A survey of parametric modelling methods for designing the head of a high-speed train

Wang, Ruibin; Zhang, Jianjun; Bian, Shaojun; You, L. H.

doi:10.1177/0954409718756558

Cited by 7 publications

(7 citation statements)

References 42 publications

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“…Normally, more design variables can describe more complicated train head shapes and provide more degrees of freedom to control the shapes in the modeling process, but it will be more difficult to maintain the surface smoothness and find an optimal solution in the optimization process. Therefore, a good parametric modeling method able to define detailed train head models is the key to success (Wang et al 2018). The partial differential equation (PDE)-based modeling method is fully capable in creating a complex shape with three design variables which provides an effective solution to these problems.…”

Section: Parametric Modelingmentioning

confidence: 99%

“…With the increasing running speed, the aerodynamic performance of high-speed trains has a significant influence on its running stability and energy efficiency which is directly dominated by the streamline head shape of high-speed trains (Raghunathan et al 2002). Therefore, the design and optimization of the head shape is the key to improve the aerodynamic performance of highspeed trains (Wang et al 2018). Generally, the optimization design of high-speed train heads mainly includes parametric modeling and aerodynamic analysis processes.…”

Section: Introductionmentioning

confidence: 99%

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Multi-objective aerodynamic optimization of high-speed train heads based on the PDE parametric modeling

Wang

Xia

et al. 2021

Struct Multidisc Optim

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With the increasing running speed, the aerodynamic issues of high-speed trains are being raised and impact the running stability and energy efficiency. The optimization design of the head shape is significantly important in improving the aerodynamic performance of high-speed trains. Existing aerodynamic optimization methods are limited by the parametric modeling methods of train heads which are unable to accurately and completely parameterize both global shapes and local details. Due to this reason, they cannot optimize both global and local shapes of train heads. In order to tackle this problem, we propose a novel multi-objective aerodynamic optimization method of high-speed train heads based on the partial differential equation (PDE) parametric modeling. With this method, the half of a train head is parameterized with 17 PDE surface patches which describe global shapes and local details and keep the surface smooth. We take the aerodynamic drag and lift as optimization objectives; divide the optimization design process into two stages: global optimization and local optimization; and develop global and local optimization methods, respectively. In the first stage, the non-dominated sorting genetic algorithm (NSGA-II) is adopted to obtain the framework of the train head with an optimized global shape. In the second stage, Latin hypercube sampling (LHS) is applied in the local shape optimization of the PDE surface patches determined by the optimized framework to improve local details. The effectiveness of our proposed method is demonstrated by better aerodynamic performance achieved from the optimization solutions in global and local optimization stages in comparison with the original high-speed train head.

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Section: Parametric Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-objective aerodynamic optimization of high-speed train heads based on the PDE parametric modeling

Wang

Xia

et al. 2021

Struct Multidisc Optim

Self Cite

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“…Various methods of describing high-speed train design in 2D and 3D space are reviewed in [2]. In the beginning, the 2D designs are analysed concerning mainly the high-speed train's forehead shape.…”

Section: Introductionmentioning

confidence: 99%

“…In the beginning, the 2D designs are analysed concerning mainly the high-speed train's forehead shape. Therefore, the 2D planar design is primarily intended for analysis of the effects of the longitudinal cross section parameters on the flow and, consequently, the aerodynamic drag [2,3]. Of course, the aerodynamic performances of 3D design are subjected to the influence of the forebody shape, dimensions, and body type (squared, rounded or sharp) [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Multidisciplinary research method for designing and selection of bio-inspired profiles in the conceptual designing stage

Linić

Lučanin

Živković

et al. 2021

J Braz. Soc. Mech. Sci. Eng.

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A multidisciplinary research method was employed with the intention to create a series of bio-inspired flattened airfoils, observe their aerodynamic characteristics, and analyse their applicability to small devices or to designs of high-speed trains, within the shortest period in the conceptual stage. A research specimen of a kingfisher, selected for biomimicry, was examined with the following methods: visual inspection, analysis of photographs, manufacturing quality control measurement with a 3D laser scanner, and microscopy. A basic multi-arc-line profile, re-engineered from the overlapped specimen shape data and based on the observations, was used for designing a series of seven derived airfoils. The aerodynamic characteristics of the bio-inspired airfoils were obtained with the panel methods at low and moderate subsonic speeds, while the small transonic difference method was used in the high-subsonic speed range. Basic and ellipse-like airfoils produce higher total drag at low and moderate velocities and higher forebody drag in the high-subsonic range when compared to derived and parabola-like airfoils. The obtained critical Mach numbers are in the range from 0.76 to 0.78, where three bionic airfoils show values equal to or smaller than the values of ellipse-and parabola-like airfoils. The profile with the shortest bio-inspired relative chord has a higher critical Mach number value than the parabola-like profile. The sonic lines above these profiles appear at close positions. The applied set of examination methods of the bio-inspired design is not time consuming and produces sufficiently good results in the conceptual stage. Therefore, a further development of unique and adjusted numerical methods and codes at pre-computational fluid dynamics run is encouraged, together with shape parameterization.

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“…Geometric design of high-speed train heads is a key part in the aerodynamic research of high-speed trains (Wang et al 2018). A high-speed train head involves a lot of design variables and high smoothness requirements.…”

Section: Introductionmentioning

confidence: 99%

Optimal NURBS conversion of PDE surface-represented high-speed train heads

et al. 2019

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The head shape of high-speed trains has become a critical factor in boosting the speed further. Aerodynamic simulation-based optimization is a dominant method to obtain the optimal head shape which relies on detailed train head models defined by a lot of design variables. Since aerodynamic simulation-based optimization involves heavy calculations, too many design variables not only causes high computational costs, but also makes the optimal solution difficult to obtain. Therefore, how to use few design variables to define detailed train head model is the key to success. Partial differential equation (PDE)-based geometric modelling which creates a complicated PDE patch with few design variables provides an effective solution to this problem. In addition, it also has the advantage of naturally maintaining any high-order continuities between two adjacent surfaces which is very important in designing highly smooth train heads to achieve excellent aerodynamic performance. At the present time, PDE-based geometric modelling cannot be directly applied in computer-aided design (CAD), computer-aided manufacturing (CAM), and computer-aided engineering (CAE) since it has not become an industrial standard. In contrast, non-uniform rational B-splines (NURBS) are commonly used in CAD, CAM, CAE, and many other engineering fields. They have already become part of industry wide standards. In order to apply PDE-based geometric modelling in shape design of high-speed train heads for CAD etc., how to optimally convert PDE surfaces into NURBS surfaces must be addressed. In this paper, a new method of achieving optimal conversion of PDE surfaces representing high-speed train heads into NURBS surfaces is developed. It takes control points and weight deformations of NURBS surfaces to be design variables, and the error between NURBS surfaces and PDE surfaces as the objective function. The least squares fitting and the genetic algorithm are combined to obtain the optimal conversion between PDE surfaces and NURBS surfaces. The application examples demonstrate the effectiveness of the developed method.

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A survey of parametric modelling methods for designing the head of a high-speed train

Cited by 7 publications

References 42 publications

Multi-objective aerodynamic optimization of high-speed train heads based on the PDE parametric modeling

Multi-objective aerodynamic optimization of high-speed train heads based on the PDE parametric modeling

Multidisciplinary research method for designing and selection of bio-inspired profiles in the conceptual designing stage

Optimal NURBS conversion of PDE surface-represented high-speed train heads

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