Aerodynamic design of transonic fan/compressor by 3D viscous RNS combined with genetic algorithms

Bin Jiang; Song Tao Wang; Guo Tai Feng; Zhong Qi Wang

Aerodynamic design of transonic fan/compressor by 3D viscous RNS combined with genetic algorithms

Bin Jiang^*
, Song Tao Wang
, Guo Tai Feng
, Zhong Qi Wang

^*Corresponding author for this work

School of Energy Science and Engineering, Harbin Institute of Technology

Research output: Contribution to journal › Article › peer-review

Abstract

This paper presents an aerodynamic design of a small transonic fan by 3D viscous RNS solver combined with genetic algorithms. The aerodynamic design system based on the 3D viscous RNS solver reduces the dependency on the design experience for designers. Furthermore the optimum with genetic algorithms is an effective method for improving the transonic fan performance as a part of the design system. The design result showed that the transonic fan designed by this method reaches the design requirement even with more efficiency value.

Original language	English
Pages (from-to)	143-148
Number of pages	6
Journal	Journal of Harbin Institute of Technology (New Series)
Volume	18
Issue number	2
State	Published - Apr 2011
Externally published	Yes

Keywords

Aerodynamic design
Genetic algorithm
Shock structure
Transonic fan

Cite this

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title = "Aerodynamic design of transonic fan/compressor by 3D viscous RNS combined with genetic algorithms",

abstract = "This paper presents an aerodynamic design of a small transonic fan by 3D viscous RNS solver combined with genetic algorithms. The aerodynamic design system based on the 3D viscous RNS solver reduces the dependency on the design experience for designers. Furthermore the optimum with genetic algorithms is an effective method for improving the transonic fan performance as a part of the design system. The design result showed that the transonic fan designed by this method reaches the design requirement even with more efficiency value.",

keywords = "Aerodynamic design, Genetic algorithm, Shock structure, Transonic fan",

author = "Bin Jiang and Wang, \{Song Tao\} and Feng, \{Guo Tai\} and Wang, \{Zhong Qi\}",

year = "2011",

month = apr,

language = "英语",

volume = "18",

pages = "143--148",

journal = "Journal of Harbin Institute of Technology (New Series)",

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T1 - Aerodynamic design of transonic fan/compressor by 3D viscous RNS combined with genetic algorithms

AU - Jiang, Bin

AU - Wang, Song Tao

AU - Feng, Guo Tai

AU - Wang, Zhong Qi

PY - 2011/4

Y1 - 2011/4

N2 - This paper presents an aerodynamic design of a small transonic fan by 3D viscous RNS solver combined with genetic algorithms. The aerodynamic design system based on the 3D viscous RNS solver reduces the dependency on the design experience for designers. Furthermore the optimum with genetic algorithms is an effective method for improving the transonic fan performance as a part of the design system. The design result showed that the transonic fan designed by this method reaches the design requirement even with more efficiency value.

AB - This paper presents an aerodynamic design of a small transonic fan by 3D viscous RNS solver combined with genetic algorithms. The aerodynamic design system based on the 3D viscous RNS solver reduces the dependency on the design experience for designers. Furthermore the optimum with genetic algorithms is an effective method for improving the transonic fan performance as a part of the design system. The design result showed that the transonic fan designed by this method reaches the design requirement even with more efficiency value.

KW - Aerodynamic design

KW - Genetic algorithm

KW - Shock structure

KW - Transonic fan

UR - https://www.scopus.com/pages/publications/79959694200

M3 - 文章

AN - SCOPUS:79959694200

SN - 1005-9113

VL - 18

SP - 143

EP - 148

JO - Journal of Harbin Institute of Technology (New Series)

JF - Journal of Harbin Institute of Technology (New Series)

IS - 2

ER -

Aerodynamic design of transonic fan/compressor by 3D viscous RNS combined with genetic algorithms

Abstract

Keywords

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