Particle Image Velocimetry (PIV) measurements have been performed in a low-pressure turbine cascade operating under unsteady inflow conditions to analyze the wake-boundary layer unsteady interaction. Data have been post-processed by means of Proper Orthogonal Decomposition (POD) to improve the understanding of the physics of this complex phenomenon. The method proposed in the paper takes advantage of the POD properties, which consist in splitting spatial (POD modes) and temporal (POD eigenvector) information. The phase identification of each PIV image within the wake passing cycle is extracted from the eigenvectors of properly selected POD modes. The paper describes the procedure employed to sort the experimental PIV snapshots and then reconstruct a phase-averaged velocity field. A very large data set has been collected to provide phase-averaged distributions of the velocity and turbulence fields with high statistical accuracy. The great spatial resolution of the PIV data allows an in depth analysis of the propagation of the large scale vortical structures recognized at the leading and trailing boundaries of the negative-jet like structure. Furthermore, smaller scale structures embedded within the bulk of the wake have been found to continuously interact with the suction side boundary layer, especially at the leading boundary of the wake where the negative-jet induces local acceleration in the flow.

POD Analysis of the Wake-Boundary Layer Unsteady Interaction in a LPT Blade Cascade

BERRINO, MARCO;LENGANI, DAVIDE;SIMONI, DANIELE;UBALDI, MARINA;ZUNINO, PIETRO
2015-01-01

Abstract

Particle Image Velocimetry (PIV) measurements have been performed in a low-pressure turbine cascade operating under unsteady inflow conditions to analyze the wake-boundary layer unsteady interaction. Data have been post-processed by means of Proper Orthogonal Decomposition (POD) to improve the understanding of the physics of this complex phenomenon. The method proposed in the paper takes advantage of the POD properties, which consist in splitting spatial (POD modes) and temporal (POD eigenvector) information. The phase identification of each PIV image within the wake passing cycle is extracted from the eigenvectors of properly selected POD modes. The paper describes the procedure employed to sort the experimental PIV snapshots and then reconstruct a phase-averaged velocity field. A very large data set has been collected to provide phase-averaged distributions of the velocity and turbulence fields with high statistical accuracy. The great spatial resolution of the PIV data allows an in depth analysis of the propagation of the large scale vortical structures recognized at the leading and trailing boundaries of the negative-jet like structure. Furthermore, smaller scale structures embedded within the bulk of the wake have been found to continuously interact with the suction side boundary layer, especially at the leading boundary of the wake where the negative-jet induces local acceleration in the flow.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/809887
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