Using Turbulence Intensity and Reynolds Number to Predict Flow Separation on a Highly Loaded, Low-Pressure Gas Turbine Blade at Low Reynolds Numbers

Abstract

The low-pressure turbine has become more important in the last few decades because of the increased emphasis on higher overall pressure and bypass ratios. The desire is to increase blade loading to reduce blade counts and stages in the low-pressure turbine of a gas turbine engine. Increased turbine inlet temperatures for newer cycles results in higher temperatures in the low-pressure turbine, especially the latter stages, where cooling technologies are not used. These higher temperatures lead to higher work from the turbine and this, combined with the high loadings, can lead to flow separation. Separation is more likely in engines operating at high altitudes and reduced throttle setting. At the high Reynolds numbers found at takeoff, the flow over a low-pressure turbine blade tends to stay attached. At lower blade Reynolds numbers (25,000 to 200,000), found during cruise at high altitudes, the flow on the suction surface of the low-pressure turbine blades is inclined to separate. This paper is a study on the flow characteristics of the L1A turbine blade at three low Reynolds numbers (60,000, 108,000, and 165,000) and 15 turbulence intensities (1.89% to 19.87%) in a steady flow cascade wind tunnel. With this data, it is possible to examine the impact of Reynolds number and turbulence intensity on the location of the initiation of flow separation, the flow separation zone, and the reattachment location. Quantifying the change in separated flow as a result of varying Reynolds numbers and turbulence intensities will help to characterize the low momentum flow environments in which the low-pressure turbine must operate and how this might impact the operation of the engine. Based on the data presented, it is possible to predict the location and size of the separation as a function of both the Reynolds number and upstream freestream turbulence intensity (FSTI). Being able to predict this flow behavior can lead to more effective blade designs using either passive or active flow control to reduce or eliminate flow separation.