Analysis and simulation tools for wind engineering

The estimation of the extreme of non-Gaussian load effects for design applications has often been treated tacitly by invoking a conventional peak factor on the basis of Gaussian processes. This assumption breaks down when the loading process exhibits non-Gaussianity, in which a conventional peak factor yields relatively nonconservative estimates because of failure to include long tail regions inherent to non-Gaussian processes. To realistically capture the salient characteristics of non-Gaussian load effects and incorporate these in the estimates of their extremes, this study examines the peak factor for non-Gaussian processes, which can be used for estimating the expected value of the positive and negative extremes of non-Gaussian load effects. The efficacy of previously introduced analytical expressions for the peak factor of non-Gaussian processes on the basis of a moment-based Hermite model is evaluated and the variance of the estimates in standard deviation is derived. In addition, some improvements ...

https://www3.nd.edu/~nathaz/documents/NatHazReport-Peak_Factors_for_Non-Gaussian_Load_Effects_Revisited_May_10_2010.pdf

Peak Factors for Non-Gaussian Load Effects Revisited

A comparison of methods to estimate peak wind loads on buildings

Linearization in Analysis of Nonlinear Stochastic Systems: Recent Results—Part I: Theory

Analysis interpretation modeling and simulation of unsteady wind and pressure data

The nonlinearity in the wind loading expression for a complaint offshore structure, e.g., a tension leg platform (TLP), results in response statistics that deviate from the Gaussian distribution. This paper focuses on the statistical analysis of the response of these structures to random wind loads. The analysis presented here involves a nonlinear system with memory. As an improvement over the commonly used linearization approach, an equivalent statistical quadratization method is presented. The higher-order response cumulants are based on Volterra series. A direct integration scheme and Kac-Siegert technique is utilized to evaluate the response cumulants. Based on the first four cumulants, the response probability density function, crossing rates, and peak value distribution are derived. The results provide a good comparison with simulation. A nonlinear wind gust loading factor based on the derived extreme value distribution of nonlinear wind effects is formulated.

J. Zhao

Papers

Analysis of Non-Gaussian Surge Response of Tension Leg Platforms Under Wind Loads