This effort develops a surrogate modeling approach for predicting the effects of manufacturing variations on blade unsteadiness and performance of a transonic turbine. CFD results from a set of 105 as-manufactured turbine blade geometries are used to train and validate the surrogate models. Blade geometry variation is characterized with point clouds created from a structured light optical measurement system and as-measured CFD grids are generated through mesh morphing of the nominal design grid data. Results from a Reynolds-averaged Navier-Stokes flow solver with the two-equation Wilcox turbulence model are used as training and validation data. Principal Component Analysis (PCA) of the measured airfoil geometry variations is used to create reduced basis of independent surrogate model parameters. Results of the surrogate are compared to the CFD results. It is shown that the surrogate model typically captures between 60% and 80% of the full CFD predicted variance. Three new approaches are introduced to improve the accuracy of the surrogate. A zonal PCA approach is defined which improves surrogate accuracy by focusing on key regions of the airfoil. A training point reduction strategy is proposed that is based on the kd-tree nearest neighbor search algorithm and reduces the required training points by 25%. A second reduction approach uses k-means clustering to effectively select training points from the 105 blade population and is used to reduce the required training points by up to 66%.

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