1

ADVENT

Aim: To Develop advanced numerical tools and apply them to optimisation problems in aeronautics.

ADVanced EvolutioN Team

2

Outline

Why are we interested in evolutionary optimisation?

How does it work?

What have we solved so far?

We are we going now?

3

Why Evolution?

Traditional optimisation methods will fail to find the real answer in most real engineering applications.

Techniques such as Evolution Algorithms can explore large variations in designs. They also handle errors and deceptive sub-optimal solutions with aplomb.

They are extremely easy to parallelise. They can provide optimal solutions for single and

multi-objective problems.

4

Some Applications to Date…

Evolution is being applied in thousands of fields right now. Some examples in aviation are:Whole wing design for drag reduction.Radar cross-section minimisation.Whole turbofan layout and blade design.Formation flight optimisation for maximum

engagement success.Autopilot design and trajectory optimisation.

As well as combinations of the above.

5

What Are Evolution Algorithms?

Crossover Mutation

Fittest

Evolution

Computers perform this evolution process as a mathematical simplification.

EAs move populations of solutions, rather than ‘cut-and-try’ one to another.

EAs applied to sciences, arts and engineering.

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.

6

Hierarchical Topology-Multiple Models

Model 1precise model

Model 2intermediate

model

Model 3approximate

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

Evolution Algorithm Evaluator

7

Results So Far…

Evaluations

CPU Time

Traditional

2311 224 152m20m

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 multiobjective problems has been developed and tested

8

Results So Far… (2)

Constrained aerofoil design for transonic transport aircraft 3% Drag reduction

UAV aerofoil design

-Drag minimisation for high-speed transit and loiter conditions.

-Drag minimisation for high-speed transit and takeoff conditions.

Exhaust nozzle design for minimum losses.

9

Results So Far… (3)

Three element aerofoil reconstruction from surface pressure data.

UCAV MDO Whole aircraft multidisciplinary design.Gross weight minimisation and cruise efficiency Maximisation. Coupling with NASA code FLOPS 2 % improvement in Takeoff GW and Cruise EfficiencyAF/A-18 Flutter model validation.

10

An Example: Aerofoil Optimisation

Property Flt. Cond. 1

Flt Cond.2

Mach 0.75 0.75

Reynolds 9 x 106 9 x 106

Lift 0.65 0.715

Constraints:• Thickness > 12.1% x/c (RAE 2822)• Max thickness position = 20% ® 55%

To solve this and other problems standard industrial flow solvers are being used.

Aerofoil cd

[cl = 0.65 ]

cd

[cl = 0.715 ]

Traditional Aerofoil RAE2822

0.0147 0.0185

Conventional Optimiser [Nadarajah [1]]

0.0098 (-33.3%)

0.0130 (-29.7%)

New Technique 0.0094 (-36.1%)

0.0108 (-41.6%)

For a typical 400,000 lb airliner, flying 1,400 hrs/year:

3% drag reduction corresponds to 580,000 lbs (330,000 L) less fuel burned.

[1] Nadarajah, S.; Jameson, A, " Studies of the Continuous and Discrete Adjoint Approaches to Viscous Automatic Aerodynamic Shape Optimisation," AIAA 15th Computational Fluid Dynamics Conference, AIAA-2001-2530, Anaheim, CA, June 2001.

11

Aerofoil Characteristics cl = 0.715 Aerofoil Characteristics cl = 0.65

Delayed drag divergence at high Cl

Aerofoil Characteristics

M = 0.75

Aerofoil Optimisation (2)

Delayed drag divergence at low Cl

Delayed drag rise for increasing lift.

12

Second Example: UCAV Multidisciplinary Design Optimisation - Two Objective Problem

Cruise 40000 ft, Mach 0.9, 400 nm

Landing

Release Payload 1800 Lbs

Maneuvers at Mach 0.9

Accelerate Mach 1.5, 500 nm

20000 ft

Engine Start and warm up

Taxi

Takeoff

Climb

Descend

Release Payload 1500 Lbs

Cruise efficiency maximisation and gross weight minimisation

13

UCAV MDO Design (2)

Best for Obj 1

Best for Obj 2

Compromised solution

Nash Equilibrium

14

UCAV MDO-MO (2) Comparison

Variables Pareto Member

0

Pareto Member 3

Pareto Member 7

Nash Equilibrium

Aspect Ratio 4.76 5.23 5.27 5.13

Wing Area (sq ft) 629.7 743.8 919 618

Wing Thickness (t/c)

0.046 0.050 0.041 0.021

Wing Taper Ratio 0.15 0.16 0.17 0.17

Wing Sweep (deg) 28 25 27 28

Engine Thrust (lbf) 32065 32219 32259 33356

Gross Weight (Lbs)

57540 59179 64606 62463

Increasing Cruise Efficiency

Decreasing Gross Weight

MCRUISE.L/DCRUISE22.5 25.1 27.5 23.9

Nash Point

15

Outcomes

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.

Multi-disciplinary Design Optimisation (MDO) problems have been solved.

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.

16

What Are We Doing Now?

A Hybrid EA - Deterministic optimiser.

EA + MDO : Evolutionary Algorithms Architecture for Multidisciplinary Design Optimisation

We intend to couple the aerodynamic optimisation with:

o Aerodynamics – Whole wing design using Euler codes.o Electromagnetics - Investigating the tradeoff between efficient

aerodynamic design and RCS issues.o Structures - Especially in three dimensions means we can

investigate interesting tradeoffs that may provide weight improvements.

o And others…

Wing MDO using Potential flow and

structural FEA.

17

THE END

Questions?