1. 1 ADVENT Aim: To Develop advanced numerical tools and apply them to optimisation problems in aeronautics. ADVanced EvolutioN Team

2. 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. 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. 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. 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. 6 Hierarchical Topology-Multiple Models Model 1 precise model Model 2 intermediate model Model 3 approximate 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. 7 Results So Far… EvaluationsCPU Time Traditional 2311  224152m  20m 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. 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. 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 Efficiency  AF/A-18 Flutter model validation.

10. 10 An Example: Aerofoil Optimisation PropertyFlt. Cond. 1Flt Cond.2 Mach0.75 Reynolds9 x 10 6 Lift0.650.715 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. Aerofoilc d [c l = 0.65 ] c d [c l = 0.715 ] Traditional Aerofoil RAE2822 0.01470.0185 Conventional Optimiser [Nadarajah [1]] 0.0098 (-33.3%) 0.0130 (-29.7%) New Technique0.0094 (-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. 11 Aerofoil Characteristics c l = 0.715 Aerofoil Characteristics c l = 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. 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 TaxiTakeoff Climb Descend Release Payload 1500 Lbs Cruise efficiency maximisation and gross weight minimisation

13. 13 UCAV MDO Design (2) Best for Obj 1 Best for Obj 2 Compromised solution Nash Equilibrium

14. 14 UCAV MDO-MO (2) Comparison VariablesPareto Member 0 Pareto Member 3 Pareto Member 7 Nash Equilibrium Aspect Ratio4.765.235.275.13 Wing Area (sq ft)629.7743.8919618 Wing Thickness (t/c)0.0460.0500.0410.021 Wing Taper Ratio0.150.160.17 Wing Sweep (deg)28252728 Engine Thrust (lbf)32065322193225933356 Gross Weight (Lbs) 57540 59179 6460662463 Increasing Cruise Efficiency Decreasing Gross Weight M CRUISE.L/D CRUISE 22.5 25.1 27.523.9 Nash Point

15. 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. 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: oAerodynamics – Whole wing design using Euler codes. oElectromagnetics - Investigating the tradeoff between efficient aerodynamic design and RCS issues. oStructures - Especially in three dimensions means we can investigate interesting tradeoffs that may provide weight improvements. oAnd others… Wing MDO using Potential flow and structural FEA.

17. 17 THE END Questions?