A low pressure turbine blade was designed to produce a 17% increase in blade loading over an industry-standard airfoil using integrated flow control to prevent separation. The design was accomplished using two-dimensional CFD predictions of blade performance coupled with insight gleaned from recently published work in transition modeling and from previous experiments with flow control using vortex generator jets (VGJs). In order to mitigate the Reynolds number lapse in efficiency associated with LPT airfoils, a mid-loaded blade was selected. Also, separation predictions from the computations were used to guide the placement of control actuators on the blade suction surface. Three blades were fabricated using the new design and installed in a two-passage linear cascade facility. Flow velocity and surface pressure measurements taken without activating the VGJs indicate a large separation bubble centered at 68% axial chord on the suction surface. The size of the separation and its growth with decreasing Reynolds number agree well with CFD predictions. The separation bubble reattaches to the blade over a wide range of inlet Reynolds numbers from 150,000 down to below 20,000. This represents a marked improvement in separation resistance compared to the original blade profile which separates without reattachment below a Reynolds number of 40,000. This enhanced performance is achieved by increasing the blade spacing while simultaneously adjusting the blade shape to make it less aft-loaded but with a higher peak cp. This reduces the severity of the adverse pressure gradient in the uncovered portion of the modified blade passage. With the use of pulsed VGJs, the design blade loading was achieved while providing attached flow over the entire range of Re. Detailed phase-locked flow measurements using three-component PIV show the trajectory of the jet and its interaction with the unsteady separation bubble. Results illustrate the importance of integrating flow control into the turbine blade design process and the potential for enhanced turbine performance.
Studies of dislocation density evolution are fundamental to improved understanding in various areas of deformation mechanics. Recent advances in cross-correlation techniques, applied to electron backscatter diffraction (EBSD) data have particularly shed light on geometrically necessary dislocation (GND) behavior. However, the framework is relatively computationally expensive-patterns are typically saved from the EBSD scan and analyzed offline. A better understanding of the impact of EBSD pattern degradation, such as binning, compression, and various forms of noise, is vital to enable optimization of rapid and low-cost GND analysis. This paper tackles the problem by setting up a set of simulated patterns that mimic real patterns corresponding to a known GND field. The patterns are subsequently degraded in terms of resolution and noise, and the GND densities calculated from the degraded patterns using cross-correlation ESBD are compared with the known values. Some confirmation of validity of the computational degradation of patterns by considering real pattern degradation is also undertaken. The results demonstrate that the EBSD technique is not particularly sensitive to lower levels of binning and image compression, but the precision is sensitive to Poisson-type noise. Some insight is also gained concerning effects of mixed patterns at a grain boundary on measured GND content.
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