Two effective methods for flow-field performance forecast

Bao Guo Wang; Jun Hong Wu; Shu Yan Liu; Geng Qian; Yan Ming Liu

Two effective methods for flow-field performance forecast

Bao Guo Wang^*
, Jun Hong Wu
, Shu Yan Liu
, Geng Qian
, Yan Ming Liu

^*此作品的通讯作者

空天科学与技术学院

Beijing Institute of Technology

科研成果: 期刊稿件 › 文章 › 同行评审

2 引用（Scopus）

摘要

Based on database established by multi-factor and multi-level design methods, two effective methods of flow-field performance forecast were proposed. One is response surface method, and the other is wavelet neural networks method. The typical examples indicate that two methods can forecast flow-field performance effectively. The results also show that wavelet neural networks method is superior to response surface method in terms of efficiency and precision.

源语言	英语
页（从-至）	1763-1767
页数	5
期刊	Hangkong Dongli Xuebao/Journal of Aerospace Power
卷	25
期	8
出版状态	已出版 - 8月 2010

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引用此

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title = "Two effective methods for flow-field performance forecast",

abstract = "Based on database established by multi-factor and multi-level design methods, two effective methods of flow-field performance forecast were proposed. One is response surface method, and the other is wavelet neural networks method. The typical examples indicate that two methods can forecast flow-field performance effectively. The results also show that wavelet neural networks method is superior to response surface method in terms of efficiency and precision.",

keywords = "Flow-field forecast, Flow-field performance, Response surface method, Uniform experimental design, Wavelet neural networks",

author = "Wang, \{Bao Guo\} and Wu, \{Jun Hong\} and Liu, \{Shu Yan\} and Geng Qian and Liu, \{Yan Ming\}",

year = "2010",

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AU - Wang, Bao Guo

AU - Wu, Jun Hong

AU - Liu, Shu Yan

AU - Qian, Geng

AU - Liu, Yan Ming

PY - 2010/8

Y1 - 2010/8

N2 - Based on database established by multi-factor and multi-level design methods, two effective methods of flow-field performance forecast were proposed. One is response surface method, and the other is wavelet neural networks method. The typical examples indicate that two methods can forecast flow-field performance effectively. The results also show that wavelet neural networks method is superior to response surface method in terms of efficiency and precision.

AB - Based on database established by multi-factor and multi-level design methods, two effective methods of flow-field performance forecast were proposed. One is response surface method, and the other is wavelet neural networks method. The typical examples indicate that two methods can forecast flow-field performance effectively. The results also show that wavelet neural networks method is superior to response surface method in terms of efficiency and precision.

KW - Flow-field forecast

KW - Flow-field performance

KW - Response surface method

KW - Uniform experimental design

KW - Wavelet neural networks

UR - https://www.scopus.com/pages/publications/77956300917

M3 - Article

AN - SCOPUS:77956300917

SN - 1000-8055

VL - 25

SP - 1763

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JO - Hangkong Dongli Xuebao/Journal of Aerospace Power

JF - Hangkong Dongli Xuebao/Journal of Aerospace Power

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ER -

Two effective methods for flow-field performance forecast

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