Unsteady Interaction Between a Transonic Turbine Stage and Downstream Components

Author:

Davis Roger L.1,Yao Jixian2,Clark John P.3,Stetson Gary2,Alonso Juan J.2,Jameson Antony2,Haldeman Charles W.4,Dunn Michael G.4

Affiliation:

1. United Technologies Research Center, East Hartford, CT

2. Stanford University, Stanford, CA

3. Pratt & Whitney, East Hartford, CT

4. Ohio State University, Columbus, OH

Abstract

Results from a numerical simulation of the unsteady flow through one quarter of the circumference of a transonic high-pressure turbine stage, transition duct, and low-pressure turbine first vane are presented and compared with experimental data. Analysis of the unsteady pressure field resulting from the simulation shows the effects of not only the rotor/stator interaction of the high-pressure turbine stage but also new details of the interaction between the blade and the downstream transition duct and low-pressure turbine vane. Blade trailing edge shocks propagate downstream, strike, and reflect off of the transition duct hub and/or downstream vane leading to high unsteady pressure on these downstream components. The reflection of these shocks from the downstream components back into the blade itself has also been found to increase the level of unsteady pressure fluctuations on the uncovered portion of the blade suction surface. In addition, the blade tip vortex has been found to have a moderately strong interaction with the downstream vane even with the considerable axial spacing between the two blade-rows. Fourier decomposition of the unsteady surface pressure of the blade and downstream low-pressure turbine vane shows the magnitude of the various frequencies contributing to the unsteady loads. Detailed comparisons between the computed unsteady surface pressure spectrum and the experimental data are shown along with a discussion of the various interaction mechanisms between the blade, transition duct, and downstream vane. These comparisons show overall good agreement between the simulation and experimental data and identify areas where further improvements in modeling are needed.

Publisher

ASMEDC

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