Abstract

Proper Orthogonal Decomposition POD) has been applied to a large dataset describing the profile losses of Low Pressure Turbine (LPT) cascades, thus allowing: i) the identification of the most influencing parameters that affect the loss generation; ii) the identification of the minimum number of requested conditions useful to educate a model with a reduced number of data. The dataset is constituted by the total pressure loss coefficient distributions in the pitchwise direction. Two cascades are considered: the first for tuning the procedure and identifying the number of really requested tests, and the second for the verification of the proposed model. Since the POD space shows an optimal basis describing the overall process with a low rank representation (LRR), a smooth kernel is educated by means of Least-Squares method (LSM) on the POD eigenvectors. Particularly, only a subset of data (equal to the rank of the problem) has been used to generate the POD modes and related coefficients. Thanks to the LRR of the problem in the POD space, predictors are low order polynomials of the independent variables (Re, f + and f ). It will be shown that the smooth kernel adequately estimates the loss distribution in points that do not participate to the education. Thus, analysis show that the rank of the problem is lower than the tested conditions, and consequently a reduced number of tests are really necessary. This could be useful to reduce the number of hi-fidelity simulations or detailed experiments in the future.

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