PALIOTTA P.

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...

This work provides new correlations based on local variables for characterizing the transition process developing in the case of separated flows. The goal is to improve the capability of correlation-based transition models through the use of local variables. This may indeed simplify the implementation of the correlations into modern numerical...

In the present work linear and non-linear regression functions have been tuned with an extensive database describing the unsteady aerodynamic efficiency of low-pressure-turbine cascades. The learning strategy has been first defined using a dataset published in a previous work concerning the loss coefficient measured in a large-scale cascade for...

In the present work, an Hot-Wire Anemometer and a five-hole pressure probe have been used to characterize the incoming flow of a large-scale turbine cascade. Measurements have been carried out to sample the flow in both spanwise and pitchwise directions, hence allowing a complete characterization of total pressure, mean velocity, turbulence...