Hierarchical robust design optimisation of shock control bump devices for airfoil drag reduction


Autoria(s): Lee, D.S.; Gonzalez, L.F.; Periaux, J.; Bugeda, G.
Contribuinte(s)

Tuncer, Ismail

Data(s)

2011

Resumo

The paper investigates a detailed Active Shock Control Bump Design Optimisation on a Natural Laminar Flow (NLF) aerofoil; RAE 5243 to reduce cruise drag at transonic flow conditions using Evolutionary Algorithms (EAs) coupled to a robust design approach. For the uncertainty design parameters, the positions of boundary layer transition (xtr) and the coefficient of lift (Cl) are considered (250 stochastic samples in total). In this paper, two robust design methods are considered; the first approach uses a standard robust design method, which evaluates one design model at 250 stochastic conditions for uncertainty. The second approach is the combination of a standard robust design method and the concept of hierarchical (multi-population) sampling (250, 50, 15) for uncertainty. Numerical results show that the evolutionary optimization method coupled to uncertainty design techniques produces useful and reliable Pareto optimal SCB shapes which have low sensitivity and high aerodynamic performance while having significant total drag reduction. In addition,it also shows the benefit of using hierarchical robust method for detailed uncertainty design optimization.

Identificador

http://eprints.qut.edu.au/49620/

Publicador

ECCOMAS

Relação

Lee, D.S., Gonzalez, L.F., Periaux, J., & Bugeda, G. (2011) Hierarchical robust design optimisation of shock control bump devices for airfoil drag reduction. In Tuncer, Ismail (Ed.) Proceedings of ECCOMAS Thematic Conference - CFD and Optimization, ECCOMAS, Zeynep Golf Resort, Antalya, Turkey, pp. 1-15.

Fonte

School of Electrical Engineering & Computer Science; Science & Engineering Faculty

Palavras-Chave #010303 Optimisation #090104 Aircraft Performance and Flight Control Systems #Computational Fluid Dynamics #Shock Control Bump #Evolutionary Optimization #Pareto Game #Robust Stochastic Optimization #Uncertainties
Tipo

Conference Paper