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Structural Vibration Control Using Passive Devices
Published in Suhasini Madhekar, Vasant Matsagar, Passive Vibration Control of Structures, 2022
Suhasini Madhekar, Vasant Matsagar
Wind flow is generated due to atmospheric pressure differentials and manifests itself into various forms, such as, gales and monsoon winds, cyclones/hurricanes/typhoons, tornados, thunderstorms, and localized storms. Speed of strong winds is of about 60–70 km/h; whereas that of gale winds is about 80 km/h and higher. Both types of winds cause serious destruction in the civil engineering structures, usually in tall buildings, bridges, chimneys, and towers. Wind applies lateral pressure along the height of the structure. The design of structures for wind loading is governed by intensity of wind speed, severity of aerodynamic effects, and the dynamic response of the structural systems. For flexible and slender structures, such as sky scrapers and cable-stayed and suspension bridges, consideration of aerodynamic effects in the architectural and structural design is mandatory. For the purpose of structural design, the wind environment is described in meteorological terms, by specifying the types of storm in the region of interest, e.g., hurricanes, thunderstorms, tornadoes, and in micro-meteorological terms, i.e., fluctuations in wind speeds, turbulent flow fluctuations on the surface roughness of structure, and the height of structure. Severe windstorms happen almost everywhere in the world, causing equivalent damages to that of the destructive earthquakes. There are several different phenomena giving rise to dynamic response of structures in wind, such as buffeting, vortex shedding, galloping, and flutter. Slender structures are sensitive to dynamic response, in the direction of wind, as a consequence of turbulence buffeting. Transverse (crosswind) response is more likely to arise from vortex shedding or galloping but may also result from excitation by turbulence buffeting. Flutter is a coupled motion, often being a combination of bending and torsion, and can result in instability of the structure.
A preliminary assessment of machine learning algorithms for predicting CFD-simulated wind flow patterns over idealised foredunes
Published in Journal of the Royal Society of New Zealand, 2021
Sarah J. Wakes, Bernard O. Bauer, Michael Mayo
The RANS k-ϵ RNG turbulence model was used (Wakes et al. 2010, 2016; Pattanapol et al. 2011; Wakes 2013). The boundary conditions were either inlets (Figure 1B) or pressure outlets. The dune surface was assumed hydrodynamically smooth for which the standard law-of-the-wall applies. Two wind flow parameters (speed and direction) were varied in the simulations (Table 1). Specifically, three representative wind speeds were used, ranging from the initiation of sediment transport for fine sand (7 ms−1, light breeze on the Beaufort Scale) to sustained transport (12 ms−1, strong breeze, Beaufort Scale), and to extreme sediment transport conditions rarely encountered on most mid-latitude and tropical beaches (20 ms−1, gale, Beaufort Scale). Wind direction was varied in 15° intervals from shore normal (0° approach angle) to perfectly alongshore (90° approach angle). Numerical results were extracted from the model domain as follows (Figure 1B): ten points on ten horizontal shore-parallel lines spanning the width of the model domain but clustered around the dune form at even intervals, and at two heights above the surface (0.5 and 1.5 m) for Experiment 1; and from the first 10 points of ten vertical lines at the stoss and at the lee of the dune at around midway along the width of the domain for Experiment 2.
Distribution of surface soil mercury of Wuda old mining area, Inner Mongolia, China
Published in Human and Ecological Risk Assessment: An International Journal, 2018
Chunhui Li, Handong Liang, Yang Chen, Jiangwei Bai, Yukun Cui
The Wuda District of the city of Wuhai (39°29′N, 106°42′E) is located in central Inner Mongolia at the northern end of Helan Mountain, at the southern edge of Ulan Buh Desert, and adjacent to the Ningxia Hui Autonomous Region (Figure 1A), and the Yellow River crosses through it from south to north at its eastern edge (Figure 1C). The Wuda District has an area of about 220 km2 and a population of about 130,000 (2004). Wuda is in the temperate zone, with a strong continental and arid climate. A prevailing northwesterly wind is observed in this area, with an annual average wind speed of 4.8 ms−1 and an average number of gale (Beaufort scale>7) days of 32 (Wuda Municipal Government 2012). The Wuda coalfield is located in the northwest of the District (Figure 1C) and has an area of 35 km2. It is rich in Carboniferous-Permian coal, with over 16 minable seams and a reserve of 660 million tons, and mainly produces bituminous coal (high sulfur content) (Zhang et al. 2008a). We conducted our study of topsoil Hg concentrations in Wuda District, including the coalfield, urban, industrial park, farm, and wasteland areas of the region (Figure 1C).
Measurements of car-body lateral vibration induced by high-speed trains negotiating complex terrain sections under strong wind conditions
Published in Vehicle System Dynamics, 2018
Dongrun Liu, Zhaijun Lu, Mu Zhong, Tianpei Cao, Dong Chen, Yupu Xiong
The wind speed in this paper obtained from the railway meteorological department refers to the wind speed measured through the gale warning system of Lanzhou–Xinjiang high-speed railway. To guarantee the safe running in strong winds, a number of base stations were established to test wind speed in the continuous wind area along the Lanzhou–Xinjiang high-speed railway. The wind towers are arranged along the railway line at intervals according to the nearby topography. Considering the influence of changes in terrain changes on the wind speed, the spacing between two wind towers should be 1500–2000 m in flat terrain, and 100–200 m in complex terrain with abrupt changes. The wind towers are located 10 m from the centreline of track outside the windbreak, and each wind tower is equipped with two sets of sensors to detect wind speed and wind direction, which are 4.5–5 m higher above the track, as shown in Figure 2. Ventus-V200A ultrasonic anemoscope made by Lufft Company is adapted as the wind force monitoring equipment. It has four ultrasonic sensors to measure in all directions. The wind speed measurement range is 0–60 m/s, the sampling rate is 250 ms, and the response threshold is 0.1 m/s, which fully meets the requirements of wind speed measurement for this test [11]. Next, all the wind speeds mentioned are the mean values within 1 s when the vehicle passes the position, truly reflecting the wind speed when the train passes.