Spatial Coherence of Nonlinear, Nonstationary, Non-Gaussian Ocean Waves on a One-Mile Scale from Scanning Altimeter Radar

2001 ◽  
Author(s):  
Leon E. Borgman ◽  
Edward J. Walsh
Keyword(s):  
Spatial Coherence ◽  
Ocean Waves ◽  
Non Gaussian

Approximation of Crossing Intensities for Non Linear Responses Subjected to Non Gaussian Loadings

2010 ◽  
Author(s):  
Thomas Galtier ◽  
Sayan Gupta ◽  
Igor Rychlik
Keyword(s):  
Heavy Tails ◽  
Moving Average ◽  
Ocean Waves ◽  
Response Characteristic ◽  
Second Order ◽  
Computationally Efficient ◽  
Marine Structures ◽  
Efficient Manner ◽  
New Class ◽  
Non Gaussian

Crossing intensity constitute an important response characteristic for randomly vibrating structures, especially if one is interested in estimating the risk against failures. This paper focusses on developing approximations by which estimates of the crossing intensities for response of marine structures can be obtained in a computationally efficient manner, when the loads are modeled as a special class of non-Gaussian processes, namely as LMA processes. Ocean waves exhibit considerable non-Gaussianity as marked by their skewed marginal distributions and heavy tails. Here, a new class of processes-the Laplace driven Moving Average (LMA) processes are used to model the ocean waves. LMA processes are non-Gaussian, strictly stationary, can model in principle any spectrum and have the additional flexibility to model the skewness and the kurtosis of the marginal distribution. The structure behavior assumed is limited to quadratic systems characterized by second order kernels, which is common for marine structures. Thus, an estimation of the crossing intensities of the response involves studying the crossing characteristics of a LMA process passing through a second order filter. A new computationally efficient hybrid method, which uses the saddle point approximations along with limited Monte Carlo simulations, is developed to compute crossing intensity of the response. The proposed method is illustrated through numerical examples.

Modelling Shallow-Water Waves: Truncated Hermite Models and the ShallowWave Routine

2015 ◽  
Author(s):  
Steven R. Winterstein ◽  
Sverre Haver
Keyword(s):  
Random Process ◽  
Water Waves ◽  
Statistical Modelling ◽  
Finite Depth ◽  
Ocean Waves ◽  
Process Models ◽  
Wave Heights ◽  
Wave Models ◽  
Non Gaussian ◽  
Limiting Values

Statistical modelling of ocean waves is complicated by their nonlinearity, which leads in turn to non-Gaussian statistical behavior. While non-Gaussianity is present even in deep-water applications, its effects are especially pronounced as water depths decrease. We apply two types of wave models here: (1) local models of extreme wave heights/periods and breaking limits, and (2) random process models of the entire non-Gaussian wave surface. For the random process approach, we derive a new “truncated” Hermite model, which can reflect four moments and both upper- and lower-bound limiting values due to breaking and finite-depth effects. Results are calibrated and compared with an extensive model test series, comprising up to 23 hrs of histories across 19 seastates, at depths from 15–67m (full scale).

Simulating Quadratically Nonlinear Random Processes

1997 ◽  
Vol 07 (06) ◽  
pp. 1367-1374 ◽  
Author(s):  
Barry Vanhoff ◽  
Steve Elgar
Keyword(s):  
Time Series ◽  
Power Spectrum ◽  
Ocean Waves ◽  
Original Time Series ◽  
Third Order ◽  
Target Power ◽  
Non Gaussian ◽  
Original Time ◽  
Gaussian Time ◽  
Gaussian Time Series

A technique to generate realizations of quadratically nonlinear non-Gaussian time series with a desired ("target") power spectrum and bispectrum is presented. Specifically, by generating a Gaussian time series (using amplitude information from the target power spectrum and random phases) and passing it through a quadratic filter (that uses phase information from the target bispectrum), a realization of a quadratically nonlinear random process with a specified power spectrum and bispectrum can be produced. Second- and third-order statistics from many realizations of simulated nonlinear time series compare well to those from the original time series providing the target power spectrum and bispectrum, with deviations consistent with theory. The simulation technique is shown to simulate accurately ocean waves in shallow water, which are well known to be quadratically nonlinear.

Generalization of Rayleigh Probability Distribution and its Application

1978 ◽  
Vol 22 (04) ◽  
pp. 259-265
Author(s):  
Michel K. Ochi
Keyword(s):  
Probability Distribution ◽  
Surface Effect ◽  
Ocean Waves ◽  
Random Processes ◽  
Rayleigh Distribution ◽  
Nonlinear Damping ◽  
Statistical Prediction ◽  
Rolling Motion ◽  
Surface Effect Ship ◽  
Non Gaussian

The Rayleigh probability distribution has been used extensively for statistical prediction of ocean waves, responses of ships, and marine structures in a seaway. However, the Rayleigh distribution cannot be used for statistical prediction of non-Gaussian random processes, a typical example of which is ship rolling motion with nonlinear characteristics. To evaluate the statistical properties of the maxima (peak values) of non-Gaussian random processes, this paper discusses generalization of the Rayleigh distribution and its application to practical problems. As examples of application, the problems associated with rolling motion of a vessel stabilized by rudders, responses of a surface effect ship in a seaway, and rolling motion of a ship with nonlinear damping are presented.

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