1. E. J. Whitney, K. Srinivas, K.C Wong
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 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
high–medium--altitude surveillance

4. Pareto optimal Surface of UAV, μUAV
MDO Complex Task -
UAV -Example
Multiple Goals
Minimise-Maximise
Multiple Disciplines
Aerodynamics
Structures
Pareto optimal Surface of UAV, μUAV
Aerodynamic Performance
Fight Controls
Aero elasticity
Optimization-Optimal Solution(S)
Purchase Price
Takeoff weight
Aero acoustics
Sensors
Propulsion

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. Gradient Based Optimiser
MDO FRAMEWORK
Analysis Modules
GUI
Aerofoil Design
MSES, XFOIL NSC2ke
Wing Design
FLO22
CalculiX
Optimisation
Gradient Based Optimiser
EA Optimiser
Nozzle Design
HDASS
Aircraft Design
FLOPS , ADA
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
Crossover
Mutation
Fittest
Evolution
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. Hierarchical Asynchronous Parallel Evolutionary Algorithms (HAPEA)
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
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

16. ASYNCHRONOUS EVALUATION
Evolution Algorithm
Evaluator
Why asynchronous??
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

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…
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.
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)
FLO 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 FOR AN UNMANNED AERIAL VEHICLE (UAV)
Mach Number
0.69
Cruising Altitude
10000 ft Cl
0.19
Wing Area
2.94 m2
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 Description Lower Bound Upper 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

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
Exploitation
Population size = 30
Exploration
Population size = 30
Intermediate
Grid size
96 x 12 x 16
72 x 9 x 12
48 x 6 x 8
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
Line Thickness

30. Pareto Solutions MULTIDISCIPLINARY WING DESIGN Best for Objective One
Best for Objective Two

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 < 1000 ft
Alt Cruise > ft
Endurance > 24 hrs
With respect to:
External geometry of the aircraft
Mach = 0.3
Endurance > 24 hrs
Cruise Altitude: ft

33. 16 Design variables for the aerofoil
In total we have 29 design variables
Aerofoil-Wing Geometry
16 Design variables for the aerofoil +
13 Configuration Design variables
Design Variable
Lower
Bound
Upper
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
Wing

34. DESIGN VARIABLES Tail 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
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)
34.0
Fuselage Diameter
2.6
5.0
Tail
Twist
Fuselage

35. MISSION PROFILE
Line Thickness, colours

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
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
Population size: 20

38. PARETO OPTIMAL REGION Objective 1 optimal Compromise
Line Thickness

39. CAD-Model and Flight Simulation
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
Line Thickness

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
If x not dominated
Where B is the selection buffer.

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