Multi-Level CFD-Based Airfoil Shape Optimization With Automated Low-Fidelity Model Selection

Abstract Computational fluid dynamic (CFD) models are ubiquitous in aerodynamic design. Variable-fidelity optimization algorithms have proven to be computationally efficient and therefore suitable to reduce high CPU-cost related to the design process solely based on accurate CFD models. A convenient way of constructing the variable-fidelity models is by using the high-fidelity solver, but with a varying degree of discretization and reduced number of flow solver iterations. So far, selection of the appropriate parameters has only been guided by the designer experience. In this paper, an automated low- fidelity model selection technique is presented. By defining the problem as a constrained nonlinear optimization problem, suitable grid and flow solver parameters are obtained. Our approach is compared to conventional methods of generating a family of variable-fidelity models. Comparison of the standard and the proposed approaches in the context of aerodynamic design of a transonic airfoil indicates that the automated model generation can yield significant computational savings.

[1]  A. J. Booker,et al.  A rigorous framework for optimization of expensive functions by surrogates , 1998 .

[2]  Leifur Leifsson,et al.  Surrogate-Based Methods , 2011, Computational Optimization, Methods and Algorithms.

[3]  Theresa Dawn Robinson,et al.  Surrogate-Based Optimization Using Multifidelity Models with Variable Parameterization and Corrected Space Mapping , 2008 .

[4]  G. Janiga,et al.  Optimization and Computational Fluid Dynamics , 2008 .

[5]  S. Koziel,et al.  Space mapping , 2008, IEEE Microwave Magazine.

[6]  Antony Jameson,et al.  Viscous Aerodynamic Shape Optimization of Wings including Planform Variables , 2003 .

[7]  P. A. Newman,et al.  Optimization with variable-fidelity models applied to wing design , 1999 .

[8]  Slawomir Koziel,et al.  Surrogate-Based Modeling and Optimization , 2013 .

[9]  René Van den Braembussche,et al.  Numerical Optimization for Advanced Turbomachinery Design , 2008 .

[10]  Paul M. Weaver,et al.  47th AIAA?ASME/ASCE/AHS Structures, Structural Dynamics and Materials Conference, Newport RI, USA , 2006 .

[11]  Andy J. Keane,et al.  Airfoil Design and Optimization Using Multi-Fidelity Analysis and Embedded Inverse Design , 2006 .

[12]  Andy J. Keane,et al.  Recent advances in surrogate-based optimization , 2009 .

[13]  Slawomir Koziel,et al.  Multi-fidelity design optimization of transonic airfoils using physics-based surrogate modeling and shape-preserving response prediction , 2010, J. Comput. Sci..

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