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Research Papers

Genetic Algorithm Optimization of a High-Pressure Turbine Vane Pressure Side Film Cooling Array

[+] Author and Article Information
J. J. Johnson

e-mail: jamie.johnson.1@us.af.mil

P. I. King

e-mail: paul.king@afit.edu
Department of Aeronautics and Astronautics,
Air Force Institute of Technology,
2950 Hobson Way,
WPAFB, OH 45433

J. P. Clark

e-mail: john.clark3@wpafb.af.mil

M. K. Ooten

e-mail: michael.ooten@wpafb.af.mil
Turbine Branch,
Turbine Engine Division,
Propulsion Directorate,
Air Force Research Laboratory,
1950 5th St., Bldg. 18, Rm. D136,
WPAFB, OH 45433

Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received November 18, 2012; final manuscript received January 1, 2013; published online September 23, 2013. Editor: David Wisler.

J. Turbomach 136(1), 011011 (Sep 23, 2013) (11 pages) Paper No: TURBO-12-1222; doi: 10.1115/1.4023470 History: Received November 18, 2012; Revised January 01, 2013

The following work is an in-depth investigation of the heat transfer characteristics and cooling effectiveness of a full-scale fully cooled modern high-pressure turbine (HPT) vane as a result of genetic algorithm (GA) optimization, relative to a modern baseline film cooling configuration. Individual designs were evaluated using 3D Reynolds-averaged Navier–Stokes (RANS) computational fluid dynamics (CFD) that modeled film cooling injection using a transpiration boundary condition and evaluated 10 cells from the wall with an isothermal surface condition. 1800 different cooling arrays were assessed for fitness within the optimization where film cooling parameters such as axial and radial hole location, hole size, injection angle, compound angle, and custom-designed row patterns were varied in the design space. The GA optimization terminated with a unique pressure side (PS) cooling array after only 13 generations. The fitness functions prescribed for the problem lowered the PS average near-wall surface temperature, lowered the near-wall maximum temperature, and maintained the level of near-wall average overall effectiveness. Results show how the optimization resulted in redistributed flow from overcooled areas on the vane PS to undercooled areas near the shroud. The optimized cooling array yielded a reduction of average near-wall gas temperature of 2 K, a reduction in the maximum near-wall gas temperature of 3 K, a reduction in maximum heat flux of 2 kW/m2 and a reduction in pressure loss over the vane, all while maintaining a constant level of surface-averaged overall effectiveness. Methods used to improve pressure side film cooling performance here are promising in terms of eliminating hot spots on individual HPT components in their proper operating environments as well as increasing the potential to use less air for cooling purposes in a gas turbine engine.

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References

Figures

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Fig. 1

3D rendering of the HIT RTV showing the baseline pressure side film cooling array

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Fig. 2

GA optimization flow chart

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Fig. 3

Justification for use of j = 10 grid location for CFD assessments in the optimization

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Fig. 4

Source-term cooling flux injection estimation at surface grid cells

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Fig. 5

Comparison of baseline and optimized PS cooling arrays and relative injection properties (flow is left to right)

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Fig. 6

Streamwise T,nw comparison between optimized and baseline PS cooling arrays at 10% span

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Fig. 7

Streamwise T,nw comparison between optimized and baseline PS cooling arrays at 30% span

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Fig. 8

Streamwise T,nw comparison between optimized and baseline PS cooling arrays at 50% span

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Fig. 9

Streamwise T,nw comparison between optimized and baseline PS cooling arrays at 70% span

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Fig. 10

Streamwise T,nw comparison between optimized and baseline PS cooling arrays at 90% span

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Fig. 11

Streamwise T,nw comparison between optimized and baseline PS cooling arrays at 95% span

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Fig. 12

PS contour of T,nw,optimizedT,nw,baseline (K). Flow goes from right to left.

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Fig. 13

Streamwise near-wall heat flux comparison between optimized and baseline PS cooling arrays at 10% span

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Fig. 14

Streamwise near-wall heat flux comparison between optimized and baseline PS cooling arrays at 30% span

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Fig. 15

Streamwise near-wall heat flux comparison between optimized and baseline PS cooling arrays at 50% span

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Fig. 16

Streamwise near-wall heat flux comparison between optimized and baseline PS cooling arrays at 70% span

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Fig. 17

Streamwise near-wall heat flux comparison between optimized and baseline PS cooling arrays at 90% span

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Fig. 18

Streamwise near-wall heat flux comparison between optimized and baseline PS cooling arrays at 95% span

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Fig. 19

PS contour of qnw,optimized" – qnw,baseline" (W/m2)

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Fig. 20

Streamwise φnw comparison between optimized and baseline PS cooling arrays at 10% span

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Fig. 21

Streamwise φnw comparison between optimized and baseline PS cooling arrays at 50% span

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Fig. 22

Streamwise φnw comparison between optimized and baseline PS cooling arrays at 95% span

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Fig. 23

PS contour of percent change in overall effectiveness between the optimized and baseline cooling arrays

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