Three-dimensional aerodynamic optimization design of high-speed train nose based on GA-GRNN

Yao, Shuanbao; Guo, Dilong; Yang, Guowei

doi:10.1007/s11431-012-4934-2

Cited by 40 publications

(18 citation statements)

References 12 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thus, grid-independent validation is firstly performed in the present paper with different amounts of spatial mesh that combine prism mesh near the wall and hexahedral mesh so as to assess the influence of different spatial mesh on the calculation results. With the thickness of the first prism layer meeting the requirement of the wall function (30 ≤ y + ≤ 50) that is valid to simulation the flow around high-speed trains Yao, Guo, & Yang, 2012), three sets of mesh are obtained in this paper by changing the number of grid layers in the boundary layer, increasing the mesh size and region. As can be seen in Figure 10, the value of y + of the body surface is mainly in the range from 30 to 50.…”

Section: Mesh Independence Validationmentioning

confidence: 99%

“…In order to obtain a streamlined design with excellent aerodynamic performance and improve the optimization efficiency of a train head's aerodynamic shape, many scholars have done a lot of work (Kwon et al, 2001;Lee & Kim, 2008;Sun, Song, & An, 2010;Vytla, Huang, & Penmetsa, 2010;Yao, Guo, & Yang, 2012). Lee and Kim (2008) developed an optimization algorithm that combines successive quadratic programming (SQP) optimization with a support vector machine in order to reduce micro-pressure.…”

mentioning

confidence: 99%

See 1 more Smart Citation

A modified multi-objective sorting particle swarm optimization and its application to the design of the nose shape of a high-speed train

Yao¹,

Guo

Sun

et al. 2015

Engineering Applications of Computational Fluid Mechanics

Self Cite

View full text Add to dashboard Cite

Based on the concepts of niche count and crowding distance, a modified multi-objective particle swarm optimization (MPSO) is introduced. The niche count and crowding distance are used to determine the globally best particle across four test cases using an external file. A comparative analysis was carried out between MPSO and non-dominated sorting multiobjective adaptive genetic algorithms, both real-coded and binary-coded. The results show that MPSO based on the crowding distance is best for getting the Pareto front, especially for problems with high-dimensional and non-continuous Pareto fronts. In order to verify the efficiency of MPSO in solving engineering problems, the optimal design of the aerodynamic nose shape of high-speed trains was undertaken using a modified vehicle modeling function (MVMF) parametric method. Taking the aerodynamic drag of the whole train (Cd) and the aerodynamic lift of the trailer car (Cl) as the optimization goals, the Kriging surrogate model was introduced to reduce the computational time, and the MPSO based on crowding distance was used to find the Pareto front. The optimization results show that MPSO is efficient at getting the Pareto front; compared to the original shape, the Cd and Cl of the optimal shape are reduced by 1.6% and 29.74%, respectively.

show abstract

Section: Mesh Independence Validationmentioning

confidence: 99%

mentioning

confidence: 99%

A modified multi-objective sorting particle swarm optimization and its application to the design of the nose shape of a high-speed train

Yao¹,

Guo

Sun

et al. 2015

Engineering Applications of Computational Fluid Mechanics

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this paper, the simulation condition is open air, so we employ incompressible unsteady flow to describe the flow field and adopt standard k - turbulence model to describe the flow field, relevant control equations are defined as follows [21]:…”

Section: Air Trajectories Analysis Of the Flow Fieldmentioning

confidence: 99%

Digital Design of Streamlined EMU Head Car and Its Smoothness Evaluation

Zhenfeng¹,

Chen²,

Ding³

2015

IJHIT

View full text Add to dashboard Cite

show abstract

“…Ikeda et al (2006) and Suzuki et al (2008) used B-spline curve to set up a parametric model of cross-sectional panhead, and optimized the shape of the cross-sectional contour of the panhead. Yao et al (2012) adopted a new parametric approach called local shape function based on the free form surface deformation, and a new optimization method based on the response surface method of genetic algorithm-general regression neural network (GA-GRNN). After optimization, the aerodynamic drag for a three carriage train was reduced by 8.7%.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization design method of the high-speed train head

Maruyama

Zhang

2013

J. Zhejiang Univ. Sci. A

View full text Add to dashboard Cite

Abstract:With the continuous improvement of the train speed, the dynamic environment of trains turns out to be aerodynamic domination. Solving the aerodynamic problems has become one of the key factors of the high-speed train head design. Given that the aerodynamic drag is a significant factor that restrains train speed and energy conservation, reducing the aerodynamic drag is thus an important consideration of the high-speed train head design. However, the reduction of the aerodynamic drag may increase other aerodynamic forces (moments), possibly deteriorating the operational safety of the train. The multi-objective optimization design method of the high-speed train head was proposed in this paper, and the aerodynamic drag and load reduction factor were set to be optimization objectives. The automatic multi-objective optimization design of the high-speed train head can be achieved by integrating a series of procedures into the multi-objective optimization algorithm, such as the establishment of 3D parametric model, the aerodynamic mesh generation, the calculation of the flow field around the train, and the vehicle system dynamics. The correlation between the optimization objectives and optimization variables was analyzed to obtain the most important optimization variables, and a further analysis of the nonlinear relationship between the key optimization variables and the optimization objectives was obtained. After optimization, the aerodynamic drag of optimized train was reduced by up to 4.15%, and the load reduction factor was reduced by up to 1.72%.

show abstract

Three-dimensional aerodynamic optimization design of high-speed train nose based on GA-GRNN

Cited by 40 publications

References 12 publications

A modified multi-objective sorting particle swarm optimization and its application to the design of the nose shape of a high-speed train

A modified multi-objective sorting particle swarm optimization and its application to the design of the nose shape of a high-speed train

Digital Design of Streamlined EMU Head Car and Its Smoothness Evaluation

Multi-objective optimization design method of the high-speed train head

Contact Info

Product

Resources

About