Modeling of wind-induced pressure fluctuations on roofs of low-rise buildings

1999 ◽  
Vol 26 (4) ◽  
pp. 453-467 ◽  
Author(s):  
K Suresh Kumar
Keyword(s):  
Time Series ◽  
Pressure Fluctuations ◽  
Wind Pressure ◽  
Fourier Amplitude ◽  
Simulation Methodology ◽  
Fatigue Design ◽  
Building Modeling ◽  
Wind Pressures ◽  
Non Gaussian ◽  
Simulation Scheme

A systematic study on the modeling of wind-induced pressures on low building roofs with application to extreme value and fatigue analysis is described in this paper. Extensive wind tunnel measurements form a basis to carry out the modeling. Based on the Fourier representation of time series, a general approach for simulating Gaussian as well as non-Gaussian wind pressure fluctuations has been presented. Both Fourier amplitude and phase required for the simulations are modeled individually. A simple stochastic model is proposed for the generation of Fourier phase of non-Gaussian time series. An empirical model has been suggested for the synthetic generation of normalized spectra; synthetic spectra are utilized for the generation of Fourier amplitude part. Towards the generalization of the simulation scheme, the standard spectral shapes associated with various zones of each roof and their parameters are established. The efficiency of this simulation methodology is illustrated with several examples. Applications of the simulation methodology have also been discussed. The established simulation scheme can be used to generate fluctuating wind pressures on low building roofs in a generic fashion not only for the evaluation of extreme pressures but also for fatigue design purposes.Key words: low-rise building, modeling, roofs, wind pressure.

Simulation of Non-Gaussian Wind Pressure Fields on Domed Structures

2010 ◽  
Vol 163-167 ◽  
pp. 4142-4148
Author(s):  
Nyi Nyi Aung ◽  
Ji Hong Ye
Keyword(s):  
Wind Tunnel ◽  
Pressure Fluctuations ◽  
Wind Pressure ◽  
Cumulative Distribution ◽  
Mapping Technique ◽  
Power Spectral ◽  
Time Histories ◽  
Gaussian Simulation ◽  
Gaussian Sample ◽  
Non Gaussian

Wind pressure fluctuations acting on space structures are important for prediction of peak pressure values and for fatigue design purpose. Collection of several time histories of pressure fluctuations by traditional wind tunnel measurements is time consuming and expensive. Thus, a study on developing new wind pressure simulation technique on domed structures is carried out. An efficient, flexible and easily applied stochastic non-Gaussian simulation algorithm is presented using a cumulative distribution function (CDF) mapping technique that converges to a desired target power spectral density. This method first generates Gaussian sample fields using wavelet bases and then maps them into non-Gaussian sample fields with the aid of an iterative procedure. Results from this technique are presented and compared with those from the wind tunnel experiments. The advantages and limitations of this method are also discussed.

scholarly journals On the Use of the Cubic Translation to Model Bimodal Wind Pressures

2019 ◽  
Vol 15 (2) ◽  
pp. 20-32
Author(s):  
François Rigo ◽  
Thomas Andrianne ◽  
Vincent Denoël
Keyword(s):  
Bimodal Distribution ◽  
Wind Pressure ◽  
Wind Engineering ◽  
Pressure Data ◽  
Direct Evaluation ◽  
Translation Model ◽  
Background Turbulence ◽  
Wind Pressures ◽  
Non Gaussian ◽  
Flat Roof

Abstract The cubic translation model is a well know tool in wind engineering, which provides a mathematical description of a non-Gaussian pressure as a cubic transformation of a Gaussian process. This simple model is widely used in practice since it offers a direct evaluation of the peak factors as a function of the statistics of the wind pressure data. This transformation is rather versatile but limited to processes which are said to be in the monotonic region. For processes falling outside this domain, this paper describes an alternative which is based on the physics of the wind flow. First, it is shown, with a classical example of a flow involving corner vortices on a flat roof, that the pressure data which does not meet the monotonic criterion is in fact associated with a bimodal distribution. Then, the proposed approach is to decompose this data into the two governing modes (slow background turbulence and fast corner vortices) and apply the usual translation model to each of them.

Synthesis of non-Gaussian wind pressure time series on low building roofs

1999 ◽  
Vol 21 (12) ◽  
pp. 1086-1100 ◽  
Author(s):  
K Suresh Kumar ◽  
T Stathopoulos
Keyword(s):  
Time Series ◽  
Wind Pressure ◽  
Pressure Time ◽  
Non Gaussian

scholarly journals Further Analysis of Non-Gaussian Wind Pressure Peak Factor Calculation

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Tao Ye ◽  
Ledong Zhu ◽  
Zhongxu Tan ◽  
Lanlan Li
Keyword(s):  
Time Series ◽  
Pressure Coefficient ◽  
Wind Pressure ◽  
Time Interval ◽  
Peak Factor ◽  
Pressure Time ◽  
Wind Pressure Coefficient ◽  
Non Gaussian ◽  
The Relationship ◽  
Guarantee Rate

The wind pressure time history of high-rise building cladding is mostly non-Gaussian distribution, and there is a one-to-one correspondence between a specified guarantee rate and its corresponding peak factor. A stepwise search method for calculating the peak factor of non-Gaussian wind pressure and a gradual independent segmentation method for extracting independent peak values have been proposed to determine the relationship accurately in the previous study. Based on the given experiment and calculation results in the existing research results, more analysis can be given to enrich the study on this topic. In this paper, some characteristics of wind pressure coefficient time series in time and frequency domain are analysed. Based on the basic theory of fractal, the R/S analysis of wind pressure time series is made, and the fractal characteristics of wind pressure coefficient time series are explained. Based on the statistical theory, the relationship characteristics between high-order statistics and peak factors are studied. The correlation between the guarantee rate and the corresponding peak factor is analysed, and the guarantee rates calculated by the Davenport peak factor method are evaluated. The power spectrum characteristics of fluctuating wind pressure are analysed and the relationship between turbulence characteristic frequency and optimal observation time interval is discussed.

Estimation of extremes of non-Gaussian wind pressure on building roof: Sampling error in moment-based translation process model with no monotonic limit

2019 ◽  
Vol 23 (4) ◽  
pp. 810-826 ◽  
Author(s):  
Fengbo Wu ◽  
Min Liu ◽  
Qingshan Yang ◽  
Liuliu Peng
Keyword(s):  
Hermite Polynomial ◽  
Sampling Error ◽  
Polynomial Model ◽  
Wind Pressure ◽  
Transformation Model ◽  
Sampling Errors ◽  
Translation Process ◽  
Building Roof ◽  
Wind Pressures ◽  
Non Gaussian

Estimation of extremes of non-Gaussian wind pressure on building roof is necessary for cladding design. When limited length of non-Gaussian wind pressure is used for calculation, the estimated extreme involves sampling error. The moment-based Hermite polynomial model is extensively applied for estimation of extreme wind pressure due to the straightforwardness and accuracy, however, Hermite polynomial model has a monotonic limit resulting in a restricted application region of skewness and kurtosis combination. However, another two moment-based translation process models with no monotonic limit including Johnson transformation model and piecewise Hermite polynomial model have attracted some attention as these two models can be applied to a broader region of skewness and kurtosis combination. The sampling error in estimation of extremes of non-Gaussian wind pressure on building roof by Hermite polynomial model is proposed in the literature recently. Nevertheless, the sampling errors in Johnson transformation model and piecewise Hermite polynomial model have not been addressed. In this study, sampling errors in estimation of extremes of non-Gaussian wind pressures by Johnson transformation model are investigated. Formulations for estimating sampling errors of newly defined statistical moments and subsequent extremes in piecewise Hermite polynomial model are presented. The performance of sampling errors in Hermite polynomial model, Johnson transformation model, and piecewise Hermite polynomial model are finally compared with each other. Based on very long wind pressures from wind tunnel tests, it is shown that the sampling error of minimum wind pressure (suction) in Hermite polynomial model is generally the smallest compared to Johnson transformation model and piecewise Hermite polynomial model, while that of maximum wind pressure in piecewise Hermite polynomial model seems to be the smallest.

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