Multi-Fidelity Robust Aerodynamic Design Optimization under Mixed Uncertainty
The objective of this paper is to present a robust optimization algorithm for computationally efficient airfoil design under mixed (inherent and epistemic) uncertainty using a multi-fidelity approach. This algorithm exploits stochastic expansions derived from the Non-Intrusive Polynomial Chaos (NIPC) technique to create surrogate models utilized in the optimization process. A combined NIPC expansion approach is used, where both the design and the mixed uncertain parameters are the independent variables of the surrogate model. To reduce the computational cost, the high-fidelity Computational Fluid Dynamics (CFD) model is replaced by a suitably corrected low-fidelity one, the latter being evaluated using the same CFD solver but with a coarser mesh. The model correction is implemented to the low-fidelity CFD solutions utilized for the construction of stochastic surrogate by using multi-point Output Space Mapping (OSM) technique. The proposed algorithm is applied to the design of NACA 4-digit airfoils with four deterministic design variables (the airfoil shape parameters and the angle of attack), one aleatory uncertain variable (the Mach number) and one epistemic variable (a geometry parameter) to demonstrate robust optimization under mixed uncertainties. In terms of computational cost, the proposed technique outperforms the conventional approach that exclusively uses the high-fidelity model to create the surrogates. The design cost reduces to only 34 equivalent high-fidelity model evaluations versus 168 obtained with the conventional method.
H. R. Shah et al., "Multi-Fidelity Robust Aerodynamic Design Optimization under Mixed Uncertainty," Aerospace Science and Technology, vol. 45, pp. 17 - 29, Elsevier Masson SAS, Sep 2015.
The definitive version is available at https://doi.org/10.1016/j.ast.2015.04.011
Mechanical and Aerospace Engineering
Center for High Performance Computing Research
Keywords and Phrases
Aerodynamics; Airfoils; Algorithms; Angle of Attack; Computational Fluid Dynamics; Cost Reduction; Costs; Design; Fluid Dynamics; Fuel Additives; Mapping; Optimization; Stochastic Models; Stochastic Systems; Uncertainty Analysis; Aerodynamic Shape Optimization; Multi-Fidelity Modeling; Polynomial Chaos; Robust Designs; Space Mappings; Shape Optimization
International Standard Serial Number (ISSN)
Article - Journal
© 2015 Elsevier Masson SAS, All rights reserved.
01 Sep 2015