Design optimization of aerodynamic shapes of a wing and its winglet using modified quantum-behaved particle swarm optimization algorithm

Abstract

The modified quantum-behaved particle swarm optimization algorithm is developed. It has the ability to learn from excellent individuals and precisely update all the particles that are involved in computational fluid dynamics computation. The airfoil parameterization method of the Hicks–Henne form function was also improved. The Reynolds averaged Navier–Stokes equation solver and the multi-objective and nonlinear adaptive value weighting method were used to optimize a transonic and high-aspect-ratio swept-back wing and winglet. The optimization results show that the drag characteristics of the optimized configuration are reduced greatly, the shock-wave amplitude on the wing is reduced, and intense shock wave on the winglet is completely eliminated, thus indicating that this method has strong engineering practicality.