Evolutionary Multiobjective Optimization of Winglets

Evolutionary multiobjective optimization is employed for designing the geometric configurations of winglets adapted to a base wing. Seven decision variables are employed for the winglet parameterization, and the wing-winglet transition region is modeled using Bezier surfaces. A case study is presented to illustrate the application of this technique to the design of wingtip devices. The optimization model includes two objectives: the ratio of drag-lift coefficients, and the wing root bending moment coefficient. The solutions obtained are discussed, and a Monte Carlo sensitivity analysis is performed to test the robustness of the results to uncertainties in the variables. Finally, a winglet geometry is suggested to be retrofitted to the base wing, providing an increased lift to drag ratio at the expense of increasing the root bending moment of the wing.

[1]  Bogdan Filipic,et al.  DEMO: Differential Evolution for Multiobjective Optimization , 2005, EMO.

[2]  M. Giles,et al.  Viscous-inviscid analysis of transonic and low Reynolds number airfoils , 1986 .

[3]  B. Thwaites,et al.  Approximate Calculation of the Laminar Boundary Layer , 1949 .

[4]  R. W. Derksen,et al.  Differential Evolution in Aerodynamic Optimization , 1999 .

[5]  D. F. Rogers,et al.  An Introduction to NURBS: With Historical Perspective , 2011 .

[6]  A. D. Young The Calculation of the Profile Drag of Aerofoils and Bodies of Revolution at Supersonic Speeds , 1953 .

[7]  Man Mohan Rai,et al.  Robust Optimal Design With Differential Evolution , 2004 .

[8]  Felipe Campelo,et al.  Preference-guided evolutionary algorithms for many-objective optimization , 2016, Inf. Sci..

[9]  Guillermo Paniagua,et al.  Multidisciplinary design optimization of a compact highly loaded fan , 2014 .

[10]  Douglas James Bayley Design Optimization of Space Launch Vehicles Using a Genetic Algorithm , 2007 .

[11]  Michael Stadler,et al.  Inverse Aeroacoustic Design of Axial Fans Using Genetic Optimization and the Lattice-Boltzmann Method , 2014 .

[12]  Eckart Zitzler,et al.  Indicator-Based Selection in Multiobjective Search , 2004, PPSN.

[13]  Jason E. Hicken,et al.  Induced-Drag Minimization of Nonplanar Geometries Based on the Euler Equations , 2010 .

[14]  George S. Dulikravich,et al.  Multi-Winglets: Multi-Objective Optimization of Aerodynamic Shapes , 2015 .

[15]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[16]  Steven M. Willits,et al.  THE DESIGN AND TESTING OF A WINGLET AIRFOIL FOR LOW-SPEED AIRCRAFT , 2001 .

[17]  Jacob R. Weierman,et al.  Winglet Design and Optimization for UAVs , 2010 .

[18]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[19]  Richard T. Whitcomb,et al.  A design approach and selected wind tunnel results at high subsonic speeds for wing-tip mounted winglets , 1976 .

[20]  Roy J. Hartfield,et al.  Design Optimization of a Space Launch Vehicle Using a Genetic Algorithm , 2007 .

[21]  David W. Zingg,et al.  A Numerical Optimization Study on Winglets , 2014 .

[22]  H. Sobieczky,et al.  WINGLETS – MULTIOBJECTIVE OPTIMIZATION OF AERODYNAMIC SHAPES , 2014 .

[23]  Michael Stadler,et al.  Inverse Aeroacoustic Design of Axial Fans Using Genetic Optimization and the Lattice-Boltzmann Method , 2013 .

[24]  L. A. Vargas Desenvolvimento e implementação de um procedimento numérico para cálculo de conjuntos asa-empenagens de geometria complexa em regime de vôo subsônico, assimétrico e não linear , 2006 .

[25]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[26]  Kazuhiro Nakahashi,et al.  Multi-disciplinary design exploration for winglet , 2008 .

[27]  T. Kármán Über laminare und turbulente Reibung , 1921 .

[28]  M. R. Head,et al.  Entrainment in the Turbulent Boundary Layer , 2002 .

[29]  David F. Rogers,et al.  An Introduction to NURBS , 2000 .