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TECHNICAL PAPERS

The Impact of Blade-to-Blade Flow Variability on Turbine Blade Cooling Performance

[+] Author and Article Information
Vince Sidwell

Multidisciplinary Design and Optimization Group,  Pratt & Whitney, 400 Main Street, M∕S 165-16, East Hartford, CT 06109

David Darmofal

Aeronautics and Astronautics,  Massachusetts Institute of Technology, 77 Massachusetts Avenue, Room 37-401, Cambridge, MA 02139

J. Turbomach 127(4), 763-770 (May 17, 2005) (8 pages) doi:10.1115/1.2019247 History: Received August 13, 2004; Revised May 17, 2005

The focus of this paper is the impact of manufacturing variability on turbine blade cooling flow and, subsequently, its impact on oxidation life. A simplified flow network model of the cooling air supply system and a row of blades is proposed. Using this simplified model, the controlling parameters which affect the distribution of cooling flow in a blade row are identified. Small changes in the blade flow tolerances (prior to assembly of the blades into a row) are shown to have a significant impact on the minimum flow observed in a row of blades resulting in substantial increases in the life of a blade row. A selective assembly method is described in which blades are classified into a low-flow and a high-flow group based on passage flow capability (effective areas) in life-limiting regions and assembled into rows from within the groups. Since assembling rows from only high-flow blades is equivalent to raising the low-flow tolerance limit, high-flow blade rows will have the same improvements in minimum flow and life that would result from more stringent tolerances. Furthermore, low-flow blade rows are shown to have minimum blade flows which are the same or somewhat better than a low-flow blade that is isolated in a row of otherwise higher-flowing blades. As a result, low-flow blade rows are shown to have lives that are no worse than random assembly from the full population. Using a higher fidelity model for the auxiliary air system of an existing jet engine, the impact of selective assembly on minimum blade flow and life of a row is estimated and shown to be in qualitative and quantitative agreement with the simplified model analysis.

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Copyright © 2005 by American Society of Mechanical Engineers
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Figures

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Figure 1

Cross section of the first-stage of an existing high-pressure turbine and the aft portion of the combustor, including the schematic representation of the turbine cooling air delivery portion of a higher-fidelity auxiliary air system model. Three plenums are highlighted that correspond to plenums in the simplified model.

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Figure 2

Simplified model of a turbine cooling air supply system and a row of n single-passage cooled turbine blades. Asfs(Ps,Pp) represents the flow through the cooling air supply system and Aifb(Pp,Pg) represents the flow through each cooled blade.

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Figure 3

Schematic of a simplified turbine cooling air supply system where each blade consists of three passages (a, b, and c)

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Figure 4

Probability density function representing the passage a cooling flow effective area and defining low-flow and high-flow populations. Note: for the example given, blades are classified only according to their passage a flows.

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Figure 5

Histograms showing passage a cooling flow for all blades in each row (top) and minimum passage a cooling flow in each row (bottom) for blade rows assembled from the full population, the low-flow population, and the high-flow population. Values are normalized by the mean passage a flow of all blades in the full population.

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Figure 6

Histograms showing the minimum passage a cooling flow in each row for rows assembled from the full, high-flow, and low-flow populations. Values are normalized to the mean value of the minimum passage a flow through the full population. Note: the data here is the same shown in the lower half of Fig. 5 though scaled differently to increase clarity.

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Figure 7

Fifth percentile ṁ̃amin for both the low-flow and high-flow classes of blades as a function of the tolerance value [(Ãtol∕Anom)a]. Also included is the corresponding percent of low-flow blades.

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Figure 8

Fifth percentile ṁ̃amin for both the low-flow and high-flow classes of blades as a function of β for (Ãtol∕Anom)a=−0.04

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Figure 9

Histograms showing the minimum leading-edge passage flow for rows assembled from the full, high-flow, and low-flow populations for an existing commercial jet engine. Values are normalized to the mean value of the minimum leading-edge passage flow through the full population blade rows.

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Figure 10

Histograms showing the blade row oxidation life for rows assembled from the full, high-flow, and low-flow populations for an existing commercial jet engine. Values are normalized to the mean value of the row life for the full population rows.

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Figure 11

Histograms showing the total cooling flow (for all passages) delivered to rows assembled from the full, high-flow, and low-flow populations for an existing commercial jet engine. Values are normalized to the mean value of the total flow for the full population blade rows.

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