Surrogate Models for Aerodynamic Shape Optimisation

The main challenges in full-scale aerospace systems development are related to the level of our understanding with respect to the systems behaviour. Computational modelling, through high-fidelity simulations, provides a viable approach towards efficient implementation of the design specifications and enhancing our understanding of the system’s response. Although high-fidelity modelling provides valuable information the associated computational cost restricts its applicability to full-scaled systems. This chapter presents a Computational Fluid Dynamics optimisation strategy based on surrogate modelling for obtaining high-fidelity predictions of aerodynamic forces and aerodynamic efficiency. An Aerodynamic Shape Optimisation problem is formulated and solved using Genetic Algorithm with surrogate models in the place of actual computational fluid dynamics algorithms. Ordinary Kriging approach and Hammersley Sequence Sampling plan are used to construct the surrogate models.

[1]  Daniel Raymer,et al.  Enhancing Aircraft Conceptual Design using Multidisciplinary Optimization , 2002 .

[2]  Urmila M. Diwekar,et al.  An efficient sampling technique for off-line quality control , 1997 .

[3]  E. Tadmor,et al.  New High-Resolution Central Schemes for Nonlinear Conservation Laws and Convection—Diffusion Equations , 2000 .

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

[5]  U Selvakumar,et al.  Notice of RetractionAerodynamic shape optimization using computer mapping of natural evolution process , 2010, 2010 2nd International Conference on Computer Engineering and Technology.

[6]  Thomas A. Zang,et al.  AIAA 99-3798 Multidisciplinary Design Optimization Techniques: Implications and Opportunities for Fluid Dynamics Research , 1999 .

[7]  Frank Thiele,et al.  Knowledge Based Airfoil Aerodynamic and Aeroacoustic Design , 2005 .

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

[9]  Michael S. Eldred,et al.  OVERVIEW OF MODERN DESIGN OF EXPERIMENTS METHODS FOR COMPUTATIONAL SIMULATIONS , 2003 .

[10]  Patrice Castonguay,et al.  Effect of Shape Parameterization on Aerodynamic Shape Optimization , 2007 .

[11]  David E. Goldberg,et al.  Genetic Algorithms, Tournament Selection, and the Effects of Noise , 1995, Complex Syst..

[12]  F. Wubs.  Notes on numerical fluid mechanics , 1985 .

[13]  P. Sagaut,et al.  Building Efficient Response Surfaces of Aerodynamic Functions with Kriging and Cokriging , 2008 .

[14]  H. Sobieczky.  Parametric Airfoils and Wings , 1999 .

[15]  Johan Andersson,et al.  A survey of multiobjective optimization in engineering design , 2001 .

[16]  J. Hajek.  Parameterization of Airfoils and Its Application in Aerodynamic Optimization , 2007 .

[17]  Anthony A. Giunta,et al.  Aircraft Multidisciplinary Design Optimization using Design of Experiments Theory and Response Surface Modeling Methods , 1997 .

[18]  Timothy W. Simpson,et al.  Sampling Strategies for Computer Experiments: Design and Analysis , 2001 .

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

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