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Research Papers

Multi-Objective Aerodynamic Optimization of Axial Turbine Blades Using a Novel Multilevel Genetic Algorithm

[+] Author and Article Information
Özhan Öksüz

Department of Aerospace Engineering, Middle East Technical University, Ankara, Turkey; TUSAŞ Engine Industry, Inc. (TEI), Eskisehir 26003, Turkeyozhan.oksuz@tei.com.tr

İbrahim Sinan Akmandor

Department of Aerospace Engineering, Middle East Technical University; Pars Makina Ltd., ODTU-OSTIM Teknokent, Ankara, Turkeysinan.akmandor@parsmakina.com

J. Turbomach 132(4), 041009 (May 04, 2010) (14 pages) doi:10.1115/1.3213558 History: Received March 25, 2009; Revised April 15, 2009; Published May 04, 2010; Online May 04, 2010

In this paper, a new multiploid genetic optimization method handling surrogate models of the CFD solutions is presented and applied for a multi-objective turbine blade aerodynamic optimization problem. A fast, efficient, robust, and automated design method is developed to aerodynamically optimize 3D gas turbine blades. The design objectives are selected as maximizing the adiabatic efficiency and torque so as to reduce the weight, size, and cost of the gas turbine engine. A 3D steady Reynolds averaged Navier–Stokes solver is coupled with an automated unstructured grid generation tool. The solver is verified using two well-known test cases. The blade geometry is modeled by 36 design variables plus the number of blade variables in a row. Fine and coarse grid solutions are respected as high- and low-fidelity models, respectively. One of the test cases is selected as the baseline and is modified by the design process. It was found that the multiploid multi-objective genetic algorithm successfully accelerates the optimization and prevents the convergence with local optimums.

Copyright © 2010 by American Society of Mechanical Engineers
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References

Figures

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Figure 5

VKI test case-coarse mesh

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Figure 6

VKI test case-fine mesh

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Figure 7

VKI test case planes of X/Cax=0.86 and X/Cax=1.11

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Figure 8

The comparison of low-/high-fidelity CP0 and CPs values with measurements at plane X/Cax=0.86

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Figure 1

(a) Stacking layers and (b) stacking layers on centroid

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Figure 2

Six layers in the meridional view

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Figure 3

Layer geometric parameterization

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Figure 4

Blade profile reshaping algorithm

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Figure 9

The comparison of low-/high-fidelity CP0 and CPs values with measurements at plane X/Cax=1.11

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Figure 10

The comparison of low-/high-fidelity β values with measurements at planes X/Cax=0.86 and X/Cax=1.1

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Figure 11

UH test case-coarse mesh of stator and rotor

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Figure 12

The comparison of the spanwise total pressure distributions at the downstream plane of the rotor (measurements are shorter)

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Figure 13

The comparison of the spanwise static pressure distributions at the downstream plane of the rotor

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Figure 14

Flowchart of the MOGAXL

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Figure 15

Multifidelity tournament selection

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Figure 16

Diploid MOGAXL full crossover illustration (for two children setting)

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Figure 17

Diploid MOGAXL level crossover illustration

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Figure 18

Pareto-optimal frontiers of the MOGA and the MOGAXL after 100 generations

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Figure 19

Number of elite members in the EMS-H

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Figure 20

Computational cost comparison

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Figure 21

Pareto-optimal frontiers after three generations of MOGA and nine generations of MOGAXL

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