American Institute of Aeronautics and Astronautics 1 Airfoil Optimisation by Swarm Algorithm with Mutation and Artificial Neural Networks Manas S. Khurana 1 , Hadi Winarto 2 and Arvind K. Sinha 3 The Sir Lawrence Wackett Aerospace Centre – RMIT University, Melbourne, VIC, 3000 The process of aerodynamic shape optimisation requires the development of intelligent models to address the stipulated design goals. The Direct Numeric Optimisation (DNO) approach is examined in this paper, which analyses the feasibility of a shape, in iteration until convergence based on defined objectives and constraints. The method is computationally intensive hence the components of the DNO architecture are defined, validated and modified to generate an efficient search optimisation model. Efficiency is enhanced by mapping the solution space for High-Altitude Long Endurance (HALE) airfoil design problem, through an inverse mapping of PARSEC airfoil shape variables over a series of benchmark profiles. Solution regions with aerodynamically infeasible shapes are identified and eliminated from the search process, to reduce computational time. A single-point airfoil optimisation with Gradient-Based method, over the defined search space is examined. Variations in base airfoils confirmed the solution space is highly multimodal and gradient methods merely locate the local optima. A Particle Swarm Optimisation (PSO) algorithm incorporating a double-mutation operator to mitigate sub-optimal solutions, for highly multimodal solution topologies was defined and validated. The swarm algorithm for airfoil shape optimisation confirmed the limited search flexibility of gradient methods, by establishing a global solution with a 16% reduction in drag. The swarm algorithm is computationally intense for shape optimisation. An Artificial Neural Network (ANN) is developed and validated with a relationship between the mapped PARSEC solution space and the aerodynamic coefficients of lift and drag established. A network sensitivity study indicated a double-layered network with 30 neurons for lift and 20 for drag is required to establish the aerodynamic coefficients with acceptable accuracy. The surrogate model is used for airfoil shape optimisation by replacing the flow solver from the DNO loop. Time savings are established with the aerodynamic performance of the output solution in line with the results of the direct PSO-Flow-Solver combination. Neural network simulations for fitness function approximation are prone to errors. Hence, future research will focus on developing a hybrid search methodology by integrating the flow solver and ANN in the DNO approach. 1 PhD Candidate, The Sir Lawrence Wackett Aerospace Centre – RMIT University, 850 Lorimer Street, Port Melbourne, VIC 3000, Australia – AIAA Student Member 2 Associate Professor, School of Aerospace, Mechanical and Manufacturing Engineering – RMIT University, GPO Box 2476V, 3001, VIC, AIAA Member 3 Director Aerospace and Aviation, The Sir Lawrence Wackett Aerospace Centre – RMIT University, 850 Lorimer Street, Port Melbourne, VIC 3000, Australia - AIAA Member. 47th AIAA Aerospace Sciences Meeting Including The New Horizons Forum and Aerospace Exposition 5 - 8 January 2009, Orlando, Florida AIAA 2009-1278 Copyright © 2009 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.