Search

As the high-speed train navigates extreme tunnels—characterised by high altitude, steep gradients, and an extended length—extreme tunnel pressure waves are introduced into the carriage, inducing internal pressure fluctuations that in turn impact passenger pressure comfort. To clarify the pressure fluctuation dynamics experienced by the train navigating extreme tunnels, this study initially employs one-dimensional (1D) numerical simulation to replicate extreme tunnel pressure waves. Subsequently, an internal pressure fluctuation model is formulated, accounting for the coupled effects of the oxygen supply system, airtight gaps, air ducts, and car body deformation. Based on this model, the analysis extends to investigating the diverse effects of distinct factors on internal pressure fluctuation. Employing the orthogonal experimental design methodology, it is established that the primary effect on internal pressure fluctuation is attributed to air ducts, followed by the oxygen supply system, airtight gaps, and finally, car body deformation. Lastly, the effect of pressure variation on passenger pressure comfort is analysed. Outcomes reveal that under the straight tunnel condition, the change rate of internal pressure in 10 s interval is 1025.2 Pa/10 s, exceeding 2.52 % of the standard, and the amplitude of internal pressure reaches 1.5 kPa, surpassing 50 % of the standard. However, under the extreme tunnel condition, the amplitude of internal pressure escalates to 10.9 kPa, substantially exceeding the passenger comfort standard. [Display omitted] • Established the internal pressure model of trains traversing extreme tunnels. • Obtained a sequence of transfer ways affecting the internal pressure fluctuation. • Obtained the internal pressure fluctuation mechanism of trains traversing tunnels. • Analysed the effect of internal pressure variation on passenger pressure comfort. [ABSTRACT FROM AUTHOR]

• Test result of pressure sensor for high speed train will be affected by vibration. • Simulation of actual vibration condition of train body with large mass method. • Simulation of pressure sensor voltage output by electrical structure simulation. • Vibration with same amplitude and different frequency has different influence. • Linear simplified model can accurately calculate vibration effect. This paper devotes to investigate the influence of vibration acceleration on measurement of piezoresistive pressure. For the sake of definiteness, sensor operation conditions in high-speed train are considered. Where, the sensor chip is loaded with dynamic accelerations with frequency within 20 Hz. The real state of the pressure sensor under different frequency vibration load is simulated by using large mass method (LMM). And the voltage output of the pressure sensor under different vibration loads was obtained by the joint simulation of electrical- structure. By analysis of different vibration loads and the voltage output of sensors, their internal relations are obtained. These outcomes provide a guidance for elimination of vibration influence in high-speed train surface fluctuation pressure measurement. It is also useful in other fields of pressure sensor operation in acceleration-loaded environments. [ABSTRACT FROM AUTHOR]

Detached Eddy Simulation (DES) has been used to investigate the effect of the Reynolds number on the wake of a high-speed train in the range from 5.2 ​× ​105 to 2.2 ​× ​107. Then, the characteristics of vortex structure, streamlines distribution, velocity characteristics, and pressure characteristics in the wake region of the high-speed train at five Reynolds numbers are studied. The results show that with the increase of the Reynolds number, some small-scale vortices emerge in the wake region of the high-speed train, which increases the complexity of the wake vortex structure, but the large-scale vortex structure has not changed significantly. The separation lines and vortices distributed on the train surface, and the characteristics of the pressure distribution in the wake region do not change considerably at different Reynolds numbers. The frequency of the vortex shedding from the train based on Strouhal number is approximately a constant at five Reynolds numbers, and the phenomenon and mechanism of the flow in the wake region are almost the same within the range of Reynolds number selected in this study. With the increase of the Reynolds number, the fluctuation degree of the velocity and pressure, and the vorticity in the wake region increase substantially. • DES has been used to investigate the wake characteristics of a high-speed train at five Reynolds numbers. • With the increase of Re , small-scale vortices emerge, but the large-scale vortex structure has not changed significantly. • The separation lines and vortices distributed on the train surface do not change considerably at different Re. • The phenomenon and mechanism of the flow in wake region is almost same within the range of Re from 5.2 × 105 to 2.2 × 107. • With the increase of Re , the fluctuation degree of velocity and pressure, and the vorticity in the wake region increase. [ABSTRACT FROM AUTHOR]

Highlights • The chaotic features of the signals of high speed trains are extracted. • A feature extraction method based on MPE-LTSA is proposed. • The proposed method is also applied to extract features in high speed trains. • This method can be used in other feature recognition for complex chaotic signals. Abstract Hunting stability is an important factor for high-speed trains to achieve safe operation, which can be monitored by on-board instruments. When analysing measured online tracking data of high-speed trains, the authors have observed that small amplitude hunting tend to appear. When these signals show growth of lateral vibration to high enough amplitude, train derailment would happen. Research on the bifurcation evolution of small amplitude hunting of high-speed trains has been rarely reported so far. In this paper, chaotic features of the data are extracted and the results show that lateral acceleration signals from the bogie frame has strong nonlinear characteristics. Then a commonly used method based on frequency distribution characteristics of bogie vibration energy is first used to separate different states of hunting. However, the results are not satisfactory. So a feature extraction method based on Multiscale Permutation Entropy (MPE) and Local Tangent Space Alignment (LTSA) is proposed to distinguish the different states of complex signals. The proposed method is applied to extract features of the small amplitude hunting signals at high-speed of 320–350 km/h. The results show that the MPE-LTSA method can identify the bifurcation evolution of small amplitude hunting signals much more effectively than the method based on the MPE-ISOMAP (Isometric Feature Mapping) and MPE-PCA (Principle Component Analysis). The method can be used in other feature recognition for the complex chaotic signals. [ABSTRACT FROM AUTHOR]