Abstract
A π-shaped composite deck in the form of an open section is a type of blunt body that is highly susceptible to wind
loads. To investigate its vortex-induced vibration (VIV) performance, a large-scale (1/20) section model of a cable-stayed bridge
with a main span of 650 m was tested in a wind tunnel. The vibration suppression mechanism of the countermeasures was
analyzed using computational fluid dynamic. Experimental results demonstrate that the vertical and torsional VIVs of the
original section can be suppressed by combining guide plates with a tilt angle of 35° and bottom central stabilizing plates as
aerodynamic countermeasures. Numerical results indicate that the large-scale vortex under the deck separates into smaller
vortices, resulting in the disappearance of the von Karman vortex street in the wake zone because the countermeasures
effectively suppress the VIVs. Furthermore, a full-bridge aeroelastic model with a scale of 1/100 was constructed and tested to
evaluate the wind resistance performance and validate the effectiveness of the proposed countermeasures.
Abstract
A solar concentrator is a reflective surface in the shape of a parabola that collects solar rays in a focal area. This
concentrator follows the path of the sun during the day with the help of a tracking system. One of the most important issues in
the design and construction of these reflectors is the force exerted by the wind. This force can sometimes disrupt the stability of
the concentrator and overturn the entire system. One of the ways to estimate the force is to use the numerical solution of the air
flow in three dimensions around the dish. Ansys Fluent simulation software has been used for modeling several angles of attack
between 0 and 180 with respect to the horizon. From the comparison of the velocity vector lines on the dish at angles of 90 to -
90 degrees, it was found that the flow lines are more concentrated inside the dish and there is a tendency for the flow to escape
around in the radial direction, which indicates the presence of more pressure distribution inside the dish. It was observed that the
pressure on the concave surface was higher than the convex one. Then, the effect of adding a hole with various diameter of 200,
300, 400, 500, and 600 mm on the dish was investigated. By increasing the diameter up to the optimized size of 400 mm, a
decrease in the maximum pressure value in the pressure distribution was shown inside the dish. This pressure drop decreased the
drag coefficient. The effect of the hole on the dish was also investigated for the 30-degree angled dish, and it was found that the
results of the 90-degree case should be considered as the basis of the design.
Key Words
ansys fluent; CFD; concentrator; drag; optimization; wind
Address
Sayyed Hossein Mostafavi, Amir Torabi and Behzad Ghasemi:Faculty of Engineering and Technology, Shahrekord University, Rahbar Bolivar, Shahrekord, Iran
Abstract
The aerodynamic damping is an essential factor that can considerably affect the dynamic response of the cablestayed bridge induced by crosswind load. However, developing an accurate and efficient aerodynamic damping model is crucial
for evaluating the crosswind load-induced response on cable-stayed bridges. Therefore, this study proposes a new method for
identifying aerodynamic damping of the bridge structures under crosswind load using an extended Kalman filter (EKF) and the
particle filter (PF) algorithm. The EKF algorithm is introduced to capture the aerodynamic damping ratio. PF technique is used
to select the optimal spectral representation of the noise. The effectiveness and accuracy of the proposed solution were
investigated through full-scale vibration measurement data of the crosswind-induced on the bridge's girder. The results show that
the proposed solution can generate an efficient and robust estimation. The errors between the target and extracted values are
around 0.01 mm and 0.003^o, respectively, for the vertical and torsional motion. The relationship between the amplitude and the
aerodynamic damping ratio is linear for small reduced wind velocity and nonlinear with the increasing value of the reduced wind
velocity. Finally, the results show the influence of the level of noise.
Abstract
Aiming at the problem of non-stationary wind field simulation of downbursts, a non-stationary down-burst
generation system was designed by adding a nozzle and program control valve to the inlet of the original wall jet model. The
computational fluid dynamics (CFD) method was used to simulate the downburst. Firstly, the two-dimensional (2D) model was
used to study the outflow situation, and the database of working conditions was formed. Then the combined superposition of
working conditions was carried out to simulate the full-scale measured downburst. The three-dimensional (3D) large eddy
simulation (LES) was used for further verification based on this superposition condition. Finally, the wind tunnel test is used to
further verify. The results show that after the valve is opened, the wind ve-locity at low altitude increases rapidly, then stays
stable, and the wind velocity at each point fluctuates. The velocity of the 2D model matches the wind velocity trend of the
measured downburst well. The 3D model matches the measured downburst flow in terms of wind velocity and pulsation
characteris-tics. The time-varying mean wind velocity of the wind tunnel test is in better agreement with the meas-ured timevarying mean wind velocity of the downburst. The power spectrum of fluctuating wind ve-locity at different vertical heights for
the test condition also agrees well with the von Karman spectrum, and conforms to the "-5/3" law. The vertical profile of the
maximum time-varying average wind veloci-ty obtained from the test shows the basic characteristics of the typical wind profile
of the downburst. The effectiveness of the downburst generation system is verified.
Key Words
computational fluid dynamics; downburst; large eddy simulation; non-stationary wind field; wall jet
Address
Yongli Zhong, Yichen Liu, Xinpeng Liu and Jun Luo:School of Civil Engineering and Architecture, Chongqing University of Science and Technology, 401331, China
Hua Zhang:CENTRAL-SOUTH Architectural Design Institute Co., Ltd, 430061, China
Zhitao Yan:1)School of Civil Engineering and Architecture, Chongqing University of Science and Technology, 401331, China 2)School of Civil Engineering, Chongqing University, 400045, China
Kaihong Bai:Chongqing Institute of Geology and Mineral Resources, 400042, China
Feng Li:Chongqing Urban Investment Infrastructure Construction Co., Ltd, 400015, China
Abstract
The aerodynamic force is a significant component that influences the stability and safety of structures. It has
unstable properties and depends on computer precision, making its long-term prediction challenging. Accurately estimating the
aerodynamic traits of structures is critical for structural design and vibration control. This paper establishes an unsteady
aerodynamic time series prediction model using Long Short-Term Memory (LSTM) network. The unsteady aerodynamic force
under varied Reynolds number and angles of attack is predicted by the LSTM model. The input of the model is the aerodynamic
coefficients of the 1 to n sample points and output is the aerodynamic coefficients of the n+1 sample point. The model is
predicted by interpolation and extrapolation utilizing Unsteady Reynolds-average Navier-Stokes (URANS) simulation data of
flow around a circular cylinder, square cylinder and airfoil. The results illustrate that the trajectories of the LSTM prediction
results and URANS outcomes are largely consistent with time. The mean relative error between the forecast results and the
original results is less than 6%. Therefore, our technique has a prospective application in unsteady aerodynamic force prediction
of structures and can give technical assistance for engineering applications.
Key Words
angles of attack; deep learning; engineering structures; long short-term memory; Reynolds number; unsteady
aerodynamic force
Address
Shijie Liu:School of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
Zhen Zhang:1)School of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
2)State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China 3)Innovation Center for Wind Engineering and Wind Energy Technology of Hebei Province, Shijiazhuang 050043, China
Xue Zhou:School of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
Qingkuan Liu:1)School of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
2)State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China 3)Innovation Center for Wind Engineering and Wind Energy Technology of Hebei Province, Shijiazhuang 050043, China
