Aerodynamic design prediction using surrogate-based modeling in genetic algorithm architecture

Abstract A new optimization algorithm is proposed and tested for four different test cases: benchmark test functions, multi-element airfoil optimization in subsonic flow, active flow control parameter optimization in transonic flow, and a direct shape optimization of an airfoil in transonic flow. The new algorithm emphasizes the use of an aerodynamic design prediction based on a global and a local surrogate modelling in genetic algorithm structure. A global response surface approximation is modelled by using low-order polynomials. A local response surface approximation is constructed by using neural networks. For all the demonstration problems considered herein, remarkable reductions in the computational times have been accomplished. The new approach significantly decreases the required CFD calls by approximately 50% in multi-element airfoil optimization; more than 70% in active flow control parameter optimization; and approximately 50% in direct shape optimization problem.

[1]  Wei Shyy,et al.  Shape optimization of supersonic turbines using global approximation methods , 2002 .

[2]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[3]  Raphael T. Haftka,et al.  Surrogate-based Analysis and Optimization , 2005 .

[4]  Gerald Farin,et al.  Curves and surfaces for computer aided geometric design , 1990 .

[5]  Pierre Sagaut,et al.  A surrogate-model based multidisciplinary shape optimization method with application to a 2D subsonic airfoil , 2007 .

[6]  Florent Duchaine,et al.  Computational-Fluid-Dynamics-Based Kriging Optimization Tool for Aeronautical Combustion Chambers , 2009 .

[7]  Christopher L. Rumsey,et al.  Prediction of high lift: review of present CFD capability , 2002 .

[8]  Andy J. Keane,et al.  Statistical Improvement Criteria for Use in Multiobjective Design Optimization , 2006 .

[9]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[10]  Armando Vavalle,et al.  Iterative Response Surface Based Optimization Scheme for Transonic Airfoil Design , 2007 .

[11]  Oktay Baysal,et al.  Vibrational genetic algorithm enhanced with fuzzy logic and neural networks , 2010 .

[12]  P. Spalart.  A One-Equation Turbulence Model for Aerodynamic Flows , 1992 .

[13]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[14]  Yasin Volkan Pehlivanoglu,et al.  Hybrid Intelligent Optimization Methods for Engineering Problems , 2010 .

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

[16]  A. Jameson,et al.  Multi-Element High-Lift Configuration Design Optimization Using Viscous Continuous Adjoint Method , 2004 .

[17]  Ning Qin,et al.  Surface suction on aerofoil aerodynamic characteristics at transonic speeds , 1998 .

[18]  Abdurrahman Hacioglu,et al.  Fast evolutionary algorithm for airfoil design via neural network , 2007 .

[19]  Peter Horst,et al.  Improved Representation of High-Lift Devices for a Multidisciplinary Conceptual Aircraft Design Process , 2009 .

[20]  P. Roe.  Approximate Riemann Solvers, Parameter Vectors, and Difference Schemes , 1997 .

[21]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[22]  C. P. van Dam,et al.  The aerodynamic design of multi-element high-lift systems for transport airplanes , 2002 .

[23]  Sergey Peigin,et al.  Robust optimization of 2D airfoils driven by full Navier–Stokes computations , 2004 .

[24]  Drew Landman,et al.  Experimental Geometry Optimization Techniques for Multi-Element Airfoils , 2000 .

[25]  Andy J. Keane,et al.  A new hybrid updating scheme for an evolutionary search strategy using genetic algorithms and kriging , 2005 .

[26]  Bryan Glaz,et al.  Multiple-Surrogate Approach to Helicopter Rotor Blade Vibration Reduction , 2009 .

[27]  Ibrahim Ozkol,et al.  Vibrational genetic algorithm as a new concept in airfoil design , 2002 .

[28]  Wei Chen,et al.  Multiresponse and Multistage Metamodeling Approach for Design Optimization , 2009 .

[29]  W. Vent,et al.  Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. Broschiert , 1975 .

[30]  A. Jameson.  Optimum aerodynamic design using CFD and control theory , 1995 .

[31]  A. Keane,et al.  Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling , 2003 .

[32]  Gerald E. Farin,et al.  Curves and surfaces for computer-aided geometric design - a practical guide, 4th Edition , 1997, Computer science and scientific computing.

[33]  Christine M. Anderson-Cook,et al.  Practical Genetic Algorithms , 2005 .