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Research Papers

The Effect of Manufacturing Variations on Unsteady Interaction in a Transonic Turbine

[+] Author and Article Information
John P. Clark

Mem. ASME
Turbomachinery Branch,
Turbine Engine Division,
Aerospace Systems Directorate,
Air Force Research Laboratory,
1864 4th Street,
Wright-Patterson AFB, OH 45433
e-mail: john.clark.38@us.af.mil

Joseph A. Beck

Mem. ASME
AFRL/RXMS,
Wright-Patterson AFB, OH 45433

Alex A. Kaszynski, Angela Still

Mem. ASME
Universal Technology Co.,
Dayton, OH 54532

Ron-Ho Ni

Mem. ASME
AeroDynamic Solutions, Inc.,
Danville, CA 94526

1Corresponding author.

Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received October 23, 2017; final manuscript received November 27, 2017; published online April 30, 2018. Editor: Kenneth Hall. This work is in part a work of the U.S. Government. ASME disclaims all interest in the U.S. Government's contributions.

J. Turbomach 140(6), 061007 (Apr 30, 2018) (9 pages) Paper No: TURBO-17-1197; doi: 10.1115/1.4039361 History: Received October 23, 2017; Revised November 27, 2017

This effort focuses on the comparison of unsteadiness due to as-measured turbine blades in a transonic turbine to that obtained with blueprint geometries via computational fluid dynamics (CFD). A Reynolds-averaged Navier–Stokes flow solver with the two-equation Wilcox turbulence model is used as the numerical analysis tool for comparison between the blueprint geometries and as-manufactured geometries obtained from a structured light optical measurement system. The nominal turbine CFD grid data defined for analysis of the blueprint blade were geometrically modified to reflect as-manufactured turbine blades using an established mesh metamorphosis algorithm. The approach uses a modified neural network to iteratively update the source mesh to the target mesh. In this case, the source is the interior CFD surface grid while the target is the surface blade geometry obtained from the optical scanner. Nodes interior to the CFD surface were updated using a modified iterative spring analogy to avoid grid corruption when matching as-manufactured part geometry. This approach avoids the tedious manual approach of regenerating the CFD grid and does not rely on geometry obtained from coordinate measurement machine (CMM) sections, but rather a point cloud representing the entirety of the turbine blade. Surface pressure traces and the discrete Fourier transforms (DFT) thereof from numerical predictions of as-measured geometries are then compared both to blueprint predictions and to experimental measurements. The importance of incorporating as-measured geometries in analyses to explain deviations between numerical predictions of blueprint geometries and experimental results is readily apparent. Further analysis of every casting produced in the creation of the test turbine yields variations that one can expect in both aero-performance and unsteady loading as a consequence of manufacturing tolerances. Finally, the use of measured airfoil geometries to reduce the unsteady load on a target blade in a region of interest is successfully demonstrated.

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Figures

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Fig. 5

The standard deviation of surface distance variation between the 105 measured blades and the blueprint geometry. Suction- and pressure-side views are on the left and right, respectively.

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Fig. 4

The mean of surface distance variation for the 105 measured blades with respect to the blueprint geometry. Suction- and pressure-side views are on the left and right, respectively.

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Fig. 3

Quantitative 3D CFD discrepancies based on nominal and as-measured geometries (pressure side)

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Fig. 2

Quantitative 3D CFD discrepancies based on nominal and as-measured geometries (suction side)

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Fig. 1

CFD surface (red) overlaid onto nominal finite element model mesh (white)

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Fig. 6

Two-dimensional cross section for the population of airfoils at 50% span (red) versus that of nominal design (dash blue)

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Fig. 7

Histogram of as-measured trailing-edge diameter at the nominal 50% span

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Fig. 8

Histogram of as-measured trailing-edge metal angle at the nominal 50% span

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Fig. 9

Histogram of as-measured trailing-edge wedge angle at the nominal 50% span

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Fig. 10

The three airfoil rows in the transonic turbine of this study with color scale indicative of instantaneous static pressure

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Fig. 11

The standard deviation of unsteady surface pressure magnitude (left) as a percent of Ptin at 46E and the phase angle in degrees (right). Also plotted are the locations of sensors on the blade suction side downstream of the throat.

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Fig. 12

Unsteady pressures (top), DFT magnitudes (middle), and a spectrogram for experimental data from sensor #7 on Fig.11

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Fig. 13

Mean and peak-to-peak variation in calculated DFT magnitude at 46E for all sensor locations in Fig. 11. N.B. All simulation results are represented and compared to experimental data.

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Fig. 14

DFT magnitude at 46E as a percent of Ptin on blade 20 for a full-wheel simulation (left) and a 2:4:2 model containing only measured blades 20 through 23 (right)

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Fig. 15

The DFT magnitude at 46E as a percent of Ptin on blade 20 for a full-wheel simulation (left) and a 2:4:2 model containing only measured blade 20 and three measured blades selected to reduce unsteadiness at sensor 7 (right)

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Fig. 16

Unsteady pressure signals and wavelet scalograms for a kulite sensor on the downstream vane pressure side at 52% span and 20% axial chord. Results of simulations with blueprint- and as-measured blades are shown in the top and bottom scalograms, respectively. Ensemble-averaged experimental data is shown in the center scalogram.

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