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Uncertainty in High-Pressure Stator Performance Measurement in an Annular Cascade at Engine-Representative Reynolds and Mach
Author(s) -
Lakshya Bhatnagar,
Guillermo Paniagua,
David G. Cuadrado,
Papa Aye N. Aye-Addo,
Antonio Castillo Sauca,
Francisco Lozano,
Matthew Bloxham
Publication year - 2021
Publication title -
journal of engineering for gas turbines and power
Language(s) - English
Resource type - Journals
eISSN - 1528-8919
pISSN - 0742-4795
DOI - 10.1115/1.4052385
Subject(s) - turbine , transonic , aerodynamics , stator , mach number , calibration , reynolds number , measurement uncertainty , computational fluid dynamics , computer science , aerospace engineering , mechanical engineering , mechanics , marine engineering , physics , engineering , turbulence , quantum mechanics
The betterment of 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 is 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 computational fluid dynamics, 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.

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