Experimental investigation of total pressure loss development in a highly loaded low-pressure turbine cascade

Improvements in turbine design methods have resulted in the development of blade profiles with both high lift and good Reynolds lapse characteristics. An increase in aerodynamic loading of blades in the low-pressure turbine (LPT) section of aircraft gas turbine engines has the potential to reduce engine weight or increase power extraction. Increased blade loading means larger pressure gradients and increased secondary losses near the endwall. Prior work has emphasized the importance of reducing these losses if highly loaded blades are to be utilized. The present study analyzes the secondary flow field of the front-loaded low-pressure turbine blade designated L2F with and without blade profile contouring at the junction of the blade and endwall. The current work explores the loss production mechanisms inside the LPT cascade. Stereoscopic particle image velocimetry (SPIV) data and total pressure loss data are used to describe the secondary flow field. The flow is analyzed in terms of total pressure loss, vorticity, Q-Criterion, turbulent kinetic energy, and turbulence production. The flow description is then expanded upon using an implicit large eddy simulation (ILES) of the flow field. The Reynolds-averaged Navier-Stokes (RANS) momentum equations contain terms with pressure derivatives. With some manipulation, these equations can be rearranged to form an equation for the change in total pressure along a streamline as a function of velocity only. After simplifying for the flow field in question, the equation can be interpreted as the total pressure transport along a streamline. A comparison of the total pressure transport calculated from the velocity components and the total pressure loss is presented and discussed. Peak values of total pressure transport overlap peak values of total pressure loss through and downstream of the passage suggesting that the total pressure transport is a useful tool for localizing and predicting loss origins and loss development using velocity data which can be obtained nonintrusively.