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Research Papers: Gas Turbines: Turbomachinery

Multi-Objective Aerodynamic Optimization Design and Data Mining of a High Pressure Ratio Centrifugal Impeller

[+] Author and Article Information
Zhendong Guo

Institute of Turbomachinery,
Xi'an Jiaotong University,
No. 28 Xianning West Road,
Xi'an 710049, China
e-mail: ericzhendong@stu.xjtu.edu.cn

Liming Song

Associate Professor
Institute of Turbomachinery,
Xi'an Jiaotong University,
No. 28 Xianning West Road,
Xi'an 710049, China
e-mail: songlm@mail.xjtu.edu.cn

Zhiming Zhou

Institute of Turbomachinery,
Xi'an Jiaotong University,
No. 28 Xianning West Road,
Xi'an 710049, China
e-mail: zhimingzhou@stu.xjtu.edu.cn

Jun Li

Mem. ASME
Institute of Turbomachinery,
Xi'an Jiaotong University,
No. 28 Xianning West Road,
Xi'an 710049, China
e-mail: junli@mail.xjtu.edu.cn

Zhenping Feng

Mem. ASME
Institute of Turbomachinery,
Xi'an Jiaotong University,
No. 28 Xianning West Road,
Xi'an 710049, China
e-mail: zpfeng@mail.xjtu.edu.cn

1Corresponding author.

2Engaged in research in multi-objective and multidisciplinary design optimization for turbomachinery cascades.

Contributed by the Turbomachinery Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received January 5, 2015; final manuscript received February 5, 2015; published online March 17, 2015. Editor: David Wisler.

J. Eng. Gas Turbines Power 137(9), 092602 (Sep 01, 2015) (14 pages) Paper No: GTP-15-1004; doi: 10.1115/1.4029882 History: Received January 05, 2015; Revised February 05, 2015; Online March 17, 2015

An automated three-dimensional multi-objective optimization and data mining method is presented by integrating a self-adaptive multi-objective differential evolution algorithm (SMODE), 3D parameterization method for blade profile and meridional channel, Reynolds-averaged Navier–Stokes (RANS) solver technique and data mining technique of self-organizing map (SOM). Using this method, redesign of a high pressure ratio centrifugal impeller is conducted. After optimization, 16 optimal Pareto solutions are obtained. Detailed aerodynamic analysis indicates that the aerodynamic performance of the optimal Pareto solutions is greatly improved. By SOM-based data mining on optimized solutions, the interactions among objective functions and significant design variables are analyzed. The mechanism behind parameter interactions is also analyzed by comparing the data mining results with the performance of typical designs.

Copyright © 2015 by ASME
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References

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Figures

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Fig. 1

Validation of relative Mach number distribution at streamwise sections (a) location of streamwise sections, (b) section 1, and (c) section 2

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Fig. 2

Relative Mach number contours at pitch averaged meridian plane

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Fig. 3

Limiting streamlines of full and splitter blade suction surface (a) full blade and (b) splitter blade

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Fig. 4

Entropy distribution along streamwise sections

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Fig. 5

Flow chart of multi-objective design optimization and data mining method

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Fig. 6

Optimization results of typical test functions (a) OSY and (b) TNK

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Fig. 7

Parameterization of the blade (a) mean camber line and thickness distribution of section profile, (b) 2D blade section profile, (c) circumference folding, (d) 3D blade profile, and (e) 3D blade profile of impeller with splitters

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Fig. 8

Parameterization of meridional channel

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Fig. 9

Schematic map of SOM (a) 2D projection and (b) component map

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Fig. 10

SOM component maps with 2000 neutrons (a) x, (b) cos (10x), and (c) color coding

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Fig. 11

SOM-based scatterplot

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Fig. 12

Design variables for optimization process (a) meridional channel, (b) full blade, and (c) splitter blade

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Fig. 13

Optimal Pareto solutions

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Fig. 14

Relative Mach number at pitch-averaged plane (a) reference design, (b) Design A, (c) Design B, and (d) Design C

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Fig. 15

Entropy distributions at streamwise sections and limiting streamlines near the wall (a) reference design, (b) Design A, (c) Design B, and (d) Design C

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Fig. 16

Entropy contours near the splitter blade suction surface (a) reference design, (b) Design A, (c) Design B, and (d) Design C

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Fig. 17

Relative meridional velocity in streamwise sections (a) reference design, (b) Design A, (c) Design B, and (d) Design C

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Fig. 18

Absolute circumferential velocity near the exit (a) reference design, (b) Design A, (c) Design B, and (d) Design C

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Fig. 19

Overall performance along the span near the impeller exit (a) isentropic efficiency and (b) total pressure ratio

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Fig. 20

Off-design performance of the impellers (a) isentropic efficiency and (b) total pressure ratio

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Fig. 21

SOM component maps of objective functions: (a) ηis, (b) P2t/P1t, and (c) color coding

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Fig. 22

SOM-based scatterplots of significant design variables (a) x7 versus ηis, (b) x7 versus P2t/P1t, (c) x15 versus ηis, (d) x15 versus P2t/P1t, (e) x20 versus ηis, (f) x20 versus P2t/P1t, (g) x24 versus ηis, and (h) x24 versus P2t/P1t

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Fig. 23

Configurations of flow path (a) Design A, (b) Design B, and (c) Design C

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Fig. 24

Blade profiles of root sections (a) Design A, (b) Design B, and (c) Design C

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Fig. 25

Blade profiles of tip sections (a) Design A, (b) Design B, and (c) Design C

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Fig. 26

Value distribution of design variables of meridional flow path (a) x3, (b) x6, and (c) x8

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Fig. 27

Value distribution of design variables near the blade leading edge (a) x9, (b) x10, (c) x13, (d) x14, (e) x22, and (f) x23

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