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Research Papers

Design and Optimization of the Internal Cooling Channels of a High Pressure Turbine Blade—Part II: Optimization

[+] Author and Article Information
Tom Verstraete

Department of Turbomachinery and Propulsion, von Kármán Institute for Fluid Dynamics, Waterloosesteenweg 72, 640 Sint-Genesius-Rode, Belgiumtom.verstraete@vki.ac.be

Sergio Amaral1

Department of Aerospace Engineering, Pennsylvania State University, 229 Hammond Building, University Park, PA 16802sergio.amaral@ge.com

René Van den Braembussche

Department of Turbomachinery and Propulsion, von Kármán Institute for Fluid Dynamics, Waterloosesteenweg 72, 640 Sint-Genesius-Rode, Belgiumvdb@vki.ac.be

Tony Arts

Department of Turbomachinery and Propulsion, von Kármán Institute for Fluid Dynamics, Waterloosesteenweg 72, 640 Sint-Genesius-Rode, Belgiumarts@vki.ac.be

1

Present address: GE Infra Energy, Greenville, SC.

J. Turbomach 132(2), 021014 (Jan 13, 2010) (9 pages) doi:10.1115/1.3104615 History: Received September 30, 2008; Revised November 19, 2008; Published January 13, 2010; Online January 13, 2010

This second paper presents the aerothermal optimization of the first stage rotor blade of an axial high pressure (HP) turbine by means of the conjugate heat transfer (CHT) method and lifetime model described in Paper I. The optimization system defines the position and diameter of the cooling channels leading to the maximum lifetime of the blade while limiting the amount of cooling flow. It is driven by the results of a CHT and subsequent stress analysis of each newly designed geometry. Both temperature and stress distributions are the input for the Larson–Miller material model to predict the lifetime of the blade. The optimization procedure makes use of a genetic algorithm (GA) and requires the aerothermal analysis of a large number of geometries. Because of the large computational cost of each CHT analysis, this results in a prohibitive computational effort. The latter has been remediated by using a more elaborate optimization system, in which a large part of the CHT analyzes is replaced by approximated predictions by means of a metamodel. Two metamodels, an artificial neural network and a radial basis function network, have been tested and their merits have been discussed. It is shown how this optimization procedure based on CHT calculations, a GA, and a metamodel can lead to a considerable extension of the blade lifetime without an increase in the amount of cooling flow or the complexity of the cooling geometry.

Copyright © 2010 by American Society of Mechanical Engineers
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References

Figures

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Figure 11

cc5 diameter versus lifetime and mass flow

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Figure 12

cc5 η versus lifetime and mass FLOW

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Figure 13

cc4 diameter versus lifetime and mass flow

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Figure 14

cc3 η versus lifetime and mass flow

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Figure 15

Comparison of ANN and RBF optimized geometries at hub and shroud

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Figure 1

Schematic overview of the optimization algorithm

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Figure 2

Parametrization of the location and diameter of the cooling channel

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Figure 3

Definition of the fourth cooling channel with ε=0

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Figure 4

Lifetime and coolant mass flow versus penalty

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Figure 5

Pareto front for a two objective optimization versus single-objective optimization

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Figure 6

Artificial neural network topology

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Figure 7

Radial basis function network topology

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Figure 16

Wall temperature of ANN iteration 18

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Figure 17

Effective stress at rupture of ANN iteration 18

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