A Statistical Approach In Turbine Heat Transfer
Harika S. Kahveci
Abstract: A high-quality extensive database is very critical to the gas turbine industry for improving the capabilities of the current state-of-the-art design of these machines so that more efficient cooled designs with extended turbine life can be accomplished. A series of experiments was performed at the OSU Gas Turbine Laboratory involving a rotating rig with a cooled 1-1/2 stage high-pressure transonic turbine operating at design corrected conditions with the goal of providing the turbine designer with such high-quality data. The turbine stage used is a modern 3-D design consisting of a cooled high-pressure vane, an un-cooled high-pressure rotor, and a low-pressure vane. The work investigates the influence of different vane inlet temperature profiles and cooling flow rates on heat transfer of the full-stage turbine. A novel application of a traditional statistical method is introduced to the analysis to assign confidence limits to measurements in the absence of repeat runs. This approach is later incorporated into a CFD validation effort for blade heat transfer predictions in order to quantify the overall predictive uncertainty due to the variation in the inlet temperature profile, gauge position, and surface roughness. Presented data analysis highlights important turbine flow regions that are highly complex and are still not well understood today.
Reminder: Tea and cookies will be in the seminar room before the seminar.