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CONTENTS
Volume 23, Number 4, October 2016
 

Abstract
Synchronous multi-pressure measurements were carried out with relatively long time duration for a double-layer reticulated shell roof model in the atmospheric boundary layer wind tunnel. Since the long roof is open at two ends for the storage of coal piles, three different testing cases were considered as the empty roof without coal piles (Case A), half coal piles inside (Case B) and full coal piles inside (Case C). Based on the wind tunnel test results, non-Gaussian time-dependent statistics of net wind pressure on the shell roof were quantified in terms of skewness and kurtosis. It was found that the direct statistical estimation of high-order moments and peak factors is quite sensitive to the duration of wind pressure time-history data. The maximum value of COVs (Coefficients of variations) of high-order moments is up to 1.05 for several measured pressure processes. The Mixture distribution models are proposed for better modeling the distribution of a parent pressure process. With the aid of mixture parent distribution models, the existing translated-peak-process (TPP) method has been revised and improved in the estimation of non-Gaussian peak factors. Finally, non-Gaussian peak factors of wind pressure, particularly for those observed hardening pressure process, were calculated by employing various state-of-the-art methods and compared to the direct statistical analysis of the measured long-duration wind pressure data. The estimated non-Gaussian peak factors for a hardening pressure process at the leading edge of the roof were varying from 3.6229, 3.3693 to 3.3416 corresponding to three different cases of A, B and C.

Key Words
wind pressure; mixture distribution; non-Gaussian; peak factor; wind tunnel tests

Address
M.F. Huang, Song Huang, He Feng and Wenjuan Lou: Institute of Structural Engineering, Zhejiang University, Hangzhou 310058, China

Abstract
The numerous benefits of Savonius turbine such as simple in structure, has appropriate self-start ability, relatively low operating velocity, water acceptance from any direction and low environmental impact have generated interests among researchers. However, it suffers from a lower efficiency compared to other types of water turbine. To improve its performance, parameters such flow pattern, pressure and velocity in different conditions must be analyzed. For this purpose, a detailed description on the flow field of various types of Savonius rotors is required. This article presents a numerical study on a nonlinear two-dimensional flow over a classic Savonius type rotor and a Benesh type rotor. In this experiment, sliding mesh was used for solving the motion of the bucket. The unsteady Reynolds averaged Navier-Stokes equations were solved for velocity and pressure coupling by using the SIMPLE (Semi-Implicit Method for Pressure linked Equations) algorithm. Other than that, the turbulence model using k-e standard obtained good results. This simulation demonstrated the method of the flow field characteristics, the behavior of velocity vectors and pressure distribution contours in and around the areas of the bucket.

Key Words
CFD; angle of attack; turbulence model; structure of profile

Address
A. Reza Hassanzadeh and M. Arif Ismail: Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia
Omar bin Yaakob: aculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia;
Marine Technology Centre, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia
Yasser M. Ahmed: Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia;
Faculty of Engineering, Alexandria University, Egypt

Abstract
Taipei 101 Tower, which has 101 stories with height of 508 m, is located in Taipei where typhoons and earthquakes commonly occur. It is currently the second tallest building in the world. Therefore, the dynamic performance of the super-tall building under strong wind actions requires particular attentions. In this study, Large Eddy Simulation (LES) integrated with a new inflow turbulence generator and a new sub-grid scale (SGS) model was conducted to simulate the wind loads on the super-tall building. Three-dimensional finite element model of Taipei 101 Tower was established and used to evaluate the wind-induced responses of the high-rise structure based on the simulated wind forces. The numerical results were found to be consistent with those measured from a vibration monitoring system installed in the building. Furthermore, the equivalent static wind loads on the building, which were computed by the time-domain and frequency-domain analysis, respectively, were in satisfactory agreement with available wind tunnel testing results. It has been demonstrated through the validation studies that the numerical framework presented in this paper, including the recommended SGS model, the inflow turbulence generation technique and associated numerical treatments, is a useful tool for evaluation of the wind loads and wind-induced responses of tall buildings.

Key Words
tall building; computational fluid dynamics (CFD); large eddy simulation (LES); finite element method (FEM); wind load; wind-induced response; full-scale measurement; wind tunnel test

Address
C.L. Lu and L.H. Zhi: College of Civil Engineering, Hunan University, Changsha 410082, China
Q.S. Li:Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong
S.H. Huang: School of Engineering Science, University of Science and Technology of China,Hefei, 230026, China
Alex Y. Tuan: Department of Civil Engineering, Tamkang University, Tamsui, Taiwan
Sheng-chung Su: Central Weather Bureau, Taipei, Taiwan



Abstract
Nowadays, wind energy is the most rapidly developing technology and energy source and it is reusable. Due to its cleanliness and reusability, there have been rapid developments made on transferring the wind energy systems to electric energy systems. Converting the wind energy to electrical energy can be done only with the wind turbines. So installing a wind turbine depends on the wind speed at that location. The expected wind power can be estimated using a perfect probability distribution. In this paper Weibull and Weibull distribution with multiple parameters has been used in deriving the mathematical expression for estimating the wind power. Statistically the parameters of Weibull and Weibull distribution are estimated using the maximum likelihood techniques. We derive a probability distribution for the power output of a wind turbine with given rated wind speeds for the regions where the wind speed histograms present a bimodal pdf and compute the first order moment of this distribution.

Key Words
Weibull & Weibull distribution; maximum likelihood method; capacity factor; wind power; V_rated

Address
Seshaiah C.V. Chalamcharla and Indhumathy D. Doraiswamy: Department of Mathematics, Sri Ramakrishna Engineering College, Coimbatore, India

Abstract
The influence mechanism of mean value components, noted as P0, on POD applications for complete random fields PC(t) and fluctuating random fields PF(t) are illustrated mathematically. The critical philosophy of the illustration is introduction of a new matrix, defined as the correlation function matrix of P0, which connect the correlation function matrix of PC(t) and PF(t), and their POD results. Then, POD analyses for several different wind pressure fields were presented comparatively as validation. It\'s inevitable mathematically that the first eigenmode of PC(t) resembles the distribution of P0 and the first eigenvalue of PC(t) is close to the energy of P0, due to similarity of the correlation function matrixs of PC(t) and P0. However, the viewpoint is not rigorous mathematically that the first mode represents the mean pressure and the following modes represent the fluctuating pressure when PC(t) are employed in POD application. When PC(t) are employed, POD results of all modes would be distorted by the mean value components, and it\'s impossible to identify P0 and PF(t) separately. Consequently, characteristics of the fluctuating component, which is always the primary concern in wind pressure field analysis, can only be precisely identified with P0 excluded in POD.

Key Words
POD; influence mechanism; mean value components; complete fields; fluctuating fields; eigenmodes; eigenvalues

Address
Jun-Feng Zhang and Huai Chen: School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Yao-Jun Ge and Lin Zhao: State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China


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