Particle Image Velocimetry (PIV) is a well-established technique for determining the flow direction and velocity magnitude of complex flows. This paper presents a methodology for executing this non-intrusive measurement technique to study a scaled-up turbine vane geometry within an annular cascade at engine-relevant conditions. Custom optical tools such as laser delivery probes and imaging inserts were manufactured to mitigate the difficult optical access of the test section and perform planar PIV. With the use of a burst-mode Nd: YAG laser and Photron FASTCAM camera, the frame straddling technique is implemented to enable short time intervals for the collection of image pairs and velocity fields at 10 kHz. Furthermore, custom image processing tools were developed to optimize the contrast and intensity balance of each image pair to maximize particle number and uniformity, while removing scattering and background noise. The pre-processing strategies significantly improve the vector yield under challenging alignment, seeding, and illumination conditions. With the optical and software tools developed, planar PIV was conducted in the passage of a high-pressure stator row, at mid-span, in an annular cascade. Different Mach and Reynolds number operating conditions were achieved by modifying the temperature and mass flow. With careful spatial calibration, the resultant velocity vector fields are compared with Reynolds Averaged Navier Stokes (RANS) simulations of the vane passage with the same geometry and flow conditions. Uncertainty analysis of the experimental results is also presented and discussed, along with prospects for further improvements.

Optical measurements based on fast response Pressure Sensitive Paint (PSP) provide enhanced spatial resolution of the pressure field. This paper presents laser lifetime PSP at 20 kHz, with precise calibrations, and results from a demonstration in an annular vane cascade. The laser lifetime PSP methodology is first evaluated in a linear wind tunnel with a converging-diverging nozzle followed by a wavy surface. This test section is fully optically accessible with maximum modularity. A data reduction procedure is proposed for the PSP calibration, and optimal pixel binning is selected to reduce the uncertainty. In the annular test section, laser lifetime PSP was used to measure the time-averaged static pressure field on a section of the suction surface of a high-pressure turbine vane. Tests were performed at engine representative conditions in the Purdue Big Rig for Annular Stationary Turbine Analysis module at the Purdue Experimental Turbine Aerothermal Lab. The 2-D pressure results showed a gradual increase of pressure in the spanwise and flow directions, corroborated with local static pressure taps and computational results. The variation in PSP thickness was measured as a contribution to the uncertainty. The discrete Fourier transform of the unsteady pressure signal showed increased frequency content in wind-on conditions compared to wind-off conditions at the mid-span and 30% span. Compared to the mid-span region, the hub end wall region had an increase in frequencies and pressure amplitude. This result was anticipated given the expected presence of secondary flow structures in the near hub region.

Particle Image Velocimetry (PIV) is a well-established technique for determining the flow direction and velocity magnitude of complex flows. This paper presents a methodology for executing this non-intrusive measurement technique to study a scaled-up turbine vane geometry within an annular cascade at engine-relevant conditions. Custom optical tools such as laser delivery probes and imaging inserts were manufactured to mitigate the difficult optical access of the test section and perform planar PIV. With the use of a burst-mode Nd: YAG laser and Photron FASTCAM camera, the frame straddling technique is implemented to enable short time intervals for the collection of image pairs and velocity fields at 10 kHz. Furthermore, custom image processing tools were developed to optimize the contrast and intensity balance of each image pair to maximize particle number and uniformity, while removing scattering and background noise. The pre-processing strategies significantly improve the vector yield under challenging alignment, seeding, and illumination conditions. With the optical and software tools developed, planar PIV was conducted in the passage of a high-pressure stator row, at mid-span, in an annular cascade. Different Mach and Reynolds number operating conditions were achieved by modifying the temperature and mass flow. With careful spatial calibration, the resultant velocity vector fields are compared with Reynolds Averaged Navier Stokes (RANS) simulations of the vane passage with the same geometry and flow conditions. Uncertainty analysis of the experimental results is also presented and discussed, along with prospects for further improvements.

The betterment of the turbine performance plays a prime role in all future transportation and energy production systems. Precise uncertainty quantification of experimental measurement of any performance differential is therefore essential for turbine development programs. In this paper, the uncertainty analysis of loss measurements in a high-pressure turbine vane are presented. Tests were performed on a stator geometry at engine representative conditions in a new annular turbine module called BRASTA (Big Rig for Annular Stationary Turbine Analysis) located within the Purdue Experimental Turbine Aerothermal Lab. The aerodynamic probes are described with emphasis on their calibration and uncertainty analysis, first considering single point measurement, followed by the spatial averaging implications. The change of operating conditions and flow blockage due to measurement probes are analyzed using CFD, and corrections are recommended on the measurement data. The test section and its characterization are presented, including calibration of the sonic valve. The sonic valve calibration is necessary to ensure a wide range of operation in Mach and Reynolds. Finally, the vane data are discussed, emphasizing their systematic and stochastic uncertainty.