The OEN mixture model for the joint distribution of wind speed and direction: A globally applicable model with physical justification

2019 ◽  
Vol 191 ◽  
pp. 141-158 ◽  
Author(s):  
Nicholas J. Cook
Keyword(s):  
Wind Speed ◽  
Mixture Model ◽  
Joint Distribution

Joint distribution of wind speed and direction in the context of field measurement

2015 ◽  
Vol 20 (5) ◽  
pp. 701-718 ◽  
Author(s):  
Hao Wang ◽  
Tianyou Tao ◽  
Teng Wu ◽  
Jianxiao Mao ◽  
Aiqun Li
Keyword(s):  
Wind Speed ◽  
Field Measurement ◽  
Joint Distribution

Bridge Design Basic Wind Speed Based on the Joint Distribution of Wind Speed and Direction

2011 ◽  
Vol 90-93 ◽  
pp. 805-812 ◽  
Author(s):  
Zheng Wei Ye ◽  
Yi Qiang Xiang
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Joint Distribution ◽  
Probability Model ◽  
Model Fitting ◽  
Frequency Function ◽  
Actual Distribution ◽  
Extreme Type ◽  
Sampling Intervals ◽  
The Impact

Based on the method of separation of wind speed and direction variable, considering the wind direction frequency function, ascending order to calculate the probability of the actual distribution of the sample, extreme type Ⅰ (Gumbel) and three parameters of extreme type Ⅱ (Frechet) and extreme type Ⅲ (Weibull) probability distribution to fit the sample, this paper has analyzed the weather station observations of 34 consecutive years of the joint distribution of wind speed and direction near to a huge bridge, gained the basic design wind speed in different directions, comparatively analyzed the impact of the sampling interval of change on the basic wind speed as well. The results shows: wind speed in different directions at the same location or different sampling intervals samples of the wind speed sample may be subject to different types of extreme value distribution, should separately fitting; different wind direction frequency of extreme wind speed occurrence and the basic wind speed there are certain differences, taking into account the joint distribution of wind speed and direction is necessary to determine the design basic wind speed of the bridge, and will be conservative without considering the joint distribution; for the same sample wind speed matrix, the shorter the sampling intervals, the optimal distribution of the higher probability model fitting precision, the smaller the basic wind speed, the more economic and reasonable the results.

Global Hierarchical Models for Wind and Wave Contours: Physical Interpretations of the Dependence Functions

2020 ◽  
Author(s):  
Andreas F. Haselsteiner ◽  
Aljoscha Sander ◽  
Jan-Hendrik Ohlendorf ◽  
Klaus-Dieter Thoben
Keyword(s):  
Wind Speed ◽  
Wave Height ◽  
Hierarchical Models ◽  
Significant Wave Height ◽  
Joint Distribution ◽  
Environmental Data ◽  
Contour Method ◽  
Dependence Function ◽  
Significant Wave ◽  
Estimated Parameters

Abstract Applications such as the design of offshore wind turbines requires the estimation of the joint distribution of variables like wind speed, wave height and wave period. The joint distribution can then be used, for example, to define design load cases using the environmental contour method. Often the joint distribution is described using so-called global hierarchical models. In these models, one variable is taken as independent and the other variables are modelled to be conditional on this variable using particular dependence functions. In this paper, we propose to use dependence functions that offer physical interpretation. We define a novel dependence function that describes how the median of the zero-up-crossing period increases with significant wave height and a novel dependence function that describes how the median significant wave height increases with wind speed. These dependence functions allow us to reason about the physical meaning, even when we extrapolate outside the range of a given sample of environmental data. In addition, we can analyze the estimated parameters of the dependence function to speculate which kind of sea dominates at a given site. We fitted statistical models with the proposed dependence functions to six datasets and analyzed the estimated parameters. Then we calculated environmental contours based on these estimated joint distributions. The environmental contours had physically reasonable shapes, even at areas that were outside the datasets that were used to fit the underlying distributions.

Design optimization on high-rise buildings considering occupant comfort reliability and joint distribution of wind speed and direction

2018 ◽  
Vol 156 ◽  
pp. 460-471 ◽  
Author(s):  
Jiyang Fu ◽  
Qingxing Zheng ◽  
Youqin Huang ◽  
Jiurong Wu ◽  
Yonglin Pi ◽  
...  
Keyword(s):  
Wind Speed ◽  
Design Optimization ◽  
Joint Distribution ◽  
High Rise ◽  
Occupant Comfort

scholarly journals Calculation of Joint Return Period for Connected Edge Data

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 300 ◽  
Author(s):  
Guilin Liu ◽  
Baiyu Chen ◽  
Zhikang Gao ◽  
Hanliang Fu ◽  
Song Jiang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Correlation Analysis ◽  
Wave Height ◽  
Joint Distribution ◽  
Measured Data ◽  
Two Dimensional ◽  
Ocean Engineering ◽  
Extreme Wave ◽  
Correlation Measure ◽  
Copula Functions

For better displaying the statistical properties of measured data, it is particularly important to select a suitable multivariate joint distribution model in ocean engineering. According to the characteristics and properties of Copula functions and the correlation analysis of measured data, the nonlinear relationship between random variables can be captured. Additionally, the models based on the Copula theory have more general applicability. A series of correlation measure index, derived from Copula functions, can expand the correlation measure range among variables. In this paper, by means of the correlation analysis between the annual extreme wave height and the corresponding wind speed, their joint distribution models were studied. The newly established two-dimensional joint distribution functions of the extreme wave height and the corresponding wind speed were compared with the existing two-dimensional joint distributions.

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