This work proposes a new method for using reduced-order models in lieu of high-fidelity analysis during the sensitivity analysis step. Reduced-order models are developed using a combination of proper orthogonal decomposition and radial basis functions. Interpolation using reduced-order models based on proper orthogonal decomposition is compared against normal optimization on a general airfoil shape optimization problem. The interpolation procedure does not require additional high-fidelity evaluations to construct new reduced-order models. The errors associated with the reduced-order models themselves as well as the gradients calculated from them are compared. The effects of each approach on the overall optimization paths, times, and function counts are also examined.
Reduced-Order Techniques for Sensitivity Analysis and Design Optimization of Aerospace Systems
AIAA Journal ; 53 , 12 ; 3567-3576
2015-10-07
10 pages
Article (Journal)
Electronic Resource
English
Reduced-Order Techniques for Sensitivity Analysis and Design Optimization of Aerospace Systems
Online Contents | 2015
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|British Library Conference Proceedings | 2015
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