1. MULTIDISCIPLINARY AIRCRAFT DESIGN AND OPTIMISATION USING A ROBUST EVOLUTIONARY TECHNIQUE WITH VARIABLE FIDELITY MODELS The University of Sydney L. F. Gonzalez E. J. Whitney K. Srinivas Pole Scientifique J. Périaux 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, New York,USA, 30 Aug - 1 Sep 2004,

2. Outline Introduction – Problems in aeronautical design and optimisation – The need for Evolutionary algorithms Theory – Evolution Algorithms (EAs). – Multidisciplinary –Multi-objective Design – Hierarchical Asynchronous Evolutionary Algorithm (HAPEA). Application -Mathematical Test functions -Two real world examples Conclusions

3. Multidisciplinary aircraft design and optimisation Aircraft Deign is multidisciplinary in nature and there is a strong interaction between the different multi-physics involved (aerodynamics, structures, propulsion) A tradeoff between aerodynamic performance and other objectives becomes necessary.

4. 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. Problems in Aerodynamic Optimisation (1)

5. Traditional optimisation methods will fail to find the real answer in many real engineering applications, (Noise, complex functions). Problems in Aerodynamic Optimisation (2) Question- High Fidelity model? Or a thorough search with a Low Fidelity Model? Commercial solvers are essentially inaccessible from a modification point of view (they are black- boxes).

6. Why Evolution? Evolution Algorithms can explore large variations in designs. Robust towards noise and local minima and easy to to parallelise, reducing computation time. Provide optimal solutions for single and multi-objective problems or calculating a robust Nash game EAs successively map multiple populations of points, allowing solution diversity.

7. Evolution Algorithms What are EAs. Computers can be adapted to perform this evolution process. Crossover Mutation Fittest Evolution 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.

8. Introduction to 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.

9. Maximise/ Minimise Subjected to constraints objective functions, output (e.g. cruise efficiency). x: vector of design variables, inputs (e.g. aircraft geometry ) with upper and lower bounds; 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. Introduction to Multi-Objective Optimisation (2)

10. 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. Pareto Optimal Set

11. Our Approach: Parallel Computing and Asynchronous Evaluation Pareto Tournament Selection Hierarchical Population Topology Hierarchical Asynchronous Parallel Evolutionary Algorithms (HAPEA)

12. Evolution Algorithm Asynchronous Evaluator 1 individual Different Speeds Parallel Computing and Asynchronous Evaluation

13. Asynchronous Evaluation 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 Methods of solutions to MO and MDO -> variable time to complete. Time to solve non-linear PDE - > Depends upon geometry Why asynchronous How:

14. Asynchronous Evaluation Methods of solutions to MO and MDO -> variable time to complete. Time TO SOLVE NON-linear pfF - > DEPENDS UPON GEOMETRY Traditional EAs -> create an unnecessary bottleneck when used on parallel computers; -> i.e processors that have already completed their solutions will remain idle until all processors have completed their work.

15. The selection operator is a novel approach to determine whether an individual x is to be accepted into the main population Create a tournament Where B is the selection buffer. Population Tournament Q Asynchronous Buffer Evaluate x If x not dominated x Pareto Tournament Selection

16. Hierarchical Population Topology Model 1 precise model Model 2 intermediate model Model 3 approximate model Exploration (large mutation span) Exploitation (small mutation span)

17. Ackley MOEAs Examples Optimisation of Analytical Test Functions

18. Test Functions: Ackley Increasing number of variables

19. Here our EA solves a two objective problem with two design variables. There are two possible Pareto optimal fronts; one obvious and concave, the other deceptive and convex MOEA Examples

20. Again, we solve a two objective problem with two design variables however now the optimal Pareto front contains four discontinuous regions

21. 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

22. Applications So Far… (1) 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.

23. Applications So Far… (2) 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.

24. Applications So Far… (3) Transonic wing design Two Objectives UAV Wing Design Wind Tunnel Test : Evolved Aerofoils Evolved Wings Evolved Aircrafts (in progress)

25. Two Representative Examples Three Element Aerofoil Euler Reconstruction. Multidisciplinary UAV Design Optimisation

26. Three Element Aerofoil Euler Reconstruction. Problem Definition: Rebuild from scratch the pressure distributions that approximately fit the target pressure distributions of a three element aerofoil set. Flow Conditions -Mach 0.2, - Angle of Attack 17 deg - Euler Flow, unstructured mesh

27. Design variables Fitness Function The fitness function is the RMS error of the surface pressure coefficients on all the three elements Multi-element aerofoil reconstruction problem The design variables are the position And rotation of the slat and flap Upper and lower bounds of position and rotation are and respectively

28. Implementation Population size: 40 Grid nbv 2500 Single Population EA (EA SP) Hierarchical Asynchronous Parallel EA (HAPEA) Population size: 40 Viscous: Grid nbv 2500 Viscous: Grid nbv 2000 Viscous: Grid nbv 1500

29. Pressure Distribution

30. Candidate and Target Geometries

31. Example of Convergence History. A better solution in lower computing time

32. UAV Conceptual Design Optimisation Problem Minimise two objectives: Gross weight  min(WG) Endurance  min (1/E) Subject to: Takeoff distance <1000 ft, Alt Cr > 40000 ROC > 1000 fpm, Endurance > 24 hrs With respect to: external geometry of the aircraft Mach = 0.3 Endurance > 24 hrs Cruise Altitude: 40000 ft

33. Design Variables In total we have 29 design variables Design VariableLower Bound Upper Bound Wing Area (sq ft)280330 Aspect Ratio S1825.2 Wing Sweep (deg) 0.08.0 Wing Taper Ratio0.280.8 13 Configuration Design variables Camber Twist Wing

34. Design Variables Camber Twist Horizontal Tail Area (sq ft) 65.085.0 HT Aspect Ratio3.015.0 HT Taper Ratio0.20.55 HT Sweep (deg)12.015.0 Vertical Tail Area (sq ft) 11.029.0 VT Aspect Ratio1.03.2 VT Taper Ratio0.280.62 VT Sweep (deg)12.034.0 Fuselage Diameter 2.65.0 Tail Fuselage

35. Design Variables: Bounding Envelope of the Aerofoil Search Space Ten control points on the thickness distribution. 16 Design variables for the aerofoil Constraints: Thickness > 12% x/c Pitching moment > -0.065 Two Bezier curves representation: Six control points on the mean line.

36. Mission profile

37. Structural & weight analysis A compromise on fidelity models Vortex induced drag: VLMpc Viscous drag: friction Aerofoil Design Xfoil pMOEA (HAPEA) Optimisation Aircraft design and analysis Aerodynamics FLOPS FLOPS (Modified to accept user computed aerodynamic data) Design Tools

38. Implementation Population size: 20 Grid 141x 74x 36 on aerofoil, 20 x 6 on Vortex model Grid 109x 57x 27 on aerofoil, 17 x 6 on Vortex model Grid 99x 52x 25 on aerofoil, 15 x 6 on Vortex model

39. Pareto optimal region Objective 1 optimal Objective 2 optimal Compromise

40. Sample of Pareto Optimal configurations Pareto Member 0 Pareto Member 14 Pareto Member 16 Pareto Member 19

41. The results indicate that aircraft design optimisation and shape optimisation problem can be resolved with an evolutionary approach using a hierarchical topology. The new method contributes to the development of numerical tools required for the complex task of MDO and aircraft design. A practical design of a long endurance high altitude UAV was studied and realistic designs were obtained. No problem specific knowledge is required  The method appears to be broadly applicable to different analysis codes A family of Pareto optimal configurations was obtained giving the designer a restricted search space to proceed into more details phases of design. Conclusions

42. Acknowledgements The authors would like to thank Arnie McCullers at NASA LARC for providing the FLOPS code. The authors would like to acknowledge Professor Steve Armfield and Dr Patrick Morgan at The University of Sydney for providing the facilities on using the cluster of computers. The authors would like to thank Professor M. Drela for providing the MSES code Also to Professor K. Deb for discussions on developments and applications of MOEA during his visit to The University of Sydney in 2003.

43. Questions…

44. ADDITIONAL SLIDES

45. Flow is treated as two dimensional, inviscid and is calculated using B. Mohammadi code NSC2ke The solver uses unstructured mesh which are generated using Bamg. The computations stop when the 2-norm of the residual falls below a prescribed limit, in this case CFD Solver

46. Optimization Methods Guess /Intuition: decreases as the increasing dimensionality. Nonlinear simplex: simple and robust but inefficient for more than a few design variables. Grid or random search: the cost of searching the design space increases rapidly with the number of design variables. Gradient-based: it is most efficient for a large number of design variables; assumes the objective function is “well-behaved”. Evolution algorithms: good for discrete design variables and very robust; but infeasible when using a large number of design variables.

47. Evolution Algorithm Asynchronous Evaluator 1 individual Different Speeds Ignores the concept of generation-based solution. Fitness functions are computed asynchronously. Only one candidate solution is generated at a time, and only one individual is incorporated at a time rather than an entire population at every generation as is traditional EAs. Solutions can be generated and returned out of order. Asynchronous Evaluation (1)

48. Evolution Algorithm Asynchronous Evaluator 1 individual Different Speeds No need for synchronicity  no possible wait-time bottleneck. No need for the different processors to be of similar speed. Processors can be added or deleted dynamically during the execution. There is no practical upper limit on the number of processors we can use. All desktop computers in an organisation are fair game. Asynchronous Evaluation (2)

49. Generation –based approach used by evolutionary algorithm, traditional genetic algorithms and evolution strategy create an unnecessary bottleneck when used on parallel computers, i.e., the processors that have already completed their solutions will remain idle until all processors have completed their work Need for Asynchronous Evaluation cases used in engineering today may take different times to complete their operations Time taken for solutions of non-linear partial differential equations will strongly depend upon the geometry.

50. Methods of solutions to MO and MDO -> VARIABLE TIME TO COMPLETE. Time TO SOLVE NON-linear pfF - > DEPENDS UPON GEOMETRY Traditional EAs -> create an unnecessary bottleneck when used on parallel computers; -> I.E processors that have already completed their solutions will remain idle until all processors have completed their work.

51. Why MOEAs: MOEA have been studied intensively in the last 8 years and show to be promising and effective for different non -linear problems. In Multi-objective optimisation we seek to find a set of Pareto optimal solutions which are better those other solutions in all objectives. The two main desirable characteristics a MOEA (DEb 2003) are: -- Convergence to true Pareto optimal set -- Good Diversity among the solutions on the optimal set pMOEA In general “..a pMOEA seeks to find as good or better MOP solutions in less time than its serial MOEA counterpart, use less resources, and /or search more of the solution space, i.e., increased efficiency and effectiveness [Veldhuizen et al 2003] Multi-Objective EAs (MOEAs) and parallel (pEAs)

52. Traditional EA techniques, hierarchical methods, deterministic and EAs Navier Stokes computationally expensive but accurate) –panel methods ( fast but could be unstable) Bezier, Splines, depending on the problem Parallelization strategy Optimisation Method Flow analysis method Geometry Representation Implementation pMOEAs Components of pEA optimisation

53. Solved on a single population Asynchronous: Assign a small fictitious delay to each function evaluation. This will vary uniformly between two values fastest and lowest. Evaluate asynchronously. Synchronous: Assign the same delay to all individuals in advance. Wait until the slowest evaluation is completed, as it will occur in practice on a cluster of computers. Four unknowns=4), Stopping Condition = 0.0001, 25 runs. Configurations up to tslowest /tfastest = 5 Asynchronous Test Case – Sphere Function

54. Aircraft Design and Analysis The FLOPS (FLight OPtimisation System) solver developed by L. A. (Arnie) McCullers, NASA Langley Research Center was used for evaluating the aircraft configurations. FLOPS is a workstation based code with capabilities for conceptual and preliminary design of advanced concepts. FLOPS is multidisciplinary in nature and contains several analysis modules including: weights, aerodynamics, engine cycle analysis, propulsion, mission performance, takeoff and landing, noise footprint, cost analysis, and program control. FLOPS has capabilities for optimisation but in this case was used only for analysis. Drag is computed using Empirical Drag Estimation Technique (EDET) - Different hierarchical models are being adapted for drag build up using higher fidelity models.

55. Conclusions The proper use of evolutionary techniques for MDO can reduce weight and improve endurance of an aircraft concept by minor changes in the design variables. The results obtained for the aircraft design optimisation are encouraging and promote application of the method with higher fidelity solvers and approximation techniques.

56. Evolutionary Design Optimisation Problem Definition HAPEA Optimiser Setup Do While Convergence not reached Create and evaluate initial population Generate and evaluate new candidates Evaluation of Candidates Compute the flow around the aerofoil sections and obtain a Cdo estimate for the wing Create drag polar on the candidate geometry Satisfying trim conditions. Analyze each configuration using FLOPS) Compute Objective Functions Evolve/ modify design variables on optimiser until stopping criteria is met.

57. Aerodynamic Analysis Control Points

58. Sample of Pareto Optimal configurations Pareto Member 0 Pareto Member 14 Pareto Member 16 Pareto Member 19 AR =23.77 SW = 321.74 ft2 SWEEP = 4.87 TR = 0.48 ARHT = 5.43 SWPWT = 10.74 TRHT = 0.43 FW = 3148 Lbs ENDR = 1846 min AR =24.30 SW =321.2 ft2 SWEEP =2.77 deg TR = 0.45 ARHT = 5.61 SWPWT = 5.79 TRHT = 0.40 FW =3486 Lbs ENDR= 2184 min AR = 18.9 SW = 301.2 ft2 SWEEP = 7.07 TR = 0.70 ARHT = 3.43 SWPWT = 3.48 TRHT = 0.33 FW =2978 Lbs ENDR= 1533 min AR = 24.14 SW = 305 ft2 SWEEP =2.01 deg TR = 0.62 ARHT = 4.9 SWPWT = 6.55 TRHT = 0.44 FW =3337 Lbs ENDR= 2008 min

59. Convergence history for objective one