Cascade With Sinusoidal Leading Edges: Identification And Quantification of Deflection With Unsupervised Machine Learning

Abstract

One of the key issues in turbomachinery design is the identification of loss mechanisms and their quantification, both during preliminary design and in all subsequent optimization loops. Over the years, many correlations have been proposed, accounting for different dissipative mechanisms that occur in blade-to-blade passages, such as the development of boundary layers, turbulent wake mixing, shockwaves, and secondary flows or off-design incidence. In recent years, the fan industry started the production of more complex rotor geometries, characterized by sinusoidal leading and trailing edges, mostly to extend stall margin and to reduce noise emissions. Literature still lacks a quantification of the losses introduced by the secondary motions released by serrated leading-edges. In this paper we investigate a design of experiments that entails 76 cases of a 3D flow cascade with NACA 4digit profiles with sinusoidal leading edges to measure losses according to the Lieblein’s approach. The flow field simulated with RANS strategy was investigated using an unsupervised machine learning strategy to classify and isolate the turbulent wake downstream of the cascade with a combination of Principal Component Analysis and Gaussian Mixture clustering. Then a gradient boosting regressor was used to derive the correlation between input parameters and cascade deflection.