Prediction of wheel profile wear and vehicle dynamics evolution considering stochastic parameters for high-speed train

Luo, Ren C.; Shi, Huailong; Teng, Wanxiu; Song, Chunyuan

doi:10.1016/j.wear.2017.09.019

Cited by 57 publications

(43 citation statements)

References 21 publications

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“…Therefore, there is no wear coefficient for high-speed railways in China at present. However, many researchers and scholars [20][21][22][23] have used the wear coefficient obtained from the commuter rail system in Stockholm to study the wheel/rail wear, which indicates that this wear coefficient can be suitable for various conditions, including the wheel/rail wear in China high-speed railways. Therefore, the wear coefficient adopted in this paper is depicted by Figure 5 according to the commuter rail system in Stockholm (Jendel [24]).…”

Section: Multibody Dynamics Modelmentioning

confidence: 99%

“…The statistical histograms of the vertical/lateral wheel-rail force, derailment coefficient, and the wheel load reduction ratio are illustrated in Figure 19, and the time history curves of contact patch area are shown in Figure 20. Moreover, the derailment coefficient and the wheel load reduction ratio can be calculated by Equations (20) and (21).…”

Section: Dynamic Performancesmentioning

confidence: 99%

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Optimizing the Rail Profile for High-Speed Railways Based on Artificial Neural Network and Genetic Algorithm Coupled Method

Jiang

Gao

2020

Sustainability

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Though the high-speed railways are seen as a sustainable form of transportation, the fact that the rail wear in high-speed railways negatively affects the running safety and riding comfort, as well as the maintenance of railways, has drawn a wide range of concerns among researchers and scholars. In order to reduce the rail wear and achieve the goal of sustainable transportation, this paper proposes an ingenious optimization program of rail profiles based on the artificial neural network (ANN) and genetic algorithm (GA) coupled method. The candidate solutions of the nonlinear GA programming model are regarded as the inputs of the trained ANN model. Meanwhile, the outputs of the trained ANN model serve as the objective functions of the GA model. The computational results show that the optimized rail profile not only has superior performances in terms of the wheel/rail wear and contact conditions, but also maintains good dynamic performances. Therefore, this study can provide the theoretical and practical basis for the design and the preventive grinding of rails in the high-speed railways. Also, the ANN-GA coupled model can be extended and further employed on the optimization of other rail profiles.

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Section: Multibody Dynamics Modelmentioning

confidence: 99%

Section: Dynamic Performancesmentioning

confidence: 99%

Optimizing the Rail Profile for High-Speed Railways Based on Artificial Neural Network and Genetic Algorithm Coupled Method

Jiang

Gao

2020

Sustainability

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“…For predicting wheel or track wear, the Archard model is used, described by the equation [13,17,21]:…”

Section: The Wear Modelmentioning

confidence: 99%

Assessment of durability and reliability of ET41 series locomotive wheels based on laboratory tests

Witaszek¹

2020

Diagnostyka

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“…where and are stochastic MHFs coefficient vectors and and j , j = 1, 2, … , n are stochastic MHFs coefficient matrices. Substituting (9)- (12) in relation (8), we obtain Using the 6-th property in relation (13), we get Utilizing operational matrices defined in relations (5) and (6) in (14), we have…”

Section: Solving Stochastic Itô-volterra Integral Equation With Multimentioning

confidence: 99%

“…Nowadays, modelling different problems in different issues of science leads to stochastic equations [1]. These equations arise in many fields of science such as mathematics and statistics [2][3][4][5][6][7], finance [8][9][10], physics [11][12][13], mechanics [14,15], biology [16][17][18], and medicine [19,20]. Whereas most of them do not have an exact solution, the role of numerical methods and finding a reliable and accurate numerical approximation have become highlighted [21].…”

Section: Introductionmentioning

confidence: 99%

A computational method for solving stochastic Itô–Volterra integral equation with multi-stochastic terms

2018

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In this paper, a linear combination of quadratic modified hat functions is proposed to solve stochastic Itô-Volterra integral equation with multi-stochastic terms. All known and unknown functions are expanded in terms of modified hat functions and replaced in the original equation. The operational matrices are calculated and embedded in the equation to achieve a linear system of equations which gives the expansion coefficients of the solution. Also, under some conditions the error of the method is O(h 3). The accuracy and reliability of the method are studied and compared with those of block pulse functions and generalized hat functions in some examples.

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Prediction of wheel profile wear and vehicle dynamics evolution considering stochastic parameters for high-speed train

Cited by 57 publications

References 21 publications

Optimizing the Rail Profile for High-Speed Railways Based on Artificial Neural Network and Genetic Algorithm Coupled Method

Optimizing the Rail Profile for High-Speed Railways Based on Artificial Neural Network and Genetic Algorithm Coupled Method

Assessment of durability and reliability of ET41 series locomotive wheels based on laboratory tests

A computational method for solving stochastic Itô–Volterra integral equation with multi-stochastic terms

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