TY - GEN
T1 - Combining a reduced polynomial chaos expansion approach with universal Kriging for uncertainty quantification
AU - Weinmeister, Justin
AU - Xie, Nelson
AU - Gao, Xinfeng
AU - Prasad, Aditi Krishna
AU - Roy, Sourajeet
N1 - Publisher Copyright:
© 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85088069455&partnerID=8YFLogxK
U2 - 10.2514/6.2017-3481
DO - 10.2514/6.2017-3481
M3 - Conference contribution
AN - SCOPUS:85088069455
SN - 9781624104978
T3 - 8th AIAA Theoretical Fluid Mechanics Conference, 2017
BT - 8th AIAA Theoretical Fluid Mechanics Conference, 2017
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - 8th AIAA Theoretical Fluid Mechanics Conference, 2017
Y2 - 5 June 2017 through 9 June 2017
ER -