
(-36.1%)
0.0108
(-41.6%)
 For a typical 400,000 lb
airliner, flying 1,400
hrs/year:
 3% drag reduction
corresponds to 580,000
lbs (330,000 L) less fuel
burned.
[1] Nadarajah, S.; Jameson, A, " Studies of the Continuous and Discrete Adjoint Approaches to Viscous Automatic Aerodynamic Shape
Optimisation," AIAA 15th Computational Fluid Dynamics Conference, AIAA-2001-2530, Anaheim, CA, June 2001.
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Aerofoil Optimisation (2)
Aerofoil Characteristics cl = 0.65
Aerofoil Characteristics cl = 0.715
Delayed drag
divergence at
high Cl
Delayed drag
divergence at
low Cl
Aerofoil
Characteristics
M = 0.75
Delayed drag
rise for
increasing lift.
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Second Example: UCAV Multidisciplinary
Design Optimisation - Two Objective Problem
Cruise efficiency maximisation and gross weight minimisation
Cruise 40000 ft,
Mach 0.9, 400 nm
Taxi
Release Payload
1800 Lbs
Accelerate
Mach 1.5, 500 nm
Climb
20000 ft
Maneuvers at
Mach 0.9
Release Payload
1500 Lbs
Descend
Takeoff
Landing
Engine Start and warm up
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UCAV MDO Design (2)
Best for Obj 1
Nash Equilibrium
Compromised solution
Best for Obj 2
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UCAV MDO-MO (2) Comparison
Variables
Pareto
Member 0
Pareto
Member 3
Pareto
Member 7
Nash
Equilibrium
Aspect Ratio
4.76
5.23
5.27
5.13
Wing Area (sq ft)
629.7
743.8
919
618
Wing Thickness (t/c)
0.046
0.050
0.041
0.021
Wing Taper Ratio
0.15
0.16
0.17
0.17
Wing Sweep (deg)
28
25
27
28
Engine Thrust (lbf)
32065
32219
32259
33356
Gross Weight
(Lbs)
57540
59179
64606
62463
Nash Point
Decreasing Gross Weight
MCRUISE.L/DCRUISE
22.5
25.1
Increasing Cruise Efficiency
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27.5
23.9
Outcomes
 The new technique with multiple models: Lower the computational expense dilemma
in an engineering environment (three times faster)
 Direct and inverse design optimisation problems have been solved for one or many
objectives.
 Multi-disciplinary Design Optimisation (MDO) problems have been solved.
 The algorithms find traditional classical results for standard problems, as well as
interesting compromise solutions.
 In doing all this work, no special hardware has been required – Desktop PCs networked
together have been up to the task.
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What Are We Doing Now?
 A Hybrid EA - Deterministic optimiser.
 EA + MDO : Evolutionary Algorithms Architecture for Multidisciplinary Design
Optimisation
We intend to couple the aerodynamic optimisation with:
o Aerodynamics – Whole wing design using Euler codes.
o Electromagnetics - Investigating the tradeoff between efficient
aerodynamic design and RCS issues.
o Structures - Especially in three dimensions means we can investigate
interesting tradeoffs that may provide weight improvements.
o And others…
Wing MDO using Potential
flow and structural FEA.
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THE END
Questions?
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