Influence of Search Algorithms on Aerodynamic Design Optimization of Aircraft Wings

Abstract

The Method of search algorithms or optimisation algorithms is one of the most important parameters which will strongly influence the fidelity of the solution during an aerodynamic shape optimisation problem. Nowadays various optimisation methods such as Genetic Algorithm (GA), Simulated Annealing (SA), Particle Swarm Optimisation (PSO) etc., are more widely employed to solve the aerodynamic shape optimisation problems. In addition to the optimisation method, the geometry parameterisation becomes an important factor to be considered during the aerodynamic shape optimisation process. Since the reduction in the number of design parameters is one of the most important requirements for the aerodynamic shape optimisation problem, it becomes important to mathematically describe the airfoil geometry with minimum number of design parameters. The objective of this work is to introduce the knowledge of describing general airfoil geometry using twelve parameters by representing its shape as a polynomial function and coupling this approach with flow solution and optimisation algorithms. It is also demonstrated that the estimation of a suitable optimisation scheme for a given optimisation problem. An aerodynamic shape optimisation problem is formulated for NACA 0012 airfoil and solved using the methods of Particle Swarm Optimisation and Genetic Algorithm for 5.0 deg angle of attack. The results show that the particle swarm optimisation scheme is more effective in finding the optimum solution among the various possible solutions. It is also found that the PSO shows more exploitation characteristics as compared to the GA which is considered to be more effective explorer.

An Optimum Neural Network for Evolutionary Aerodynamic Shape Design

“…Actually, the boundary values of the input parameters have widespread orders. For example, the domain of changes for the rst PARSEC parameter (r LE ) is [0.006-0.0115] where the domain of the fth parameter ( TE ) is [0][1][2][3][4][5][6][7][8][9][10]. This causes the e ects of the higherorder parameters to be much more than those of the lower-order parameters during the training process.…”

“…Among di erent methods for aerodynamic shape optimization, the Genetic Algorithm (GA) is a popular method that has been widely used by researchers [1,2]. The speci cations of GA cause its superiority to other optimization methods.…”

“…The applications of this method to the airfoil shape optimization are presented in [1,2,[5][6][7]. However, the unfavorable key about GA is the computational time consumed in aerodynamic shape optimization problems when Computational Fluid Dynamic (CFD) methods are used for tness function calculation.…”

Multi-point optimization of transonic airfoils using an enhanced genetic algorithm

“…Such features make GA attractive to practical engineering applications like aerodynamic shape optimization. [4][5][6][7][8][9] However, unfavorable point about GA is the computational time consumed in aerodynamic shape optimization problems when computational fluid dynamics (CFD) methods are used for fitness function calculation. This will be more crucial when a multipoint optimization is concerned; because the number of CFD computations has to be doubled in these cases.…”

“…Such features make GA attractive to practical engineering applications like aerodynamic shape optimization. 4–9…”