Multidisciplinary Design Optimisation of Unmanned
Aerial Vehicles (UAV) using Multi

Criteria
Evolutionary Algorithms
Eleventh Australian International Aerospace Congress 13

17 March , Melbourne Convention Centre and
Australian International Airshow 2005 at Avalon Airport Design November 15

19, 2004
L. F. Gonz
á
lez
,
E. J. Whitney, K. Srinivas, K.C Wong
The University of Sydney, Australia
J. Périaux
Dassault Aviation
–
Pole Scientifique, INRIA
Sophia Antipolis, OPALE project associate
OUTLINE
Introduction
Unmanned Aerial Vehicle (UAV/UCAV) Design
Requirements
The need and requirements for a Multidisciplinary
Design Optimsation Framework in Aeronautics
Theory
Evolution Algorithms (EAs).
Multidisciplinary
–
Multi

objective Design
Hierarchical Asynchronous Evolutionary Algorithm
(HAPEA)
.
Applications: UAV Design
Conclusions
OUTLINE
UAVDESIGN REQUIREMENTS
Use and development of UAV for military
and civilian applications is rapidly
increasing.
Similar to the manned aircraft the challenge
is to develop trade

off studies of optimal
configurations to produce a high
performance aircraft that satisfy the mission
requirements.
UAV systems are ever increasingly
becoming important topics for aerospace
research and industrial institutions.
There are difficulties in these new concepts
because of the compromising nature of the
missions to be performed, like high

or
medium

altitude surveillance, combat
environments (UCAV) and many others.
Multi

missions
high
–
medium

altitude surveillance
High
Performance
Complex
–
trade

offs
Optimization

Optimal Solution(S)
Pareto optimal
Surface of UAV,
μUAV
MDO Complex Task

UAV

Example
Multiple Goals
Minimise

Maximise
Purchase Price
Aerodynamic
Performance
Takeoff weight
Multiple Disciplines
Structures
Fight Controls
Aero acoustics
Propulsion
Sensors
Aero elasticity
Aerodynamics
Search Space
–
Large
Multimodal
Non

Convex
Discontinuous
Post

Processing
Visualization tools
Multi

objective, trade

off
WHY A FRAMEWORK FOR MDO?
in

house/ commercial solvers

inaccessible
–
modification
Optimisation
Multiple Disciplines
Parallel Computing
►
A software system to integrate and evaluate
different complexities of MDO is required
REQUIREMENTS FOR A MO

MDO FRAMEWORK
Robust Optimisation methods
(Global solutions,
handle n
oise, complex
functions, ease of integration of legacy
codes CFD

FEA

black

boxes
).
P
roblem formulation and execution
(Automatic movement of data, parallel
Processing heterogeneous computers).
Architectural design and information access
(GUI, object oriented, no

overhead on
optimization, e
asily extended, database

management, post

processing, visualization
capabilities, fault
–
tolerance mechanisms)
Data
Data
GUI
Aerofoil Design
MSES, XFOIL
NSC2ke
Wing Design
FLO22
CalculiX
Aircraft Design
FLOPS , ADA
Nozzle Design
HDASS
Mathematical
Test Functions
GUI
Design of Experiments
Optimisation
EA Optimiser
Gradient Based
Optimiser
Parallel Computing
MPI
PVM
Analysis Modules
RSM
Kriging
Post

Processor
Propeller Design
…
Mesh generator
MDO FRAMEWORK
Traditional Gradient Based
methods for MDO might fail
if search space is:
Advanced Optimisation Tools:
Evolutionary Optimisation
Crossover
Mutation
Fittest
Evolution
ROBUST AND EFFICIENT OPTIMISATION
TOOLS
►
Large
►
Multimodal
►
Non

Convex
►
Many Local Optimum
►
Discontinuous
►
Good for all of the above
►
Easy to paralellise
►
Robust towards noise
►
Explore larger search spaces
►
Good for multi

objective problems
EVOLUTION ALGORITHMS
What are EAs.
►
There are many evolutionary methods and
algorithms.
►
The complex task of MDO requires ….
Crossover
Mutation
Fittest
Evolution
►
B
ased 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.
►
A Robust and efficient evolutionary optimisation
method.
DRAWBACK OF EVOLUTIONARY
ALGORITHMS
►
A typical MDO problem relies on CFD and FEA for
aerodynamic and structural analysis.
►
CFD/FEA Computation are time
consuming
►
Our research addresses these issue in
some detail
►
Evolution process is time consuming/ high number of
function evaluations are required.
Hierarchical Asynchronous Parallel Evolutionary
Algorithms
(HAPEA
)
ROBUST OPTIMISATION METHODS
►
Multi

objective Parallel Evolutionary
Algorithm
►
Hierarchical Topology
►
Asynchronous Approach
Features of the Method:
►
Our Contribution…..
MULTI

OBJECTIVE OPTIMISATION (1)
A
eronautical design problems
normally
require a simultaneous optimisation of
conflicting objectives and associated number
of constraints
. They
occur when two or more
objectives that cannot be combined rationally.
For example:
►
Drag at two different values of lift.
►
Drag and thickness.
►
Pitching moment and maximum lift.
Best to let the designer choose after the optimisation
phase.
Maximise/ Minimise
Subjected to
constraints
►
Objective functions, output (e.g. cruise efficiency).
►
x:
vector of design variables, inputs (e.g. aircraft/wing geometry)
►
g(x)
equality constraints and
h(x)
inequality constraints: (e.g.
element von Mises stresses); in general these are nonlinear
functions of the design variables.
N
i
x
f
i
...
1
K
k
x
h
N
j
x
g
k
i
...
1
0
...
1
0
x
f
i
MULTI

OBJECTIVE OPTIMISATION (2)
F
2
F
1
Pareto Optimal Front
Non

Dominated
Dominated
Feasible region
Infeasible region
►
A set of solutions that are
non

dominated w.r.t all
others points in the
search space, or that
they dominate every
other solution in the
search space except
fellow members of the
Pareto optimal set.
P
ARETO
O
PTIMAL
S
ET
►
EAs work on population
based solutions …can
find a optimal Pareto set
in a single run
HIERARCHICAL TOPOLOGY

MULTIPLE MODELS
Model 1
precise model
Model 2
intermediate
model
Model 3
approximate
model
Exploration
Exploitation
►
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.
►
Asynchronous Parallel Computing
Hierarchical Topology
ASYNCHRONOUS EVALUATION
►
Suspend the idea of generation
Solution can be generated in and out of order
►
Processors
–
Can be of different speeds
–
Added at random
–
Any number of them possible
Methods of solutions to
MO and MDO

> variable
time to complete.
►
Time to solve non

linear PDE

> Depends upon geometry
Why asynchronous??
How:
Evolution Algorithm
Evaluator
PROBLEM FORMULATION AND
EXECUTION
►
The Method is applicable to integrated or distributed
MDO analysis
►
Single or multi

objective problems can be analysed
►
EAs require no derivatives of the objective function
►
The coupling of the algorithm with different analysis
codes is by simple function calls and input and output
data files.
►
Different programming languages C, C++, Fortran 90,
and Fortran 77. and CFD and FEA software: FLO22
FLOPS, ADA, XFOIL, MSES, CalculiX
ARCHITECTURAL DESIGN AND
INFORMATION ACCESS
Design Modules
Design of
Experiments
Post

processing
Parallel
Computing
Optimisation
Tools
DESIGN AND OPTIMISATION MODULES
Wing Design
Aircraft Design
RESULTS SO FAR…
Evaluations
CPU Time
Traditional
2311
224
152m
20m
New
Technique
504
490
(

78%)
48m
24m
(

68%)
►
The new technique is
approximately three times
faster than other similar
EA methods.
►
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
XFOIL
Flight Optimisation
Software
(FLOPS)
FLO22 Nsc2ke ADS (In house)
►
A testbench for single and multi

objective problems has
been developed and tested
Aircraft Conceptual Design and
Multidisciplinary Optimisation
2D Nozzle Inverse Optimisation
Transonic Wing Design
UAV Aerofoil Design
Shock Control Bump
Optimisation
CURRENT AND ONGOING OPTIMISED
INDUSTRIAL APLICATIONS
Propeller Design
High Lift Aircraft System
Transonic aerofoil optimisation using
Grid

free solvers
AF/A

18 Flutter
Model Validation
F3 Rear Wing Aerodynamics
M
1
.
1
1
3
8
9
1
.
0
6
9
3
4
1
.
0
2
4
7
8
0
.
9
8
0
2
2
7
0
.
9
3
5
6
7
1
0
.
8
9
1
1
1
5
0
.
8
4
6
5
6
0
.
8
0
2
0
0
4
0
.
7
5
7
4
4
8
0
.
7
1
2
8
9
2
0
.
6
6
8
3
3
7
0
.
6
2
3
7
8
1
0
.
5
7
9
2
2
5
0
.
5
3
4
6
6
9
0
.
4
9
0
1
1
3
0
.
4
4
5
5
5
8
0
.
4
0
1
0
0
2
0
.
3
5
6
4
4
6
0
.
3
1
1
8
9
0
.
2
6
7
3
3
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0
.
2
2
2
7
7
9
0
.
1
7
8
2
2
3
0
.
1
3
3
6
6
7
0
.
0
8
9
1
1
1
5
0
.
0
4
4
5
5
5
8
CURRENT AND ONGOING OPTIMISED
INDUSTRIAL APLICATIONS
MULTIDISCIPLINARY AND
MULTI

OBJECTIVE WING DESIGN
OPTIMISATION
Mach Number
0.69
Cruising Altitude
10000
ft
C
l
0.19
Wing Area
2.94
m
2
MOO OF TRANSONIC WING DESIGN FOR
AN UNMANNED AERIAL VEHICLE (UAV)
Objective:
Minimisation of
wave drag and wing weight
min
min
2
1
weight
w
sparcap
d
total
f
c
f
DESIGN VARIABLES
16 Design variables on
three span wise aerofoils
9 Design variables on
three span wise aerofoil
section
57 design variables
,,,
,,,,
rb bt l
rb bt r b t
ARw b
+
Description
Lower
Bound
Upper
Bound
Wing Aspect Ratio [AR]
3.50
15.00
Break to root Taper [λbr]
0.65
0.80
Break to tip Taper [λbt]
0.20
0.45
Wing 1/4 Chord inboard Sweep, deg [Λi]
10.00
25.00
Break Location, [bl]
0.30
0.45
DESIGN VARIABLES
Minimum thickness
Position of Maximum
thickness
Fitness functions
CONSTRAINTS & OBJECTIVE FUNCTIONS
/14% ,12% int,11% tip
t c root ermediate
/
20% 55%
t c
x
1
2
min( )
min
w
weight
f Cd
f totalsparcap
Approach one :
Traditional EA with single population model
Computational Grid 96 x 12 x 16
Approach two :
HAPEA
Exploitation
Population size = 30
Exploration
Population size = 30
Intermediate
Population size =
30
Grid size
96 x 12 x 16
Grid size
72 x 9 x 12
Grid size
48 x 6 x 8
Six machines were used in all calculations
IMPLEMENTATION
The algorithm was run five times for 2000 function evaluations and took
about six hours to compute
PARETO FRONTS AFTER 2000
FUNCTION EVALUATIONS
MULTIDISCIPLINARY WING DESIGN
Best for Objective One
Best for Objective Two
Pareto Solutions
RESULTS
Aerofoil Geometries at 0, 20 and 100% semispan
UAV
DESIGN AND OPTIMISATION
Minimise two objectives:
Operational Fuel Weight
†
m楮⡏䙗(
Endurance
†
浩渠⠱⽅⤠
卵扪散e瑯
T
慫敯a映汥湧
栠㰠〰
ft
Alt Cruise > 40000
ft
Endurance >
24 hrs
With respect to:
External geometry of the aircraft
•
Mach
= 0.3
•
Endurance
> 24 hrs
•
Cruise Altitude:
40000 ft
DESIGN VARIABLES
In total we have 29 design variables
Design Variable
Lower
Bound
Upper
Bound
Wing Area (sq ft)
280
330
Aspect Ratio
18
25.2
Wing Sweep (deg)
0.0
8.0
Wing Taper Ratio
0.28
0.8
13 Configuration Design variables
Aerofoil

Wing Geometry
Wing
16 Design variables for the
aerofoil
+
DESIGN VARIABLES
Twist
Horizontal Tail Area
(sq ft)
65.0
85.0
HT Aspect Ratio
3.0
15.0
HT Taper Ratio
0.2
0.55
HT Sweep (deg)
12.0
15.0
Vertical Tail Area
(sq ft)
11.0
29.0
VT Aspect Ratio
1.0
3.2
VT Taper Ratio
0.28
0.62
VT Sweep (deg)
12.0
34.0
Fuselage Diameter
2.6
5.0
Tail
Fuselage
MISSION PROFILE
Structural &
weight analysis
A compromise on fidelity models
Vortex induced drag: VLMpc
Viscous drag: friction.f
Aerofoil Design Xfoil
Evolutionary Algorithms
(HAPEA)
Optimisation
Aircraft design
and analysis
Aerodynamic
Analysis
Analytically by FLOPS
Flight Optimsation System
(FLOPS)
–
NASA CODE
DESIGN TOOLS
IMPLEMENTATION
Population size: 20
Population size: 20
Population size: 20
Grid 141 x 74 x 36 on aerofoil, 20
x 6 on Vortex model
Grid 109 x 57 x 27 on aerofoil, 17
x 6 on Vortex model
Grid 99 x 52 x 25 on aerofoil, 15
x 6 on Vortex model
►
Aircraft Design and Optimisation Module
►
Hierarchical Topology
PARETO OPTIMAL REGION
Objective 1 optimal
Objective 2 optimal
Compromise
PARETO OPTIMAL CONFIGURATIONS
CAD

Model and Flight Simulation
OUTCOMES (1)
The new technique facilitates the process of conceptual
and preliminary MDO studies
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.
Some Multidisciplinary Design Optimisation (MDO)
problems have been solved.
OUTCOMES (2)
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.
No problem specific knowledge is required
The method appears to be broadly applicable to
different analysis codes.
Work to be done on approximate techniques and
use of higher fidelity models.
Acknowledgements
Mourad Sefrioui, Dassault Aviation for fruitful
discussions on Hierarchical EAs and his contribution to
the optimization procedure.
Steve Armfield and Patrick Morgan at the University of
Sydney for providing the cluster of computing facilities.
We would like to thank Arnie McCullers at NASA LaRC
who kindly provided the FLOPS software.
Questions…
Thank you for your attention
Additional Slides
Acknowledgements
Multidisciplinary design problems
involve search space that are
multi

modal, non

convex or
discontinuous.
Traditional methods use
deterministic approach and rely
heavily on the use of iterative
trade

off studies between
conflicting requirements.
Problems in MDO (1)
Traditional optimisation methods
will fail to find the real answer in
most real engineering applications,
(Noise, complex functions).
The internal workings of validated
in

house/ commercial solvers are
essentially inaccessible from a
modification
point of view (they are
black

boxes).
Problems in MDO
The process of MDO is complex and involves several
considerations as robust optimisation tools, problem formulations,
parallel computing visualization tools.
A software system or “framework” is desired”
Parallelization Module
Classification of our Model:
The algorithm can be classified as a hierarchical Hybrid pMOEA model
[CantuPaz] uses a Master slave PMOEA but incorporate the concept of
isolation and migration trough hierarchical topology binary tree structure
where each level executes different MOEAs/parameters (heterogeneous)
The distribution of objective function evaluations over the salve
processors is where each slake performs k objective function evaluations.
Parallel Processing system characteristics:
We use a Cluster of maximum 18 PCs with Heterogeneous CPUs, RAMs ,
caches, memory access times , storage capabilities and communication
attributes.
Inter

processor communication:
Using the Parallel Virtual Machine (PVM)
EAs
•
The selection operator is a
novel approach to
determine whether an
individual x is to be
accepted into the main
population
•
Create a tournament Q
Where
B
is the selection buffer.
Population
Tournament Q
Asynchronous Buffer
Evaluate x
If x not dominated
x
Pareto Tournament Selection
B
n
B
B
q
q
q
Q
n
2
1
6
1
;
....
,
2
1
Evolutionary Algorithms
Explore large search spaces
.
Robust towards noise and local minima
Easy to parallelise
Map multiple populations of points,
allowing solution diversity.
A number of multi

objective solutions
in a Pareto set
or
performing a robust Nash game.
UAV design
Pareto Optimal configurations
The Challenge
The use of higher fidelity models is still prohibitive,
research on surrogate modeling/approximation
techniques is required.
MDO is a challenging topic, the last few year have
seen several approaches for Design and optimization
using Evolutionary techniques but research indicate
that it is problem dependent and it is still an open
problem.
Access to
Dell Linux Cluster is limited for
benchmarking purposes. Use of higher fidelity
models is still prohibitive.
Work in Progress
•
Master of Engineering
Rotor Blade design and Optimisation using
evolutionary Techniques
Adaptive Transonic Wing/Aerofoil Design and MDO
using Evolutionary Techniques
Grid

less Algorithms for Design and optimisation in
Aeronautics
•
Undergraduate Projects
Transonic wing design using DACE (Design of
Experiments

approximation Theories)
An empirical study on DSMC for within evolutionary
Optimisation
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