Simulation of wind velocity time histories on long span structures modeled as non-Gaussian stochastic waves

2020 ◽  
Vol 59 ◽  
pp. 103016
Author(s):  
Haijun Zhou ◽  
George Deodatis ◽  
Michael Shields ◽  
Brett Benowitz
2014 ◽  
Vol 140 (9) ◽  
pp. 04014061 ◽  
Author(s):  
M. F. Huang ◽  
Wenjuan Lou ◽  
Xiaotao Pan ◽  
C. M. Chan ◽  
Q. S. Li

2018 ◽  
Vol 22 (6) ◽  
pp. 1255-1265 ◽  
Author(s):  
Yongle Li ◽  
Chuanjin Yu ◽  
Xingyu Chen ◽  
Xinyu Xu ◽  
Koffi Togbenou ◽  
...  

A growing number of long-span bridges are under construction across straits or through valleys, where the wind characteristics are complex and inhomogeneous. The simulation of inhomogeneous random wind velocity fields on such long-span bridges with the spectral representation method will require significant computation resources due to the time-consuming issues associated with the Cholesky decomposition of the power spectrum density matrixes. In order to improve the efficiency of the decomposition, a novel and efficient formulation of the Cholesky decomposition, called “Band-Limited Cholesky decomposition,” is proposed and corresponding simulation schemes are suggested. The key idea is to convert the coherence matrixes into band matrixes whose decomposition requires less computational cost and storage. Subsequently, each decomposed coherence matrix is also a band matrix with high sparsity. As the zero-valued elements have no contribution to the simulation calculation, the proposed method is further expedited by limiting the calculation to the non-zero elements only. The proposed methods are data-driven ones, which can be applicable broadly for simulating many complicated large-scale random wind velocity fields, especially for the inhomogeneous ones. Through the data-driven strategies presented in the study, a numerical example involving inhomogeneous random wind velocity field simulation on a long-span bridge is performed. Compared to the traditional spectral representation method, the simulation results are with high accuracy and the entire simulation procedure is about 2.5 times faster by the proposed method for the simulation of one hundred wind velocity processes.


2010 ◽  
Vol 163-167 ◽  
pp. 4142-4148
Author(s):  
Nyi Nyi Aung ◽  
Ji Hong Ye

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.


2001 ◽  
Vol 127 (4) ◽  
pp. 408-409 ◽  
Author(s):  
Chunhua Liu ◽  
Luyu Wang ◽  
Yinghong Cao

2004 ◽  
Author(s):  
Keith A. Stanney ◽  
Christopher D. Rahn

Aerostats are lighter-than-air vehicles tethered to the ground by a cable and used for broadcasting, communications, surveillance, and drug interdiction. The dynamic response of tethered aerostats subject to extreme atmospheric turbulence often dictates survivability. This paper develops a theoretical model that predicts the planar response of a tethered aerostat subject to atmospheric turbulence and simulates the response to 1000 simulated hurricane scale turbulent time histories. The aerostat dynamic model assumes the aerostat hull to be a rigid body with nonlinear fluid loading, instantaneous weathervaning for planar response, and a continuous tether. Galerkin’s method discretizes the coupled aerostat and tether partial differential equations to produce a nonlinear initial value problem that is integrated numerically given initial conditions and wind inputs. The proper orthogonal decomposition theorem generates, based on Hurricane Georges wind data, turbulent time histories that possess the sequential behavior of actual turbulence, are spectrally accurate, and have non-Gaussian density functions. The generated turbulent time histories are simulated to predict the aerostat response to severe turbulence. The resulting probability distributions for the aerostat position, pitch angle, and confluence point tension predict the aerostat behavior in high gust environments. The results uncover a worst case wind input consisting of a two-pulse vertical gust.


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