Comparison of spectral density models to simulate wind records

Bojórquez, Edén; Payán-Serrano, Omar; Reyes‐Salazar, Alfredo; Pozos, Adrián

doi:10.1007/s12205-016-1460-y

Cited by 6 publications

(13 citation statements)

References 12 publications

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“…As mentioned earlier, (21) has the advantage of simplicity, but incorporates two inconsistencies [11]: (1) the function is positive for any separation, which is in conflict with the definition of the longitudinal turbulence component with a zero mean; and (2) the function approaches unity for small frequencies, which is not true for separations of the same order of magnitude or even larger than the average size of gusts. To solve the two inconsistencies mentioned above, Krenk [13] suggested a modified exponential expression as the root-coherence function, which is given by…”

Section: Vertical Decay Constantsmentioning

confidence: 99%

“…where L c is a coherence scale parameter approximately equal to 340.2 m. For z o � 0.01 m, U(z r ) � 32 m/s, and U(z r ) � 55 m/s, Figure 2 shows a comparison among the three different models for the root-coherence function at three different points P 1 (0, 0, 10 m), P 2 (0, 0, 30 m), and P 3 (0, 0, 100 m); where different vertical decay constants ranging from 5 to 11.5 were used in the Davenport root-coherence function (21), whereas a vertical decay constant of 5 was used in the Krenk root-coherence function (22).…”

Section: Vertical Decay Constantsmentioning

confidence: 99%

“…Based on the above, different kinds of power-spectral density (PSD) functions can be used to simulate along-wind loads of line-like structures by using the SR method. However, Bojórquez et al [21] demonstrated that the PSD functions of Von Kármán, Von Kármán Harris, and Solari generated wind time series with better characteristic of turbulence intensity and scale length, compared with the models of Davenport, Kaimal, and modified Kaimal.…”

Section: Introductionmentioning

confidence: 99%

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Analytical Simulation of 3D Wind-Induced Vibrations of Rectangular Tall Buildings in Time Domain

Huergo

Hernández-Barrios

2022

Shock and Vibration

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The Monte Carlo simulation method for along-wind loads on tall buildings performed in the time and space domain may be the only analytical viable option for specific problems such as nonlinearity behavior, nonclassically damping, and detailed structural models in commercial software. However, both across-wind and torsional-wind loads due to vortex shedding are not usually simulated in time domain because the vertical decay constants are unknown or the empirical coherence functions cannot be applied in Monte Carlo simulation methods. In this paper, the spectral representation (SR) method is used to simulate in time domain the along-wind, across-wind, and torsional-wind loads on rectangular tall buildings considering the vertical correlation between the signals. The Krenk root-coherence function is used for the normalized cross-spectrum on the along-wind direction, whereas the Davenport root-coherence function is used for the other two types of wind loads. For both across-wind and torsional-wind loads, the Davenport root-coherence function was assessed at the vortex shedding frequency by changing the vertical decay constants until converge between the Davenport model and the Liang empirical coherence model was achieved. Based on a three‐dimensional model with two translational and one torsional degree of freedom for each floor, the proposed vertical decay constants were validated by comparing the elastic response between frequency domain and time domain approaches. Generally speaking, the results show that peak displacements are significantly underestimated for both across-wind and torsional directions when vertical correlation is neglected. In addition, the advantages of time domain simulation were shown by performing a nonlinear time history analysis considering a bilinear isotropic material hardening model in both translational directions.

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Section: Vertical Decay Constantsmentioning

confidence: 99%

Section: Vertical Decay Constantsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analytical Simulation of 3D Wind-Induced Vibrations of Rectangular Tall Buildings in Time Domain

Huergo

Hernández-Barrios

2022

Shock and Vibration

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“…The second expression is the von Karman-Harris expression for the frequency spectrum of the longitudinal component of wind velocity (parallel to the mean wind direction) [71,72], Table 6. In the formula, s 2 u is the variance of the turbulent wind speed, " UðzÞ is the mean wind speed at height z, L u is a length scale that characterizes the turbulent wind.…”

Section: Non-stationary Processesmentioning

confidence: 99%

Frequency‐based analysis of random fatigue loads: Models, hypotheses, reality

Benasciutti

Tovo

2018

Materialwissenschaft Werkst

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Spectral methods use analytical expressions to estimate the fatigue damage in vibrating structures subjected to uniaxial or multiaxial random stresses. They exploit the random vibration theory which idealizes the random stress as a random process characterized by a power spectral density (PSD) in the frequency‐domain. The random process is hypothesized to be stationary or non‐stationary, Gaussian or non‐Gaussian, and with narrow‐band or wide‐band power spectral density. The accuracy of analytical spectral solutions depends on whether such hypotheses are fulfilled by actual random time‐histories encountered in practice. Knowing the range of applicability of each spectral method is then of utmost importance to avoid estimation errors. After a short account on the frequency‐domain description of random processes, this paper presents a brief survey on selected spectral methods for uniaxial and multiaxial random loading. It then analyzes when the hypotheses behind each spectral method are not verified in reality, with the purpose to provide recommendations for a safe use of frequency‐based methods.

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“…The dynamic analysis of structures is complex and it requires real wind speed records, which are not available in most of the cases. Several studies suggest that the use of spectral density models are adequate to simulate the velocity field of the turbulent wind [1][2][3]. Due to the complexity of obtaining synthetic wind records and the dynamic structural response, the engineers have used non-complex methods such as the equivalent static analysis, which tries to consider the dynamic effects of the turbulent wind supported by the use of the well-known dynamic amplification factor [4].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Maximum Story Drift of MDOF Structures under Simulated Wind Loads Using Artificial Neural Networks

et al. 2017

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Abstract:The aim of this paper is to investigate the prediction of maximum story drift of Multi-Degree of Freedom (MDOF) structures subjected to dynamics wind load using Artificial Neural Networks (ANNs) through the combination of several structural and turbulent wind parameters. The maximum story drift of 1600 MDOF structures under 16 simulated wind conditions are computed with the purpose of generating the data set for the networks training with the Levenberg-Marquardt method. The Shinozuka and Newmark methods are used to simulate the turbulent wind and dynamic response, respectively. In order to optimize the computational time required for the dynamic analyses, an array format based on the Shinozuka method is presented to perform the parallel computing. Finally, it is observed that the already trained ANNs allow for predicting adequately the maximum story drift with a correlation close to 99%.

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Comparison of spectral density models to simulate wind records

Cited by 6 publications

References 12 publications

Analytical Simulation of 3D Wind-Induced Vibrations of Rectangular Tall Buildings in Time Domain

Analytical Simulation of 3D Wind-Induced Vibrations of Rectangular Tall Buildings in Time Domain

Frequency‐based analysis of random fatigue loads: Models, hypotheses, reality

Prediction of Maximum Story Drift of MDOF Structures under Simulated Wind Loads Using Artificial Neural Networks

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