Monte Carlo simulation of wind velocity fields on complex structures

Carassale, Luigi; Solari, Giovanni

doi:10.1016/j.jweia.2006.01.004

Cited by 99 publications

(39 citation statements)

References 11 publications

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“…Several models were developed in the literature to give an analytical representation to S uj uj (x, n), for j = 1, 2, 3. Here, the model by Solari and Piccardo [23] is adopted for this purpose and Coh vivj (x, x , n) is expressed by means of the classic exponential law [11]. The imaginary part of the CPSD (called quadrature spectrum) introduces a time lag between the simulated velocities, which may become significant for points placed in the along-wind direction x 1 .…”

Section: Stochastic Wind Modelingmentioning

confidence: 99%

“…, N. The complete characterization of the process u(t) is given by its power spectral density (PSD) matrix S u u (n) containing PSD and CPSD functions between turbulent velocities. Equivalently, the two-side PSD matrix G u u (ω) can also be utilized [11]. Having neglected the quadrature spectrum, both matrices S u u (n) and G u u (ω) are real and positive definite.…”

Section: Stochastic Wind Modelingmentioning

confidence: 99%

“…An alternative approach to the WAWS formula is represented by the POD-based technique proposed by Carassale and Solari [11]. With such an approach the matrices T(ω k ), in (1), are calculated as…”

Section: Digital Simulation Formulasmentioning

confidence: 99%

“…Likewise (2), (4) can be efficiently implemented through a fast Fourier transform (FFT) algorithm as discussed in [11], assuming again N ω = N t and Δω = 2π/N t Δt.…”

Section: Digital Simulation Formulasmentioning

confidence: 99%

“…Less demanding, yet approximate, procedures may be obtained by exploiting the properties of the POD decomposition of the spectral matrix, proposed in the papers by Li and Kareem [8], Di Paola [9], and Solari and Carassale [10]. A POD-based technique, in particular, was recently applied to simulate the wind velocity field on a domain representing a long-span suspension bridge [11] with significantly low computational efforts. A third well-established class of simulation formulas 14 and N t = T 0 /Δt (i.e., a process duration equal to one period T 0 ; black lines denote target functions, red lines denote numeric approximations): autospectrum of along-wind velocity at point 5 (a); corresponding autocorrelation function (b); cross-spectrum between along-wind velocities at points 5 and 9 (c); corresponding cross-correlation function (d).…”

Section: Introductionmentioning

confidence: 99%

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Computer Simulation of Stochastic Wind Velocity Fields for Structural Response Analysis: Comparisons and Applications

Ubertini

Giuliano

2010

Advances in Civil Engineering

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The digital simulation of wind velocity fields, modeled as multivariate stationary Gaussian processes, is a widely adopted tool to generate the external input for response analysis of wind-sensitive nonlinear structures. The problem does not entail any theoretical difficulty, existing already a large number of well-established techniques, such as the accurate weighted amplitude wave superposition (WAWS) method. However, reducing the computational effort required by the WAWS method is sometimes necessary, especially when dealing with complex structures and high-dimensional simulation domains. In these cases, approximate formulas must be adopted, which however require an appropriate tuning of some fundamental parameters in such a way to achieve an acceptable level of accuracy if compared to that obtained using the WAWS method. Among the different techniques available for this purpose, autoregressive (AR) filters and algorithms exploiting the proper orthogonal decomposition (POD) of the spectral matrix deserve a special attention. In this paper, a properly organized way for implementing stochastic wind simulation algorithms is outlined at first. Then, taking the WAWS method as a reference from the viewpoint of the accuracy of the simulated samples, a comparative study between POD-based and AR techniques is proposed, with a particular attention to computational effort and memory requirements.

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Section: Stochastic Wind Modelingmentioning

confidence: 99%

Section: Stochastic Wind Modelingmentioning

confidence: 99%

Section: Digital Simulation Formulasmentioning

confidence: 99%

“…Likewise (2), (4) can be efficiently implemented through a fast Fourier transform (FFT) algorithm as discussed in [11], assuming again N ω = N t and Δω = 2π/N t Δt.…”

Section: Digital Simulation Formulasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computer Simulation of Stochastic Wind Velocity Fields for Structural Response Analysis: Comparisons and Applications

Ubertini

Giuliano

2010

Advances in Civil Engineering

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On-line monitoring of wind-induced stresses and fatigue damage in instrumented structures

Hernández

Bernal

Caracoglia

2012

Struct. Control Health Monit.

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SUMMARYThe use of sparse-measured response as a means to monitor stresses and fatigue damage throughout metal structures has been the subject of recent work by researchers in the field of vibration-based structural health monitoring. The basic premise is that by using measured structural response, one can improve upon purely stochastic estimates. The most recent applications found in the literature employ the Kalman filter as an estimator. In this paper, we propose a finite element model-based state estimator that can be implemented as a modified version of the finite element model of the system using corrective forces that are proportional to the measurements. In contrast with the standard Kalman filter, the proposed estimator explicitly accounts for spatial correlation and for the colored nature of the excitation through knowledge of its power spectral density. The approach is used to estimate the number of threshold crossings in the bending moment history of a simulated tall vertical structure subject to turbulent wind. It is shown that the proposed model-based estimator outperforms a standard Kalman filter.

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Inertial mass damper for mitigating cable vibration

Duan

Spencer

et al. 2017

Struct. Control Health Monit.

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Summary Stay cables used in cable‐stayed bridges are prone to vibration due to their low‐inherent damping characteristics. Many methods have been implemented in practice to mitigate such vibration. Recently, negative stiffness dampers have gained attention because of their promising energy dissipation ability. The viscous inertial mass damper (VIMD) has been shown to have properties similar to negative stiffness dampers. This paper examines the potential of the VIMD to enhance the damping, and mitigate the vibration, of stay cables. First, a control‐oriented model of the cable is employed to formulate a system level model of the cable–VIMD system for small in‐plane motion. After carefully classifying and labeling the mode order, the modal characteristics of the system are analyzed, and the optimal damper parameters for the several lower frequency modes are determined numerically. The results show that the achievable modal damping ratio can be up to nearly an order of magnitude larger than that of the traditional linear viscous damper; note that the optimal parameters of the VIMD are distinct for each mode of interest. These results are further validated through analysis of the cable responses due to the distributed sinusoidal excitation. Finally, a case study is conducted for a cable with a length of 307 m, including the design of practical damper parameters, modal‐damping enhancement, and vibration mitigation under wind loads. The results show that the VIMD is a promising practical passive damper that possesses greater energy dissipation capacity than the traditional viscous damper for such cable–damper systems.

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Monte Carlo simulation of wind velocity fields on complex structures

Cited by 99 publications

References 11 publications

Computer Simulation of Stochastic Wind Velocity Fields for Structural Response Analysis: Comparisons and Applications

Computer Simulation of Stochastic Wind Velocity Fields for Structural Response Analysis: Comparisons and Applications

On-line monitoring of wind-induced stresses and fatigue damage in instrumented structures

Inertial mass damper for mitigating cable vibration

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