scholarly journals Modeling directional distributions of wind data in the United Arab Emirates at different elevations

2021 ◽  
Vol 14 (9) ◽  
Author(s):  
Aishah Al Yammahi ◽  
Prashanth R. Marpu ◽  
Taha B. M. J. Ouarda
Keyword(s):  
Wind Speed ◽  
United Arab Emirates ◽  
Wind Direction ◽  
Least Square Method ◽  
Least Square ◽  
Distribution Model ◽  
Long Term Effects ◽  
Energy Potential ◽  
Engineering Structures ◽  
Von Mises

AbstractModeling wind speed and direction are crucial in several applications such as the estimation of wind energy potential and the study of the long-term effects on engineering structures. While there have been several studies on modeling wind speed, studies on modeling wind direction are limited. In this work, we use a mixture of von Mises distributions to model wind direction. Finite mixtures of von Mises (FMVM) distributions are used to model wind directions at two sites in the United Arab Emirates. The parameters of the FMVM distribution are estimated using the least square method. The results of the research show that the FMVM is the best suited distribution model to fit wind direction at these two sites, compared to other distributions commonly used to model wind direction.

scholarly journals Influence of Wind Speed, Wind Direction and Turbulence Model for Bridge Hanger: A Case Study

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1633
Author(s):  
Yang Ding ◽  
Shuang-Xi Zhou ◽  
Yong-Qi Wei ◽  
Tong-Lin Yang ◽  
Jing-Liang Dong
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Wind Field ◽  
Solid Mechanics ◽  
Von Mises Stress ◽  
Long Span Bridges ◽  
High Rise ◽  
Long Span ◽  
Cfd Models ◽  
Von Mises

Wind field (e.g., wind speed and wind direction) has the characteristics of randomness, nonlinearity, and uncertainty, which can be critical and even destructive on a long-span bridge’s hangers, such as vortex shedding, galloping, and flutter. Nowadays, the finite element method is widely used for model calculation, such as in long-span bridges and high-rise buildings. In this study, the investigated bridge hanger model was established by COMSOL Multiphysics software, which can calculate fluid dynamics (CFD), solid mechanics, and fluid–solid coupling. Regarding the wind field of bridge hangers, the influence of CFD models, wind speed, and wind direction are investigated. Specifically, the bridge hanger structure has symmetrical characteristics, which can greatly reduce the calculation efficiency. Furthermore, the von Mises stress of bridge hangers is calculated based on fluid–solid coupling.

scholarly journals Wind Power potential in Kigali and Western provinces of Rwanda

2015 ◽  
Vol 2 (1) ◽  
pp. 25-36
Author(s):  
Otieno Fredrick Onyango ◽  
Sibomana Gaston ◽  
Elie Kabende ◽  
Felix Nkunda ◽  
Jared Hera Ndeda
Keyword(s):  
Wind Speed ◽  
Probability Density ◽  
Wind Power ◽  
Wind Direction ◽  
Small Scale ◽  
Energy Potential ◽  
Wind Speeds ◽  
Mean Wind Speed ◽  
Wind Energy Potential ◽  
Low Wind Speeds

Wind speed and wind direction are the most important characteristics for assessing wind energy potential of a location using suitable probability density functions. In this investigation, a hybrid-Weibull probability density function was used to analyze data from Kigali, Gisenyi, and Kamembe stations. Kigali is located in the Eastern side of Rwanda while Gisenyi and Kamembe are to the West. On-site hourly wind speed and wind direction data for the year 2007 were analyzed using Matlab programmes. The annual mean wind speed for Kigali, Gisenyi, and Kamembe sites were determined as 2.36m/s, 2.95m/s and 2.97m/s respectively, while corresponding dominant wind directions for the stations were ,  and  respectively. The annual wind power density of Kigali was found to be  while the power densities for Gisenyi and Kamembe were determined as and . It is clear, the investigated regions are dominated by low wind speeds thus are suitable for small-scale wind power generation especially at Kamembe site.

scholarly journals A New Distribution for Modeling Lifetime Data with Different Methods of Estimation and Censored Regression Modeling

2020 ◽  
Vol 8 (2) ◽  
pp. 610-630 ◽  
Author(s):  
Mohamed Ibrahim ◽  
Emrah Altun EA ◽  
Haitham M. Yousof
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Estimation Method ◽  
Least Square Method ◽  
Least Square ◽  
Likelihood Method ◽  
Survival Times ◽  
New Model ◽  
Von Mises ◽  
Bootstrapping Method

In this paper and after introducing a new model along with its properties, we estimate the unknown parameter of the new model using the Maximum likelihood method, Cram er-Von-Mises method, bootstrapping method, least square method and weighted least square method. We assess the performance of all estimation method employing simulations. All methods perform well but bootstrapping method is the best in modeling relief times whereas the maximum likelihood method is the best in modeling survival times. Censored data modeling with covariates is addressed along with the index plot of the modified deviance residuals and its Q-Q plot.

Characterizing Wind Speed and Air Density for Wind Energy Estimation

2009 ◽  
Author(s):  
Xiuli Qu ◽  
Jing Shi
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Density Distribution ◽  
North Dakota ◽  
Distribution Model ◽  
Energy Potential ◽  
Energy Estimation ◽  
Air Density ◽  
Wind Energy Potential

Wind energy is the fastest growing renewable energy source in the past decade. To estimate the wind energy potential for a specific site, the long-term wind data need to be analyzed and accurately modeled. Wind speed and air density are the two key parameters for wind energy potential calculation, and their characteristics determine the long-term wind energy estimation. In this paper, we analyze the wind speed and air density data obtained from two observation sites in North Dakota and Colorado, and the variations of wind speed and air density in long term are demonstrated. We obtain univariate statistical distributions for the two parameters respectively. Excellent fitting performance can be achieved for wind speed for both sites using conventional univariate probability distribution functions, but fitting air density distribution for the North Dakota site appears to be less accurate. Furthermore, we adopt Farlie-Gumbel-Morgenstern approach to construct joint bivariate distributions to describe wind speed and air density simultaneously. Overall, satisfactory goodness-of-fit values are achieved with the joint distribution models, but the fitting performance is slightly worse compared with the univariate distributions. Further research is needed to improve air density distribution model and the joint bivariate distribution model for wind speed and air density.

scholarly journals Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2158 ◽  
Author(s):  
Mekalathur B Hemanth Kumar ◽  
Saravanan Balasubramaniyan ◽  
Sanjeevikumar Padmanaban ◽  
Jens Bo Holm-Nielsen
Keyword(s):  
Wind Speed ◽  
Frequency Distribution ◽  
Wind Direction ◽  
Optimization Method ◽  
Wind Data ◽  
Gray Wolf ◽  
Energy Potential ◽  
Weibull Parameters ◽  
Wind Speeds ◽  
Wind Potential

In this paper the multiverse optimization (MVO) was used for estimating Weibull parameters. These parameters were further used to analyze the wind data available at a particular location in the Tirumala region in India. An effort had been made to study the wind potential in this region (13°41′30.4″ N 79°21′34.4″ E) using the Weibull parameters. The wind data had been measured at this site for a period of six years from January 2012 to December 2017. The analysis was performed at two different hub heights of 10 m and 65 m. The frequency distribution of wind speed, wind direction and mean wind speeds were calculated for this region. To compare the performance of the MVO, gray wolf optimizer (GWO), moth flame optimization (MFO), particle swarm optimization (PSO) and other numerical methods were considered. From this study, the performance had been analyzed and the best results were obtained by using the MVO with an error less than one. Along with the Weibull frequency distribution for the selected region, wind direction and wind speed were also provided. From the analysis, wind speed from 2 m/s to 10 m/s was present in sector 260–280° and wind from 0–4 m/s were present in sector 170–180° of the Tirumala region in India.

Multivariate and Multimodal Wind Distribution Model Based on Kernel Density Estimation

2011 ◽  
Author(s):  
Jie Zhang ◽  
Souma Chowdhury ◽  
Achille Messac ◽  
Luciano Castillo
Keyword(s):  
Wind Speed ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Wind Direction ◽  
Wind Farm ◽  
Kernel Density ◽  
Distribution Model ◽  
Wind Condition ◽  
Air Density ◽  
Wind Distribution

This paper presents a new method to accurately characterize and predict the annual variation of wind conditions. Estimation of the distribution of wind conditions is necessary (i) to quantify the available energy (power density) at a site, and (ii) to design optimal wind farm configurations. We develop a smooth multivariate wind distribution model that captures the coupled variation of wind speed, wind direction, and air density. The wind distribution model developed in this paper also avoids the limiting assumption of unimodality of the distribution. This method, which we call the Multivariate and Multimodal Wind distribution (MMWD) model, is an evolution from existing wind distribution modeling techniques. Multivariate kernel density estimation, a standard non-parametric approach to estimate the probability density function of random variables, is adopted for this purpose. The MMWD technique is successfully applied to model (i) the distribution of wind speed (univariate); (ii) the distribution of wind speed and wind direction (bivariate); and (iii) the distribution of wind speed, wind direction, and air density (multivariate). The latter is a novel contribution of this paper, while the former offers opportunities for validation. Ten-year recorded wind data, obtained from the North Dakota Agricultural Weather Network (NDAWN), is used in this paper. We found the coupled distribution to be multimodal. A strong correlation among the wind condition parameters was also observed.

Reliability Evaluation on Machining Center Based on Three-Parameter Weibull Distribution

2012 ◽  
Vol 430-432 ◽  
pp. 1645-1649 ◽  
Author(s):  
Gong Chang Ren ◽  
Zhi Wei Yang ◽  
Bo Min Meng
Keyword(s):  
Weibull Distribution ◽  
Goodness Of Fit ◽  
Location Parameter ◽  
Least Square Method ◽  
Optimization Method ◽  
Reliability Evaluation ◽  
Least Square ◽  
Distribution Model ◽  
Adaptive Genetic Algorithm ◽  
Machining Center

In order to improve the model accuracy of reliability evaluation, the Three-Parameter Weibull Distribution model of time between fault was established by introducing location parameters. The correlation coefficient optimization method based on the adaptive genetic algorithm was firstly applied to estimate the location parameter of the Three-Parameter Weibull Distribution. Shape parameter and scale parameter were obtained by the least square method. The time between failures of these series machining center submitting to three-parameter weibull Distribution was checked by the test hypothesis of goodness-of-fit distribution. Finally, the machining center was carried out reliability evaluation based on the Three-Parameter Weibull Distribution model.

Vessel Traffic Flow Distribution Model of Bridge Area Waterway in the Middle Stream of Yangtze River

2014 ◽  
Vol 551 ◽  
pp. 127-133 ◽  
Author(s):  
Hai Qiang Hou ◽  
Yi Cheng Li ◽  
Wei He ◽  
Xing Long Liu
Keyword(s):  
Traffic Flow ◽  
Yangtze River ◽  
Flow Distribution ◽  
Least Square Method ◽  
Least Square ◽  
Distribution Model ◽  
Distribution Rule ◽  
Operating Factor ◽  
Safety Operation ◽  
Vessel Speed

Traffic flow data collection in Wuhan Yangtze River Bridge, locates in the middle of Yangtze River, is help for analysis of the waterway operating factor, which in return increases the capacity in the bridge area waterways and promotes vessel collision avoidance. Traffic flow parameters like vessel speed, arrival time, vessel clearance were collected by manual observation. Through analysis of vessel speed and vessel clearance distribution rule, the vessel speed-clearance model is established based on least square method. Real-time density of ships on navigation, channel utilization and ship safety operation area can be obtained by study on the relationship between speed and shipping space..

A wavelet transform and substructure algorithm for tracking the abrupt stiffness degradation of shear structure

2018 ◽  
Vol 22 (5) ◽  
pp. 1136-1148 ◽  
Author(s):  
Chao Wang ◽  
Demi Ai ◽  
Wei-Xin Ren
Keyword(s):  
Wavelet Transform ◽  
Degrees Of Freedom ◽  
Least Square Method ◽  
Least Square ◽  
Stiffness Degradation ◽  
Discrete Wavelet ◽  
Time Varying ◽  
Engineering Structures ◽  
Shear Structure ◽  
Stiffness And Damping

Time-varying parameter identification is an important research topic for structural health monitoring, performance evaluation, damage diagnosis, and maintenance. Practical civil engineering structures usually contain multiple degrees of freedom; however, damage often locally occurs. In this study, a discrete wavelet transform and substructure algorithm is presented for tracking the abrupt stiffness degradation of shear structures. A substructure model is built by the extraction of the local structure which may contain damaged region. Time-varying stiffness and damping are expanded into multi-scales using discrete wavelet analysis. An optimization method based on Akaike information criterion is introduced to select the decomposition scale. The expanded scale coefficients are evaluated using least square method, then the original time-varying stiffness or damping parameter is identified by reconstructing from the scale coefficients. To validate the proposed method, a numerical example of seven-story shear structure with time-varying stiffness and damping is proposed. Experiment for a three-story shear-type structure with abrupt stiffness degradation is also tested in the laboratory. Both numerical and experimental results indicate that the proposed method can effectively identify the abrupt degradation of stiffness parameter with a satisfactory accuracy.

scholarly journals Performance Characterization of the UAV Chemical Application Based on CFD Simulation

Agronomy ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 308 ◽  
Author(s):  
Hang Zhu ◽  
Hongze Li ◽  
Cui Zhang ◽  
Junxing Li ◽  
Huihui Zhang
Keyword(s):  
Wind Speed ◽  
Correlation Coefficient ◽  
Cfd Simulation ◽  
Least Square Method ◽  
Least Square ◽  
Universal Method ◽  
Discrete Phase Model ◽  
Chemical Application ◽  
Distance Model ◽  
Discrete Phase

Battery-powered multi-rotor UAVs (Unmanned Aerial Vehicles) have been employed as chemical applicators in agriculture for small fields in China. Major challenges in spraying include reducing the influence of environmental factors and appropriate chemical use. Therefore, the objective of this research was to obtain the law of droplet drift and deposition by CFD (Computational Fluid Dynamics), a universal method to solve the fluid problem using a discretization mathematical method. DPM (Discrete Phase Model) was taken to simulate the motion of droplet particles since it is an appropriate way to simulate discrete phase in flow field and can track particle trajectory. The figure of deposition concentration and trace of droplet drift was obtained by controlling the variables of wind speed, pressure, and spray height. The droplet drifting models influenced by different factors were established by least square method after analysis of drift quantity to get the equation of drift quantity and safe distance. The relationship model, Yi(m), between three dependent variables, wind speed Xw(m s−1), pressure Xp(MPa) and spray height Xh(m), are listed as follows: The edge drift distance model was Y1 = 0.887Xw + 0.550Xp + 1.552Xh − 3.906 and the correlation coefficient (R2) was 0.837; the center drift distance model was Y2 = 0.167Xw + 0.085Xp + 0.308Xh − 0.667 and the correlation coefficient (R2) was 0.774; the overlap width model was Y3 = 0.692xw + 0.529xp + 1.469xh − 3.374 and the correlation coefficient (R2) was 0.795. For the three models, the coefficients of the three variables were all positive, indicating that the three factors were all positively correlated with edge drift distance, center drift distance, and overlap width. The results of this study can provide theoretical support for improving the spray quality of UAV and reducing the drift of droplets.

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