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Research Papers

The Implications of Tolerance Optimization on Compressor Blade Design

[+] Author and Article Information
Eric A. Dow

Aerospace Computational Design Laboratory,
Department of Aeronautics and Astronautics,
Massachusetts Institute of Technology,
Cambridge, MA 02139
e-mail: ericdow@mit.edu

Qiqi Wang

Assistant Professor
Aerospace Computational Design Laboratory,
Department of Aeronautics and Astronautics,
Massachusetts Institute of Technology,
Cambridge, MA 02139
e-mail: qiqi@mit.edu

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received November 2, 2014; final manuscript received June 2, 2015; published online June 23, 2015. Assoc. Editor: Knox T. Millsaps.

J. Turbomach 137(10), 101008 (Oct 01, 2015) (7 pages) Paper No: TURBO-14-1285; doi: 10.1115/1.4030791 History: Received November 02, 2014; Revised June 02, 2015; Online June 23, 2015

Geometric variability increases performance variability and degrades the mean performance of turbomachinery compressor blades. These detrimental effects can be reduced by using robust optimization to design the blade geometry or by imposing stricter manufacturing tolerances. This paper presents a novel computational framework for optimizing compressor blade manufacturing tolerances and incorporates this framework into existing robust geometry design frameworks. Optimizations of an exit guide vane geometry are conducted. When the design is optimized to improve performance at a single operating point, the optimal geometry is found to depend on the manufacturing tolerances due to a switch in the dominant loss mechanism. Including multiple operating points in the optimization avoids this switch so that the geometry and tolerance optimization problems are decoupled.

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References

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Figures

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Fig. 1

Single-point optimal redesigned UTRC blades

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Fig. 2

Mach number distributions for the baseline and single-point optimized UTRC blades. The dots denote the location of transition.

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Fig. 3

Optimal standard deviation σ(s)/c for the single-point optimized UTRC blades. The lower surface is the pressure side, and the upper surface is the suction side. (a) Deterministic optimal tolerances, (b) robust optimal tolerances, and (c) simultaneous optimal tolerances.

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Fig. 4

Multipoint optimal redesigned UTRC blades

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Fig. 5

Loss buckets for the multipoint optimized UTRC blade: (a) uniform tolerances and (b) optimized tolerances

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Fig. 6

Comparison between the baseline and multipoint deterministic optimal Mach number distributions for the UTRC blade at three different incidence angles. The transition locations are indicated by dots. (a) α = −4.5 deg, (b) α = 0 deg, and (c) α = 4.5 deg.

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Fig. 7

Optimal standard deviation σ(s)/c for the multipoint optimized UTRC blades. The lower surface is the pressure side, and the upper surface is the suction side. (a) Deterministic optimal tolerances, (b) robust optimal tolerances, and (c) simultaneous optimal tolerances.

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