Aerial Vehicles (UAV) using Multi-Criteria

mustardarchaeologistMécanique

22 févr. 2014 (il y a 3 années et 7 mois)

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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
5
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