Abstract
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.
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