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

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.

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Long-term field measurement on effects of wind speed and directional fluctuation on wind-driven cross ventilation in a mock-up building

Building and Environment ◽

10.1016/j.buildenv.2012.12.013 ◽

2013 ◽

Vol 62 ◽

pp. 1-8 ◽

Cited By ~ 18

Author(s):

J.S. Park

Keyword(s):

Wind Speed ◽

Field Measurement ◽

Term Field

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PENGARUH WINDOW TO WALL RATIO TERHADAP KENYAMANAN FISIOLOGIS DENGAN MENGGUNAKAN CFD ANSYS 14.0

Gorontalo Journal of Infrastructure and Science Engineering ◽

10.32662/gojise.v3i1.905 ◽

2020 ◽

Vol 3 (1) ◽

pp. 37

Author(s):

Rahmayanti Rahmayanti

Keyword(s):

Wind Speed ◽

Total Energy ◽

Field Measurement ◽

Air Conditioning ◽

Natural Ventilation ◽

Stokes Equations ◽

Measurement Data ◽

Field Research ◽

Navier Stokes ◽

Navier Stokes Equations

The use of air conditioning energy (AC) as an effort to remove heat in buildings reaches 30% of the total energy needed in the building. To reduce the use of energy in buildings by using natural ventilation because the system does not use mechanics. Field research has been carried out with the result that the openings at Balai Padang are unable to make occupants' comfort. Therefore, the existing openings will be given treatment by wider the existing openings which are 20%, 30%, and 40%. This study purpose to investigate the effect of WWR on histologic comfort. The numerical methodology is based on the solution of the Navier-Stokes equations, using K-epsilon RNG. Numerical results are validated with available field measurement data. The results obtained that by increasing the percentage of openings, the wind speed is also highPenggunaan energi air conditioning (AC) sebagai upaya penghapus panas di dalam bangunan mencapai 30% dari total energi yang dibutuhkan di dalam bangunan. Upaya yang dilakukan untuk mengurangi penggunaan energi di dalam bangunan yakni dengan menggunakan penghawaan alami sebagai penghapus panas karena sistemnya yang tidak menggunakan mekanis. Penelitian lapangan telah dilakukan dengan hasil bahwa bukaan yang ada di Balai Padang tidak mampu mencukupi kebutuhan kecepatan angin yang diperlukan untuk mendinginkan fisiologis penghuni. Oleh karena itu, bukaan yang ada akan diberikan perlakuan dengan memperbesar bukaan yang ada yakni 20%, 30% dan 40%. Penelitian ini bertujuan untuk mengetahui efek dari WWR terhadap kenyamanan fisiologis penghuni. Metode yang digunakan adalah eksperimental dengan menggunakan bantuan software CFD (computational Fluid Dimension) berdasarkan persamaan Navier-Stoke, menggunakan K-Epsilon RNG. Eksperimen dilakukan dengan validasi hasil pengukuran lapangan. Hasil yang didapatkan bahwa dengan menambah prosentase bukaan, kecepatan angin juga semakin besar.

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

Volume 2A: Structures, Safety, and Reliability ◽

10.1115/omae2020-18668 ◽

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.

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