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Title: Multidisciplinary Design Optimisation of Unmanned Aerial Vehicles (UAV) using Multi-Criteria Evolutionary Algorithms


1
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
2
OUTLINE
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

3
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 trade-offs
High Performance
Multi-missions highmedium--altitude surveillance
4
MDO Complex Task -
UAV -Example
Multiple Goals
Minimise-Maximise
Multiple Disciplines
Pareto optimal Surface of UAV, µUAV
Optimization-Optimal Solution(S)
5
WHY A FRAMEWORK FOR MDO?
  • A software system to integrate and evaluate
    different complexities of MDO is required

Optimisation
Multiple Disciplines
Search Space Large
Multimodal Non-Convex
Discontinuous
Multi-objective, trade-off
in-house/ commercial solvers-inaccessible
modification
Post-Processing Visualization tools
Parallel Computing
6
REQUIREMENTS FOR A MO-MDO FRAMEWORK
  • 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, database-management
    , post-processing, visualization capabilities,
    fault tolerance mechanisms)

Data
Data
GUI
7
MDO FRAMEWORK
Analysis Modules
GUI
Aerofoil Design MSES, XFOIL NSC2ke
Wing Design FLO22 CalculiX
Optimisation
Gradient Based Optimiser
EA Optimiser
Aircraft Design FLOPS , ADA
Nozzle Design HDASS
Mesh generator
Propeller Design
Mathematical Test Functions
Parallel Computing
MPI
PVM
Design of Experiments
Post-Processor
RSM
Kriging
8
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

9
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
Mutation
Fittest
  • There are many evolutionary methods and
    algorithms.
  • The complex task of MDO requires .
  • A Robust and efficient evolutionary optimisation
    method.

10
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

11
  • ROBUST OPTIMISATION METHODS
  • Our Contribution..

Hierarchical Asynchronous Parallel Evolutionary
Algorithms (HAPEA)
Features of the Method
  • Multi-objective Parallel Evolutionary Algorithm
  • Hierarchical Topology
  • Asynchronous Approach

12
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.

13
MULTI-OBJECTIVE OPTIMISATION (2)
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.

14
PARETO OPTIMAL SET
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.

F2
Feasible region
  • EAs work on population based solutions can find
    a optimal Pareto set in a single run

F1
Pareto Optimal Front
Non-Dominated
Dominated
15
HIERARCHICAL TOPOLOGY-MULTIPLE MODELS
Model 1 precise model
Exploitation
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


16
ASYNCHRONOUS EVALUATION
Why asynchronous??
  • Methods of solutions to MO and MDO -gt variable
    time to complete.
  • Time to solve non-linear PDE - gt 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

17
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

18
ARCHITECTURAL DESIGN AND INFORMATION ACCESS
  • Design Modules
  • Design of Experiments
  • Post-processing
  • Parallel Computing
  • Optimisation Tools

19
DESIGN AND OPTIMISATION MODULES
Wing Design
Aircraft Design
20
RESULTS SO FAR
  • The new technique is approximately three times
    faster than other similar EA methods.
  • 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)

21
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
22
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
23
  • MULTIDISCIPLINARY AND
  • MULTI-OBJECTIVE WING DESIGN
  • OPTIMISATION

24
MOO OF TRANSONIC WING DESIGN FORAN UNMANNED
AERIAL VEHICLE (UAV)
Objective Minimisation of wave drag and wing
weight
25
DESIGN VARIABLES
16 Design variables on three span wise aerofoils

9 Design variables on three span wise aerofoil
section
57 design variables
26
DESIGN VARIABLES
27
CONSTRAINTS OBJECTIVE FUNCTIONS
Minimum thickness
Position of Maximum thickness
Fitness functions
28
IMPLEMENTATION
Approach one Traditional EA with single
population model Computational Grid 96 x 12
x 16 Approach two HAPEA
Six machines were used in all calculations
29
PARETO FRONTS AFTER 2000 FUNCTION EVALUATIONS
The algorithm was run five times for 2000
function evaluations and took about six hours to
compute
30
MULTIDISCIPLINARY WING DESIGN
Pareto Solutions
31
RESULTS
Aerofoil Geometries at 0, 20 and 100 semispan
32
UAV DESIGN AND OPTIMISATION
  • Minimise two objectives
  • Operational Fuel Weight ? min(OFW)
  • Endurance ? min (1/E)
  • Subject to
  • Takeoff length lt 1000 ft
  • Alt Cruise gt 40000 ft
  • Endurance gt 24 hrs
  • With respect to
  • External geometry of the aircraft
  • Mach 0.3
  • Endurance gt 24 hrs
  • Cruise Altitude 40000 ft

33
DESIGN VARIABLES
In total we have 29 design variables
Aerofoil-Wing Geometry
16 Design variables for the aerofoil

13 Configuration Design variables
Wing
34
DESIGN VARIABLES

Tail
Twist
Fuselage
35
MISSION PROFILE
36
DESIGN TOOLS
Evolutionary Algorithms (HAPEA)
Optimisation
Aircraft design and analysis
Flight Optimsation System (FLOPS) NASA CODE
A compromise on fidelity models Vortex induced
drag VLMpc Viscous drag friction.f Aerofoil
Design Xfoil
Aerodynamic Analysis
Structural weight analysis
Analytically by FLOPS
37
IMPLEMENTATION
  • Aircraft Design and Optimisation Module
  • Hierarchical Topology

38
PARETO OPTIMAL REGION
Objective 1 optimal
Compromise
Objective 2 optimal
39
PARETO OPTIMAL CONFIGURATIONS
CAD-Model and Flight Simulation
40
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.

41
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.

42
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.

43
Questions
Thank you for your attention
44
Additional Slides
45
Acknowledgements
46
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.

47
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

48
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)

49
EAs
50
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
Where B is the selection buffer.
If x not dominated
51
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.
52
UAV design
53
Pareto Optimal configurations
54
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.

55
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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