Transcript Document

Multidisciplinary Design Optimisation of Unmanned
Aerial Vehicles (UAV) using Multi-Criteria
Evolutionary Algorithms
L. F. González,
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
Eleventh Australian International Aerospace Congress 13-17 March , Melbourne Convention Centre and
Australian International Airshow 2005 at Avalon Airport Design November 15-19, 2004
OUTLINE

Introduction
 Unmanned Aerial Vehicle (UAV/UCAV) Design
Requirements
OUTLINE

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
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.
Complex –tradeoffs
High
Performance
Multi-missions
high–medium-altitude surveillance
MDO Complex Task Multiple Goals
UAV -Example
Minimise-Maximise
Multiple Disciplines
Aerodynamics
Pareto optimal
Surface of UAV,
μUAV
Structures
Aerodynamic
Performance
Fight Controls
Aero elasticity
Optimization-Optimal Solution(S)
Takeoff weight
Purchase Price
Sensors
Propulsion
Aero acoustics
WHY A FRAMEWORK FOR MDO?
►A
software system to integrate and evaluate
different complexities of MDO is required
Multiple Disciplines
Optimisation
Multi-objective, tradeoff
Search Space – Large
Multimodal
Non-Convex
Discontinuous
Post-Processing
Visualization tools
in-house/ commercial solversinaccessible –modification
Parallel Computing
REQUIREMENTS FOR A MO-MDO FRAMEWORK
Data

Robust Optimisation methods
(Global solutions, handle noise, complex
functions, ease of integration of legacy
codes CFD-FEA- black-boxes).

Problem formulation and execution
(Automatic movement of data, parallel
Processing heterogeneous computers).

Architectural design and information access
(GUI, object oriented, no-overhead on
optimization, easily extended, databasemanagement, post-processing, visualization
capabilities, fault –tolerance mechanisms)
Data
GUI
MDO FRAMEWORK
GUI
Optimisation
EA Optimiser
Gradient Based
Optimiser
Mesh generator
Parallel Computing
MPI
PVM
Post-Processor
Analysis Modules
Aerofoil Design
Wing Design
MSES, XFOIL
NSC2ke
FLO22
CalculiX
Nozzle Design
Aircraft Design
HDASS
FLOPS , ADA
Propeller Design
Mathematical
…
Test Functions
Design of Experiments
RSM
Kriging
ROBUST AND EFFICIENT OPTIMISATION
TOOLS
Traditional Gradient Based
methods for MDO might fail
if search space is:
►
►
►
►
►
Large
Multimodal
Non-Convex
Many Local Optimum
Discontinuous
Advanced Optimisation Tools:
Evolutionary Optimisation
►
►
►
►
►
Good for all of the above
Easy to paralellise
Robust towards noise
Explore larger search spaces
Good for multi-objective problems
Evolution
Crossover
Mutation
Fittest
EVOLUTION ALGORITHMS
What are EAs.
►
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
►
There are many evolutionary methods and
algorithms.
►
The complex task of MDO requires ….
► A Robust
method.
Mutation
Fittest
and efficient evolutionary optimisation
DRAWBACK OF EVOLUTIONARY
ALGORITHMS
►
Evolution process is time consuming/ high number of
function evaluations are required.
►
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
ROBUST OPTIMISATION METHODS
►
Our Contribution…..
Hierarchical Asynchronous Parallel Evolutionary
Algorithms (HAPEA)
Features of the Method:
►
Multi-objective Parallel Evolutionary
Algorithm
►
Hierarchical Topology
►
Asynchronous Approach
MULTI-OBJECTIVE OPTIMISATION (1)

Aeronautical 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.
MULTI-OBJECTIVE OPTIMISATION (2)
Maximise/ Minimise
Subjected to
constraints
f i x 
gi x   0
i  1...N
j  1...N
hk x   0 k  1...K
►
f i x 
►
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.
Objective functions, output (e.g. cruise efficiency).
PARETO OPTIMAL SET
Infeasible region
►
A set of solutions that areF
2
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.
►
EAs work on population
based solutions …can
find a optimal Pareto set
in a single run
Feasible region
Pareto Optimal Front
Non-Dominated
Dominated
F1
HIERARCHICAL TOPOLOGYMULTIPLE MODELS
Exploitation
Model 1
precise model
Model 2
intermediate
model
Model 3
approximate
model
Exploration
Hierarchical Topology
►
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
ASYNCHRONOUS EVALUATION
Why asynchronous??
Evolution Algorithm

►
Evaluator
Methods of solutions to
MO and MDO -> variable
time to complete.
Time to solve non-linear PDE - > Depends upon geometry
How:
►
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
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
Aircraft
Design
Design
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 multi-objective 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 XFOIL Flight Optimisation
Software
(FLOPS)
FLO22
Nsc2ke
ADS (In house)
CURRENT AND ONGOING OPTIMISED
INDUSTRIAL APLICATIONS
Shock Control Bump Optimisation
2D Nozzle Inverse Optimisation
Transonic Wing Design
Aircraft Conceptual Design and
Multidisciplinary Optimisation
UAV Aerofoil Design
CURRENT AND ONGOING OPTIMISED
INDUSTRIAL APLICATIONS
F3 Rear Wing Aerodynamics
High Lift Aircraft System
Transonic aerofoil optimisation using
Grid-free solvers
Propeller Design
AF/A-18 Flutter
Model Validation

MULTIDISCIPLINARY AND
MULTI-OBJECTIVE WING DESIGN
OPTIMISATION
MOO OF TRANSONIC WING DESIGN FOR
AN UNMANNED AERIAL VEHICLE (UAV)
Objective: Minimisation of
wave drag and wing weight
Mach Number
0.69
Cruising Altitude
10000 ft
Cl
0.19
Wing Area
2.94 m2
 
f1  min cd w

f 2  min total sparcap weight

DESIGN VARIABLES
16 Design variables on
three span wise aerofoils
+
9 Design variables on
three span wise aerofoil
section
ARw, rb , bt , bl
 rb ,  bt ,  r , b , t
57 design variables
DESIGN VARIABLES
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
CONSTRAINTS & OBJECTIVE FUNCTIONS
Minimum thickness
t / c  14% root,12% int ermediate,11% tip
Position of Maximum
thickness
Fitness functions
 20%  xt / c  55%
f1  min(Cd w )
f 2  min  totalsparcapweight 
IMPLEMENTATION
Approach one : Traditional EA with single population model
Computational Grid 96 x 12 x 16
Approach two : HAPEA
Exploitation
Population size = 30
Grid size
96 x 12 x 16
Intermediate
Population size =
30
Grid size
72 x 9 x 12
Exploration
Population size = 30
Grid size
48 x 6 x 8
Six machines were used in all calculations
PARETO FRONTS AFTER 2000
FUNCTION EVALUATIONS
The algorithm was run five times for 2000 function evaluations and took
about six hours to compute
MULTIDISCIPLINARY WING DESIGN
Pareto Solutions
Best for Objective One
Best for Objective Two
RESULTS
Aerofoil Geometries at 0, 20 and 100% semispan
UAV DESIGN AND OPTIMISATION
Minimise two objectives:
•
•
•
Mach = 0.3
Endurance > 24 hrs
Cruise Altitude: 40000 ft
Operational Fuel Weight  min(OFW)
Endurance
 min (1/E)
Subject to:
Takeoff length < 1000 ft
Alt Cruise > 40000 ft
Endurance > 24 hrs
With respect to:
External geometry of the aircraft
DESIGN VARIABLES
In total we have 29 design variables
Aerofoil-Wing Geometry
16 Design variables for the
aerofoil
+
Wing
13 Configuration 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
DESIGN VARIABLES
Tail
Twist
Fuselage
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
MISSION PROFILE
DESIGN TOOLS
Optimisation
Aircraft design
and analysis
Aerodynamic
Analysis
Structural &
weight analysis
Evolutionary Algorithms
(HAPEA)
Flight Optimsation System
(FLOPS) – NASA CODE
A compromise on fidelity models
Vortex induced drag: VLMpc
Viscous drag: friction.f
Aerofoil Design Xfoil
Analytically by FLOPS
IMPLEMENTATION
►
Aircraft Design and Optimisation Module
►
Hierarchical Topology
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
PARETO OPTIMAL REGION
Objective 1 optimal
Compromise
Objective 2 optimal
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
Problems in MDO (1)
 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
 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).
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
Pareto Tournament Selection
• The selection operator is a
novel approach to
determine whether an
individual x is to be
accepted into the main
population
Population
Asynchronous Buffer
• Create a tournament Q
Tournament Q
Evaluate x
x
If x not dominated
1
1
Q  q1 , q2 ....qn   B; B  n  B
6
2
Where B is the selection buffer.
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