Title: Multidisciplinary Design Optimisation of Unmanned Aerial Vehicles (UAV) using Multi-Criteria Evolutionary Algorithms
1Multidisciplinary 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
2OUTLINE
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
3UAVDESIGN 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
4MDO Complex Task -
UAV -Example
Multiple Goals
Minimise-Maximise
Multiple Disciplines
Pareto optimal Surface of UAV, µUAV
Optimization-Optimal Solution(S)
5WHY 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
6REQUIREMENTS 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
7MDO 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
8ROBUST 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
9EVOLUTION 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.
10DRAWBACK 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
Hierarchical Asynchronous Parallel Evolutionary
Algorithms (HAPEA)
Features of the Method
- Multi-objective Parallel Evolutionary Algorithm
- Hierarchical Topology
- Asynchronous Approach
12MULTI-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.
- Pitching moment and maximum lift.
- Best to let the designer choose after the
optimisation phase.
13MULTI-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.
14PARETO 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
15HIERARCHICAL 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
16ASYNCHRONOUS 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
17PROBLEM 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
18ARCHITECTURAL DESIGN AND INFORMATION ACCESS
- Design Modules
- Design of Experiments
- Post-processing
- Parallel Computing
- Optimisation Tools
19DESIGN AND OPTIMISATION MODULES
Wing Design
Aircraft Design
20RESULTS 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)
21CURRENT 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
22CURRENT 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
24MOO OF TRANSONIC WING DESIGN FORAN UNMANNED
AERIAL VEHICLE (UAV)
Objective Minimisation of wave drag and wing
weight
25DESIGN VARIABLES
16 Design variables on three span wise aerofoils
9 Design variables on three span wise aerofoil
section
57 design variables
26DESIGN VARIABLES
27CONSTRAINTS OBJECTIVE FUNCTIONS
Minimum thickness
Position of Maximum thickness
Fitness functions
28IMPLEMENTATION
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
29PARETO 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
31RESULTS
Aerofoil Geometries at 0, 20 and 100 semispan
32UAV 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
33DESIGN VARIABLES
In total we have 29 design variables
Aerofoil-Wing Geometry
16 Design variables for the aerofoil
13 Configuration Design variables
Wing
34DESIGN VARIABLES
Tail
Twist
Fuselage
35MISSION PROFILE
36DESIGN 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
37IMPLEMENTATION
- Aircraft Design and Optimisation Module
38PARETO OPTIMAL REGION
Objective 1 optimal
Compromise
Objective 2 optimal
39PARETO OPTIMAL CONFIGURATIONS
CAD-Model and Flight Simulation
40OUTCOMES (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.
41OUTCOMES (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.
42Acknowledgements
- 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.
43Questions
Thank you for your attention
44Additional Slides
45Acknowledgements
46Problems 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.
47Problems 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
48Parallelization 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)
49EAs
50Pareto 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
Tournament Q
Evaluate x
x
Where B is the selection buffer.
If x not dominated
51Evolutionary 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.
52UAV design
53Pareto Optimal configurations
54The 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.
55Work 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