At speeds above 300–350 km/h, the main source of noise from trains is the aerodynamic noise caused by the air flow over the train structure. The sound level increases with train speed at a rate of between 60 and 80 times the logarithm of the speed so that, as speeds increase further, the noise increases dramatically. The main aerodynamic noise is produced by the air flow passing over the pantograph, the train nose, the bogie region and cavities such as the pantograph recess and the inter-coach gap. Experimental and numerical methods for studying aerodynamic noise are reviewed including the use of microphone arrays, wind tunnels, computational fluid dynamics and semi-empirical methods. Potential mitigation measures that can control aerodynamic noise are also reviewed.

Recent developments in the prediction and control of aerodynamic noise from high-speed trains

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Theory Of Vortex Sound

Component-based model to predict aerodynamic noise from high-speed train pantographs

Excessive vibrations of railway vehicles induced by dynamic impact loadings have a significant impact on train operating safety and stability; however, due to the complexity and diversity of railway lines and service environment, they are extremely difficult to eliminate. A comprehensive overview of recent studies on the impact vibration behavior of railway vehicles was given in this paper. First, the sources of impact excitations were categorized in terms of wheel-rail contact irregularity, aerodynamic loads, and longitudinal impulses by train traction/braking. Then the main research approaches of vehicle impact vibration were briefly introduced in theoretical, experimental, and simulation aspects. Also, the impact vibration response characteristics of railway vehicles were categorized and examined in detail to various impact excitation sources. Finally, some attempts of using the railway vehicle vibration to detect track defects and the possible mitigation measures were outlined.

Impact vibration behavior of railway vehicles: a state-of-the-art overview

Aerodynamic noise becomes a significant noise source at speeds normally reached by high-speed trains. The train bogies are identified as important sources of aerodynamic noise. Due to the difficulty to assess this noise source carrying out field tests, wind tunnel tests offer many advantages. Tests were performed in the large-scale low-noise anechoic wind tunnel at Maibara, Japan, using a 1/7 scale train car and bogie model for a range of flow speeds between 50, 76, 89 and 100 m/s. The dependence of the aerodynamic noise from the bogie region on different factors has been studied for different bogie configurations and inflow conditions representing different positions of the bogie along the train. The speed dependence and the noise directivity have also been assessed. The results show the particular importance of the components exposed to the free flow, whereas those shielded within the bogie cavity are shown to radiate much less noise.

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Anechoic wind tunnel tests on high-speed train bogie aerodynamic noise

Reduction of aerodynamic noise from square bars by introducing spanwise waviness

A numerical investigation of the aeroacoustic characteristics of the flow past a circular cylinder is presented for Reynolds numbers in the range 2.67×104−3.67×105, which falls within the upper sub...

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Numerical investigation of aerodynamic noise generated by circular cylinders in cross-flow at Reynolds numbers in the upper subcritical and critical regimes:

Xiaowan Liu

Papers

Recent developments in the prediction and control of aerodynamic noise from high-speed trains

Reduction of aerodynamic noise from square bars by introducing spanwise waviness

Numerical investigation of aerodynamic noise generated by circular cylinders in cross-flow at Reynolds numbers in the upper subcritical and critical regimes:

Using a 2.5D boundary element model to predict the sound distribution on train external surfaces due to rolling noise

Aerodynamic noise of high-speed train pantographs: Comparisons between field measurements and an updated component-based prediction model