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A PROBABILISTIC APPROACH TO UNCERTAINTY IN TURBINE EFFICIENCY MEASUREMENT
Efficiency is an essential metric for assessing turbine performance. Modern turbines rely heavily on numerical computational fluid dynamic (CFD) tools for design improvement. With more compact turbines leading to lower aspect ratio airfoils, the influence of secondary flows is significant on performance. Secondary flows and detached flows, in general, remain a challenge for commercial CFD solvers; hence, there is a need for high fidelity experimental data to tune these solvers used by turbine designers. Efficiency measurements in engine-representative test rigs are challenging for multiple reasons; an inherent problem to any experiment is to remove the effects specific to the turbine rig. This problem is compounded by the narrow uncertainty band required, ideally less than 0.5% uncertainty, to detect the incremental improvements achieved by turbine designers. Efficiency measurements carried out in engine-representative turbine rigs have traditionally relied upon strong assumptions, such as neglecting heat transfer effects. Furthermore, prior to this research there was no framework to compute uncertainty propagation that combines both inputs from experiments and computational tools.
This dissertation presents a comprehensive methodology to obtain high-fidelity adiabatic efficiency data in engine-representative turbine facilities. This dissertation presents probabilistic sampling techniques to allow for uncertainty propagation. The effect of rig-specific effects such as heat transfer and gas properties, on efficiency is demonstrated. Sources of uncertainty are identified, and a framework is presented which divides the sources into bias and stochastic. The framework allows the combination of experimental and numerical uncertainty. The accuracy of temperature and aerodynamic pressure probes, used for efficiency determination, is quantified. Corrections for those effects are presented that rely on hybrid numerical and experimental methods. Uncertainty is propagated through these methods using numerical sampling.
Finally, two test cases are presented, a stator vane in an annular cascade and a two-stage turbine in a rotating rig. The performance is analyzed using the methods and corrections developed. The uncertainty on the measured efficiency is similar to literature but the uncertainty framework allows an uncertainty estimate on the adiabatic efficiency.