Numerical study of wind loads on the streamlined bridge deck in the translating tornado-like vortex

With global warming intensifying, weather patterns become more volatile and extremes more common. Tornadoes are the most destructive natural disasters causing significant damage to infrastructure. Meanwhile, high-speed railways now face greater risks from tornado events as the national railway network and mass transit trains expand. Thus, studying the tornado flow characteristics and associated effects on high-speed trains is necessary. A study is presented regarding the wind-loading characteristics of a high-speed train running over a railway bridge induced by a tornado belonging to the future railway network. The wind-loading characteristics analyses are performed using the improved delayed detached eddy simulation method. After verifying the numerical approach and mesh strategy, computational studies are conducted to produce a tornado-like vortex and investigate the tornado-induced wind-loading characteristics of a high-speed train running on the bridge by combining a tornado simulation with a moving mesh technique. For the wind-loading parameters studied herein, the selected train's velocity range is between 50 and 350 km/h, the typical operation speed of either regular or high-speed trains. The numerical results show that the time histories of aerodynamic forces on the train revealed a pattern in tornadic flow variability, the time evolutions of the wind loads on the train were affected by train speeds, and the fluctuation was the greatest when the train ran at 50 km/h. Moreover, the train is subjected to larger aerodynamic forces and moments when it operates along with the rotating vortex flow, especially in the core region, and the train is more dangerous when it runs at a lower speed. The results in this study provide references for assessing operation safety, while a train running on the bridge encounters tornadoes.

Numerical study on wind-loading characteristics of a high-speed train running over the bridge under tornado-like vortices

He,

Zou

2024

Flow around single and two tandem rectangular cylinders with various single-side fairings

Dong,

Shi,

2024

The shape of single-side wind fairing, which is the longitudinal triangular prism that tailors the outer side of a bridge deck, is key to the aerodynamic performances of double streamlined box girders used in long-span bridges. Uniform flow past single and double 4:1 rectangular cylinders with various single-side fairings are investigated using large-eddy simulation at a Reynolds number of 1.1 × 104. Various fairing nose angles and heights are compared. The wind loading and flow characteristics of the cylinders are discussed. The upstream fairing shows a larger reduction of mean drag and fluctuating lift on double cylinders than on a single cylinder. The fairing nose angle has a stronger influence on the wind loading than the nose height. By adding the fairing, sharpening the fairing nose, or lifting the fairing nose, the lateral recirculation zones are shortened while the rear recirculation zone barely changes, leading to different influences on the surface pressure. The upstream fairing is effective in reducing the vertical range and complexity of vortex structures around single and double cylinders.

Identification of full-field wind loads on buildings using a mechanism-inspired recursive convolutional neural network with partial structural responses

Zhang,

Lei,

Liu

et al. 2024

Indirect identification approaches through structural responses have proven effective for wind load estimation in real-world engineering. Currently, methods for identifying wind loads mainly rely on theoretical inverse identification, with rare research based on the mapping relationship between structural responses and wind loads through machine learning. In this paper, a scheme for identifying full-field wind loads using a recursive convolutional neural network (CNN) inspired by physical mechanisms is proposed. The recursive form of the network, as well as the inspiration for its inputs and outputs, is inspired by the spatial correlation and the mapping relationship between wind loads and structural responses. Thus, the network inputs comprise a fusion of structural acceleration and inter-story displacement responses, while the network outputs represent the independent wind loads on structures. Notably, mismatch test is employed by the network, wherein the training and testing datasets originate from entirely different sources. Specifically, during training, Gaussian white noises that simulate wind loads are utilized, while real wind load data are used for testing. The generalization of the proposed scheme is demonstrated through the identification of full-field wind loads generated by different stationary or non-stationary wind spectra of the 76-story wind-excited benchmark building. Furthermore, the proposed scheme is validated by identifying the full-field wind loads of a 67-story shear wall structure with wind tunnel test data.