CMAS Lab - Combining A Reduced Polynomial Chaos Expansion Approach

Journal article

J. Weinmeister, N. Xie, Xinfeng Gao, A. Prasad, Sourajeet Roy
2017

Cite

APA Click to copy
Weinmeister, J., Xie, N., Gao, X., Prasad, A., & Roy, S. (2017). Combining A Reduced Polynomial Chaos Expansion Approach with Universal Kriging for Uncertainty Quantification.

Chicago/Turabian Click to copy
Weinmeister, J., N. Xie, Xinfeng Gao, A. Prasad, and Sourajeet Roy. “Combining A Reduced Polynomial Chaos Expansion Approach with Universal Kriging for Uncertainty Quantification” (2017).

MLA Click to copy
Weinmeister, J., et al. Combining A Reduced Polynomial Chaos Expansion Approach with Universal Kriging for Uncertainty Quantification. 2017.

BibTeX Click to copy

@article{j2017a,
  title = {Combining A Reduced Polynomial Chaos Expansion Approach with Universal Kriging for Uncertainty Quantification},
  year = {2017},
  author = {Weinmeister, J. and Xie, N. and Gao, Xinfeng and Prasad, A. and Roy, Sourajeet}
}

Abstract

Engineering design optimization studies, based on the large number of computational fluid dynamics simulations necessary for uncertainty quantification, are computationally expensive. 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. However, each has its limitations. 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 nonsmooth solutions. However, the new method is highly-dependent on the experimental design. The application of a standalone Kriging method on the reduced model produced excellent stability and indicates refinement of the method is possible.