Combining a reduced polynomial chaos expansion approach with universal Kriging for uncertainty quantification

Abstract

Engineering design optimization studies are computationally expensive based on the large number of computational fluid dynamics simulations necessary for uncertainty quantification. Polynomial chaos expansion methods have the potential to save computational costs by reducing the number of input design parameters. Kriging methods are able to accurately predict off-design values and give an estimate of their error. In this paper, we combine a reduced dimensional polynomial chaos approach with a universal Kriging method as a new non-intrusive metamodeling method for fast uncertainty quantification and optimization in a simplified engine nacelle inlet design. Its performance is benchmarked against the reduced dimensional polynomial chaos approach and universal Kriging. Results show the reduced-polynomial-chaos-Kriging method gives more accurate results than the reduced dimensional polynomial chaos approach for non-smooth solutions. However, the new method is highly-dependent on the experimental design used and can become discontinuous. The application of a standalone Kriging method on the reduced model produced excellent stability and indicates refinement of the method is possible. © 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.