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	Data-driven rapid prediction model for aerodynamic force of high-speed train with arbitrary streamlined head
	Chen, Dawei2; Sun ZX(孙振旭)1; Yao, Shuanbao2; 许盛峰Xu, Shengfeng1; Yin B(银波)1; Guo DL(郭迪龙)1; Yang GW(杨国伟)1; Ding, Sansan2
Source	ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
	2022-12-31
Volume	16Issue:1Pages:2190-2205
Keyword	Aerodynamic force inverse design high-speed train SVM numerical simulation wind tunnel test
ISSN	1994-2060
DOI	10.1080/19942060.2022.2136758
Corresponding Author	Sun, Zhenxu(sunzhenxu@imech.ac.cn)
English Abstract	Due to the complicated geometric shape, it's difficult to precisely obtain the aerodynamic force of high-speed trains. Taking numerical and experimental data as the training data, the present work proposed a data-driven rapid prediction model to solve this problem, which utilized the Support Vector Machine (SVM) model to construct a nonlinear implicit mapping between design variables and aerodynamic forces of high-speed train. Within this framework, it is a key issue to achieve the consistency and auto-extraction of design variables for any given streamlined shape. A general parameterization method for the streamlined shape which adopted the idea of step-by-step modeling has been proposed. Taking aerodynamic drag as the prediction objective, the effectiveness of the model was verified. The results show that the proposed model can be successfully used for performance evaluation of high-speed trains. Keeping a comparable prediction accuracy with numerical simulations, the efficiency of the rapid prediction model can be improved by more than 90%. With the enrichment of data for the training set, the prediction accuracy of the rapid prediction model can be continuously improved. Current study provides a new approach for aerodynamic evaluation of high-speed trains and can be beneficial to corresponding engineering design departments.
Classification	一类
Funding Project	Youth Innovation Promotion Association CAS[2019020]
WOS Research Area	Engineering ; Mechanics
Language	英语
WOS ID	WOS:000884571500001
Funding Organization	Youth Innovation Promotion Association CAS
Other responsible	Sun, Zhenxu
Content Type	期刊论文
源URL	[http://dspace.imech.ac.cn/handle/311007/90783]
Collection	力学研究所_流固耦合系统力学重点实验室(2012-)
Affiliation	1.Chinese Acad Sci, Inst Mech, Key Lab Mech Fluid Solid Coupling Syst, Beijing, Peoples R China 2.CRRC Qingdao Sifang Co Ltd, Qingdao, Peoples R China;
Recommended Citation GB/T 7714	Chen, Dawei,Sun ZX,Yao, Shuanbao,et al. Data-driven rapid prediction model for aerodynamic force of high-speed train with arbitrary streamlined head[J]. ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS,2022,16(1):2190-2205.
APA	Chen, Dawei.,孙振旭.,Yao, Shuanbao.,许盛峰Xu, Shengfeng.,银波.,...&Ding, Sansan.(2022).Data-driven rapid prediction model for aerodynamic force of high-speed train with arbitrary streamlined head.ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS,16(1),2190-2205.
MLA	Chen, Dawei,et al."Data-driven rapid prediction model for aerodynamic force of high-speed train with arbitrary streamlined head".ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS 16.1(2022):2190-2205.

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