Multi-Fidelity Robust Aerodynamic Design Optimization under Mixed Uncertainty

Abstract

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.

Department(s)

Mechanical and Aerospace Engineering

Research Center/Lab(s)

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)

1270-9638

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2015 Elsevier Masson SAS, All rights reserved.

Publication Date

01 Sep 2015

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