A ML strategy for the identification of optimal LPT design region and related blade shape

Abstract

This work presents a machine-learning (ML) strategy for the identification of the design region that guarantees minimum losses for Low Pressure Turbine (LPT) blades, allowing the definition of the optimal blade shape. The data-driven procedure is twofold. Firstly, an advanced loss-correlation model (M1) that describes the LPT efficiency as a function of the main flow and geometrical parameters, also accounting for unsteady effects, has been trained from a numerical database. Then, a second model (M2) has been tuned to interpret the corresponding blade geometries. Proper Orthogonal Decomposition (POD) has been applied to formally decompose the blade shape into modes and coefficients. The modes provide basis functions, while the coefficients give the weights that, depending on the combination of the design parameters, define the blade shape. Gaussian Process (GP) and Cross-Validation techniques have been used for tuning both M1 and M2. Once properly tuned, the overall procedure provides the loss-correlation model (M1) for the identification of the design region that is expected to minimize losses, and the geometrical model (M2) for a quick definition of the corresponding optimal blade shape. The procedure can be extended to other engineering applications where own efficiency and geometrical data are available.