3rd-order Spectral Representation Method: Simulation of multi-dimensional random fields and ergodic multi-variate random processes with fast Fourier transform implementation

2021 ◽  
pp. 103128
Author(s):  
Lohit Vandanapu ◽  
Michael D. Shields
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Random Fields ◽  
Spectral Representation ◽  
Random Processes ◽  
Spectral Representation Method ◽  
Representation Method

An efficient Cholesky decomposition and applications for the simulation of large-scale random wind velocity fields

2018 ◽  
Vol 22 (6) ◽  
pp. 1255-1265 ◽  
Author(s):  
Yongle Li ◽  
Chuanjin Yu ◽  
Xingyu Chen ◽  
Xinyu Xu ◽  
Koffi Togbenou ◽  
...  
Keyword(s):  
Wind Velocity ◽  
Large Scale ◽  
Spectral Representation ◽  
Cholesky Decomposition ◽  
Velocity Fields ◽  
Data Driven ◽  
Long Span Bridges ◽  
Long Span ◽  
Spectral Representation Method ◽  
Representation Method

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.

Comparison of the Spectral Representation Method to Simulate Spatially Variable Ground Motions

2013 ◽  
Vol 18 (3) ◽  
pp. 458-475 ◽  
Author(s):  
Yongxin Wu ◽  
Yufeng Gao ◽  
Dayong Li ◽  
Tugen Feng ◽  
Ali H. Mahfouz
Keyword(s):  
Spectral Representation ◽  
Ground Motions ◽  
Spectral Representation Method ◽  
Representation Method

scholarly journals Generating fractal rough surfaces with the spectral representation method

2021 ◽  
pp. 135065012110496
Author(s):  
Yuechang Wang ◽  
Abdullah Azam ◽  
Mark CT Wilson ◽  
Anne Neville ◽  
Ardian Morina
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Spectral Representation ◽  
Rough Surfaces ◽  
Filtering Method ◽  
Fractal Surfaces ◽  
Spectral Representation Method ◽  
Power Spectral ◽  
Representation Method ◽  
Non Gaussian

The application of the spectral representation method in generating Gaussian and non-Gaussian fractal rough surfaces is studied in this work. The characteristics of fractal rough surfaces simulated by the spectral representation method and the conventional Fast Fourier transform filtering method are compared. Furthermore, the fractal rough surfaces simulated by these two methods are compared in the simulation of contact and lubrication problems. Next, the influence of low and high cutoff frequencies on the normality of the simulated Gaussian fractal rough surfaces is investigated with roll-off power spectral density and single power-law power spectral density. Finally, a simple approximation method to generate non-Gaussian fractal rough surfaces is proposed by combining the spectral representation method and the Johnson translator system. Based on the simulation results, the current work gives recommendations on using the spectral representation method and the Fast Fourier transform filtering method to generate fractal surfaces and suggestions on selecting the low cutoff frequency of the power-law power spectral density. Furthermore, the results show that the proposed approximation method can be a choice to generate non-Gaussian fractal surfaces when the accuracy requirements are not high. The MATLAB codes for generating Gaussian and non-Gaussian fractal rough surfaces are provided.

scholarly journals Non-Stationary Turbulent Wind Field Simulation of Long-Span Bridges Using the Updated Non-Negative Matrix Factorization-Based Spectral Representation Method

2019 ◽  
Vol 9 (24) ◽  
pp. 5506
Author(s):  
Zidong Xu ◽  
Hao Wang ◽  
Han Zhang ◽  
Kaiyong Zhao ◽  
Hui Gao ◽  
...  
Keyword(s):  
Wind Field ◽  
Spectral Representation ◽  
Wind Fields ◽  
Turbulent Wind ◽  
Long Span Bridges ◽  
Long Span ◽  
Spectral Representation Method ◽  
Representation Method ◽  
Turbulent Wind Field ◽  
Non Negative Matrix Factorization

Numerical simulation of the turbulent wind field on long-span bridges is an important task in structural buffeting analysis when it comes to the system non-linearity. As for non-stationary extreme wind events, some efforts have been paid to update the classic spectral representation method (SRM) and the fast Fourier transform (FFT) has been introduced to improve the computational efficiency. Here, the non-negative matrix factorization-based FFT-aided SRM has been updated to generate not only the horizontal non-stationary turbulent wind field, but also the vertical one. Specifically, the evolutionary power spectral density (EPSD) is estimated to characterize the non-stationary feature of the field-measured wind data during Typhoon Wipha at the Runyang Suspension Bridge (RSB) site. The coherence function considering the phase angles is utilized to generate the turbulent wind fields for towers. The simulation accuracy is validated by comparing the simulated and target auto-/cross-correlation functions. Results show that the updated method performs well in generating the non-stationary turbulent wind field. The obtained wind fields will provide the research basis for analyzing the non-stationary buffeting behavior of the RSB and other wind-sensitive structures in adjacent regions.

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