An investigation on winglet design with limited computational cost, using an efficient optimization method

Abstract A fast and efficient optimization method is proposed for winglet design optimization. The optimization method starts from a random population, whose variables are normalized in the range [ 0 , 1 ] . Population members are then divided into two expert groups: the free group and the guided group; each has specific tasks for the active search of the domain but with a single operator. The deliberate search of the expert groups and proper adjustment of diversity and intensification leads to rapid population direction towards global optimization. The global performance of the method is first evaluated in two standard problems. Comparison of the results with alternative methods shows that the proposed method is superior in terms of convergence speed and accuracy. Then, a winglet design optimization problem for the DLR-F6 wing is carried out. The objective function evaluation is done by a high-fidelity computational fluid dynamics method. A robust CAD-based method is used for winglet shape parameterization. Results show that the new method is able to outperform particle swarm optimization (PSO), genetic algorithm (GA), and Mean-Variance Mapping Optimization (MVMO). The optimal winglet achieves a drag coefficient reduction of 9.19% compared to the initial wing while keeping the lift coefficient unchanged. Then, the effect of winglet design variables on the optimization process is investigated. It is found that the best result with minimum computational cost is achieved when using only three winglet shape parameters of length, twist angle and angle of attack. Finally, the root bending moment minimization is added to the objective function as a structural consideration, and its effects on the results are studied.

[1]  Gang Sun,et al.  A robust design for a winglet based on NURBS-FFD method and PSO algorithm , 2017 .

[2]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[3]  Narasimhan Sundararajan,et al.  Improved SRPSO algorithm for solving CEC 2015 computationally expensive numerical optimization problems , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[4]  Kazuhiro Nakahashi,et al.  Multidisciplinary Design Exploration for a Winglet , 2008 .

[5]  Jun Zhang,et al.  Aerodynamic shape optimization by continually moving ROM , 2020 .

[6]  A. Jahangirian,et al.  Accelerating global optimization of aerodynamic shapes using a new surrogate-assisted parallel genetic algorithm , 2017 .

[7]  Singiresu S. Rao.  Engineering Optimization : Theory and Practice , 2010 .

[8]  Fuxin Wang,et al.  High-resolution numerical investigation into the effects of winglet on the aerodynamic performance for a three-dimensional vertical axis wind turbine , 2020 .

[9]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[10]  Fabrizio Nicolosi,et al.  Aerodynamic guidelines in the design and optimization of new regional turboprop aircraft , 2014 .

[11]  Ali Elham,et al.  Winglet multi-objective shape optimization , 2014 .

[12]  Joel Guerrero,et al.  Biomimetic spiroid winglets for lift and drag control , 2012 .

[13]  Marija Samardžić,et al.  UAV aerodynamic design involving genetic algorithm and artificial neural network for wing preliminary computation , 2019, Aerospace Science and Technology.

[14]  Nilay Sezer-Uzol,et al.  NREL VI rotor blade: numerical investigation and winglet design and optimization using CFD , 2014 .

[15]  Patrice Castonguay,et al.  Cross Validation of Aerodynamic Shape Optimization Methodologies for Aircraft Wing-Body Optimization , 2020, AIAA Journal.

[16]  João Paulo Eguea,et al.  Fuel efficiency improvement on a business jet using a camber morphing winglet concept , 2020 .

[17]  Joaquim R. R. A. Martins,et al.  Robust aerodynamic shape optimization—From a circle to an airfoil , 2019, Aerospace Science and Technology.

[18]  J. Samareh.  Survey of Shape Parameterization Techniques for High-Fidelity Multidisciplinary Shape Optimization , 2001 .

[19]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[20]  Qian Liu,et al.  A gradient-based aero-stealth optimization design method for flying wing aircraft , 2019, Aerospace Science and Technology.

[21]  Aytaç Altan,et al.  Performance of Metaheuristic Optimization Algorithms based on Swarm Intelligence in Attitude and Altitude Control of Unmanned Aerial Vehicle for Path Following , 2020, 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT).

[22]  V. Kolár,et al.  Vortex identification: New requirements and limitations , 2007 .

[23]  A. Jahangirian,et al.  Airfoil shape parameterization for optimum Navier–Stokes design with genetic algorithm , 2007 .

[24]  A. Jahangirian,et al.  Robust aerodynamic morphing shape optimization for high-lift missions , 2020 .

[25]  István Erlich,et al.  Testing MVMO on learning-based real-parameter single objective benchmark optimization problems , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[26]  Zhenguo Wang,et al.  Winglet design for vertical axis wind turbines based on a design of experiment and CFD approach , 2019, Energy Conversion and Management.

[27]  Joaquim R. R. A. Martins,et al.  Design of a transonic wing with an adaptive morphing trailing edge via aerostructural optimization , 2018, Aerospace Science and Technology.

[28]  Melissa B. Rivers,et al.  Experimental Investigation of the DLR-F6 Transport Configuration in the National Transonic Facility , 2008 .

[29]  Jun Tao,et al.  Robust optimization for a wing at drag divergence Mach number based on an improved PSO algorithm , 2019, Aerospace Science and Technology.