Airfoil Optimisation by Swarm Algorithm with Mutation and Artificial Neural Networks
Manas S. Khurana, Hadi Winarto, Arvind Kumar Sinha · 47th AIAA Aerospace Sciences Meeting including The New Horizons Forum and Aerospace Exposition · 2009
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