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CONTENTS
Volume 31, Number 4, October 2020
 

Abstract
This paper presents an experimental investigation on the surface pressures on the CAARC standard tall building model concerning the effects of freestream turbulence. Two groups of incidence turbulence are generated in the wind tunnel experiment. The first group has an approximately constant turbulence intensity of 10.3% but different turbulence integral scale varying from 0.141 m to 0.599 m or from 0.93 to 5.88 in terms of scale ratio (turbulence integral scale to building dimension). The second group presents similar turbulence integral scale but different turbulence intensity ranging from 7.2% to 13.5%. The experimental results show that the mean pressure coefficients on about half of the axial length of the side faces near the leading edge slightly decrease as the turbulence integral scale ratio that is larger than 4.25 increases, but respond markedly to the changes in turbulence intensity. The root-mean-square (RMS) and peak pressure coefficients depend on both turbulence integral scale and intensity. The RMS pressure coefficients increase with turbulence integral scale and intensity. As the turbulence integral scale increases from 0.141 m to 0.599 m, the mean peak pressure coefficient increases by 7%, 20% and 32% at most on the windward, side faces and leeward of the building model, respectively. As the turbulence intensity increases from 7.2% to 13.5%, the mean value of peak pressure coefficient increases by 47%, 69% and 23% at most on windward, side faces and leeward, respectively. The values of cross-correlations of fluctuating pressures increase as the turbulence integral scale increases, but decrease as turbulence intensity increases in most cases.

Key Words
tall building; turbulence effects; surface pressures; cross-correlation; wind tunnel test

Address
Yonggui Li, Jiahui Yan, Yi Li:Hunan Provincial Key Laboratory of Structures for Wind Resistance and Vibration Control & School of Civil Engineering,
Hunan University of Science and Technology, Xiangtan, 411201, Hunan, China
Xinzhong Chen:National Wind Institute, Department of Civil and Environmental Engineering, Texas Tech University, Lubbock TX, 79409, U.S.A.
Qiusheng Li :3Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong

Abstract
In this study, wind data such as speeds, loads and potential of Muğla which is located in the southwest of Turkey were statistically analyzed. The wind data which consists of hourly wind speed between 2010 and 2013 years, was measured at the 10-meters height in four different ground stations (Datça, Fethiye, Marmaris, Koycegiz). These stations are operated by The Turkish State Meteorological Service (T.S.M.S). Furthermore, wind data was analyzed by using Log-Normal and Gamma distributions, since these distributions fit better than Weibull, Normal, Exponential and Logistic distributions. Root Mean Squared Error (RMSE) and the coefficients of the goodness of fit (R2) were also determined by using statistical analysis. According to the results, extreme wind speed in the research area was 33 m/s at the Datça station. The effective wind load at this speed is 0.68 kN/m2. The highest mean power densities for Datça, Fethiye, Marmaris and Koycegiz were found to be 46.2, 1.6, 6.5 and 2.2 W/m2, respectively. Also, although Log-normal distribution exhibited a good performance i.e., lower AD (Anderson – Darling statistic (AD) values) values, Gamma distribution was found more suitable in the estimation of wind speed and power of the region.

Key Words
energy; wind speed; wind load; wind power potential; log-normal distribution; gamma distribution

Address
Ramazan Ozkan:Wind Engineering and Aerodynamic Research Center, Department of Energy Systems Engineering,
Erciyes University, 38039, Kayseri, Turkey
Faruk Sen:Mugla Sotki Koçman University, Department of Energy Systems Engineering, Kotekli, Mugla, Turkey

Abstract
India is a developing nation and heavily spends on the development of wind power plants to meet the national energy demand. The objective of this paper is to investigate wind power potential of Ennore site using wind data collected over a period of two years by three parameter Weibull distribution. The Weibull parameters are estimated using maximum likelihood, least square method and moment method and the accuracy is determined using R2 and root mean square error values. The site specific capacity factor is calculated by the mathematical model developed by three parameter Weibull distribution at different hub heights above the ground level. At last, the wind energy economic analysis is carried out using capacity factor at 30 m, 40 m and 50 m height for different wind turbine models. The analysis showed that the site has potential to install utility wind turbines to generate energy at the lowest cost per kilowatt-hour at height of 50 m. This research provides information of wind characteristics of potential sites and helps in selecting suitable wind turbine.

Key Words
three parameter Weibull distribution; Estimation of Weibull parameters; statistical tests; capacity factor; economic analysis

Address
Sukkiramathi :1Department of Mathematics, Sri Ramakrishna Engineering College, Coimbatore,India
Rajkumar R.:Department of Mathematics, Kumaraguru College of Technology, Coimbatore
Seshaiah C.V.:Department of Basic Science and Humanities, GMR Institute of Technology, Srikakulam, India

Abstract
Engineering type tropical cyclone (TC) wind field models are used to estimate TC wind hazard. Some of the models are well-calibrated using observation data, while others are not extensively compared and verified. They are all proxies to the real TC wind fields. The computational effort for their use differs. In the present study, a comparison of the predicted wind fields is presented by considering three commonly used models: the gradient wind field model, slab-resolving model, and a linear height-resolving model. These models essentially predict the horizontal wind speed at a different height. The gradient wind field model and linear height-resolving model are simple to use while the nonlinear slab-resolving model is more compute-intensive. A set of factors is estimated and recommended such that the estimated TC wind hazard by using these models becomes more consistent. The use of the models, including the developed set of factors, for estimating TC wind hazard over-water and over-land is presented by considering the historical tracks for a few sites. It is shown that the annual maximum TC wind speed can be adequately modelled by the generalized extreme value distribution.

Key Words
tropical cyclone; wind field; wind hazard; adjustment factor; simulation

Address
J.Y. Gu, C. Sheng and H.P. Hong: Department of Civil and Environmental Engineering, University of Western Ontario, 1151 Richmond St, London, Ontario N6A 5B9, Canada

Abstract
In coastal residential communities, especially along the coastline, flooding is a frequent natural hazard that impacts the area. To reduce the adverse effects of flooding, it is recommended to elevate coastal buildings to a certain safe level. However, post storm damage assessment has revealed severe damages sustained by elevated buildings' components such as roofs, walls, and floors. By elevating a structure and creating air gap underneath the floor, the wind velocity increases and the aerodynamics change. This results in varying wind loading and pressure distribution that are different from their slab on grade counterparts. To fill the current knowledge gap, a large-scale aerodynamic wind testing was conducted at the Wall of Wind experimental facility to evaluate the wind pressure distribution over the surfaces of a low-rise gable roof single-story elevated house. The study considered three different stilt heights. This paper presents the observed changes in local and area averaged peak pressure coefficients for the building surfaces of the studied cases. The aerodynamics of the elevated structures are explained. Comparisons are done with ASCE 7-16 and AS/NZS 1170.2 wind loading standards. For the floor surface, the study suggests a wind pressure zoning and pressure coefficients for each stilt height.

Key Words
aerodynamics; elevated structure; mobile home; experimental; external pressure; peak pressure; stilt; Wall of Wind

Address
Nourhan Abdelfatah:Department of Civil and Environmental Engineering, Florida International University, 10555 West Flagler Street, Miami, Florida 33174, U.S.A.
Amal Elawady,Peter Irwin and Arindam Chowdhury: Department of Civil and Environmental Engineering, Florida International University, 10555 West Flagler Street, Miami, Florida 33174, U.S.A.
/The Wall of Wind Experimental Facility, 10555 West Flagler Street, Miami, Florida 33174, U.S.A.

Abstract
In this study, the impact of roof slope on the flow characteristics over low-sloped gable roofs was investigated using steady computational fluid dynamics (CFD) simulations based on a k-ω SST turbulence model. A measurement database of the flow field over a scaled model of 15° was created using particle image velocimetry (PIV). Sensitivity analyses for the grid resolutions and turbulence models were performed. Among the three common Reynolds-averaged Navier-Stokes equations (RANS) models, the k-ω SST model exhibited a better performance, followed by the RNG model and then the realizable k- model. Next, the flow properties over the differently sloped (0°to 25°) building models were determined. It was found that the effect of roof slope on the flow characteristics was identified by changing the position and size of the separation bubbles, 15° was found to be approximately the sensitive slope at which the distribution of the separation bubbles changed significantly. Additionally, it is suggested additional attention focused on the distributions of the negative pressure on the windward surfaces (especially "5" °and "10" ° roofs) and the possible snow redistribution on the leeward surfaces.

Key Words
gable roof; low slope; CFD simulation; PIV experiment

Address
Ruizhou Cao, Zhixiang Yu Zhixiang Liu, Xiaoxiao Chen : School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China
2Wind Engineering Research Center of Southwest Jiaotong University
Fu Zhu:Zhejiang Provincial Institute of Communications Planning, Design & Research, Hangzhou 310000, China


Abstract
Proper understanding of offshore wind speed variability is of essential importance in practice, which provides useful information to a wide range of coastal and marine activities. In this paper, long-term wind speed data recorded at various offshore stations are analyzed in the framework of fractal dimension analysis. Fractal analysis is a well-established data analysis tool, which is particularly suitable to determine the complexity in time series from a quantitative point of view. The fractal dimension is estimated using the conventional box-counting method. The results suggest that the wind speed data are generally fractals, which are likely to exhibit a persistent nature. The mean fractal dimension varies from 1.31 at an offshore weather station to 1.43 at an urban station, which is mainly associated with surface roughness condition. Monthly variability of fractal dimension at offshore stations is well-defined, which often possess larger values during hotter months and lower values during winter. This is partly attributed to the effect of thermal instability. In addition, with an increase in measurement interval, the mean and minimum fractal dimension decrease, whereas the maximum and coefficient of variation increase in parallel.

Key Words
offshore wind; wind speed variability; fractal analysis; fractal dimension; box-counting; persistence

Address
Z.R. Shu:Department of Civil Engineering, University of Birmingham, Edgbaston, Birmingham, United Kingdom
P.W. Chan:Hong Kong Observatory, Kowloon, Hong Kong
Q.S. Li:Joint Research Center for Engineering Structure Disaster Prevention and Control, Guangzhou University
Y.C. He :Joint Research Center for Engineering Structure Disaster Prevention and Control, Guangzhou University
B.W. Yan:Chongqing University, Key Laboratory of New Technology for Construction of Cities in Mountain Area,
Ministry of Education, School of Civil Engineering, Chongqing

Abstract
For large-scale 5MW offshore wind turbines, the discrete equation of fluid domain and the motion equation of structural domain with geometric nonlinearity were built, the three-dimensional modeling of the blade considering fluid-structure interaction (FSI) was achieved by using Unigraphics (UG) and Geometry modules, and the numerical simulation and the analysis of the vibration characteristics for wind turbine structure under rotating effect were carried out based on ANSYS software. The results indicate that the rotating effect has an apparent effect on displacement and Von Mises stress, and the response and the distribution of displacement and Von Mises stress for the blade in direction of wingspan increase nonlinearly with the equal increase of rotational speeds. Compared with the single blade model, the blade vibration period of the whole machine model is much longer. The structural coupling effect reduces the response peak value of the blade displacement and Von Mises stress, and the increase of rotational speed enhances this coupling effect. The maximum displacement difference between two models decreases first and then increases along wingspan direction, the trend is more visible with the equal increase of rotational speed, and the boundary point with zero displacement difference moves towards the blade root. Furthermore, the Von Mises stress difference increases gradually with the increase of rotational speed and decreases nonlinearly from the blade middle to both sides. The results can provide technical reference for the safe operation and optimal design of offshore wind turbines.

Key Words
offshore wind turbines; vibration characteristics; rotating effect; structure coupling effect; geometric nonlinearity; fluid-structure interaction

Address
Jian-Ping Zhang:School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Ming-Qiang Wang, Zhen Gong and Feng-Feng Shi:College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 200090, China


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