Non-Stationary Estimation of Joint Design Criteria With a Multivariate Conditional Extremes Approach

2016 ◽  
Author(s):  
Laks Raghupathi ◽  
David Randell ◽  
Kevin Ewans ◽  
Philip Jonathan
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Structural Reliability ◽  
Structural Response ◽  
Environmental Parameters ◽  
Joint Estimation ◽  
Current Profile ◽  
Practical Applications ◽  
Significant Wave ◽  
Northern North Sea

Understanding the interaction of ocean environments with fixed and floating structures is critical to the design of offshore and coastal facilities. Structural response to environmental loading is typically the combined effect of multiple environmental parameters over a period of time. Knowledge of the tails of marginal and joint distributions of these parameters (e.g. storm peak significant wave height and associated current) as a function of covariates (e.g. dominant wave and current directions) is central to the estimation of extreme structural response, and hence of structural reliability and safety. In this paper, we present a framework for the joint estimation of multivariate extremal dependencies with multi-dimensional covariates. We demonstrate proof of principle with a synthetic bi-variate example with two covariates quantified by rigorous uncertainty analysis. We further substantiate it using two practical applications (associated current given significant wave height for northern North Sea and joint current profile for offshore Brazil locations). Further applications include the estimation of associated criteria for response-based design (e.g., TP given HS), extreme current profiles with depth for mooring and riser loading, weathervaning systems with non-stationary effects for the design of FLNG/FPSO installations, etc.

A Joint Probability Model for Environmental Parameters

2011 ◽  
Vol 133 (3) ◽  
Author(s):  
Luís Volnei Sudati Sagrilo ◽  
Edison Castro Prates de Lima ◽  
Arnaldo Papaleo
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Environmental Parameters ◽  
Offshore Structures ◽  
Environmental Data ◽  
Statistical Dependence ◽  
Response Analysis ◽  
Peak Period ◽  
Significant Wave ◽  
Nataf Transformation

Joint probabilistic models (JPMs) for the environmental parameters such as wave, wind, and current are nowadays of paramount importance in order to perform the reliability analysis of marine structures. These JPMs are also essential for long-term statistics-based design of offshore structures and to perform dynamic response analysis of floating units that are strongly dependent on the directionality of the environmental actions such as turret-moored floating, production, storage, and offloading vessels (FPSOs). Recently, some JPMs have been proposed in literature to represent the joint statistics of a reduced number of environmental parameters. However, it is a difficult task to obtain practical and reliable models to express the complete statistical dependence among the environmental parameters intensities and their correspondent directions. This paper presents a methodology, based on the Nataf transformation, to create a JPM of wave, wind, and current environmental parameters taking into account, also, the statistical correlation between intensities and directions. The proposed model considers ten short-term environmental variables: the significant wave height, peak period, and direction of the sea waves, the significant wave height, peak period, and direction of the swell waves, the amplitude and direction of the 1 h wind velocity, and, finally, the amplitude and direction of the surface current velocity. The statistical dependence between them is modeled using concepts of linear-linear, linear-circular, and circular-circular variables correlation. Some results of the proposed JPM methodology are presented based on simultaneous environmental data gathered in an offshore Brazil location.

A Joint Probability Model for Environmental Parameters

2008 ◽  
Author(s):  
Lui´s Volnei Sudati Sagrilo ◽  
Edison Castro Prates de Lima ◽  
Arnaldo Papaleo
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Environmental Parameters ◽  
Offshore Structures ◽  
Environmental Data ◽  
Statistical Dependence ◽  
Response Analysis ◽  
Peak Period ◽  
Significant Wave ◽  
Nataf Transformation

The joint probabilistic models (JPM) of the environmental parameters of wave, wind and current are nowadays extremely needed in order to perform reliability analyses of offshore structures. These JPM are also essential steps for the design of offshore structures based on long-term statistics and to perform dynamic response analysis of floating units that are strongly dependent on the directionality of the environmental actions, such as turret-moored FPSOs. Recently, some JPM have been proposed in the literature to represent the joint statistics of a reduced number of environmental parameters. However, it is difficult to find a practical and fully operational model taking into account the complete statistical dependence among all the environmental parameters intensities and their correspondent directions. In this paper, it is presented a straightforward methodology, based on the Nataf transformation, to create a JPM of the environmental parameters taking into account the dependence between the intensity and direction of all variables. The proposed model considers the statistical dependence of ten short-term variables: the significant wave height, peak period and direction of the sea waves, the significant wave height, peak period and direction of the swell waves, the amplitude and direction of the 1-h wind velocity and, finally, the amplitude and direction of the surface current velocity. The statistical dependence between them is evaluated using concepts of linear-linear, linear-circular and circular-circular variables correlation. Some results of the proposed JPM methodology are presented based on simultaneous environmental data gathered in a location offshore Brazil.

The Extremal Dependence of Storm Severity, Wind Speed and Surface Level Pressure in the Northern North Sea

2013 ◽  
Author(s):  
Ross Towe ◽  
Emma Eastoe ◽  
Jonathan Tawn ◽  
Yanyun Wu ◽  
Philip Jonathan
Keyword(s):  
Wind Speed ◽  
Wave Height ◽  
North Sea ◽  
Significant Wave Height ◽  
Significant Wave ◽  
Extremal Dependence ◽  
Spatial Domains ◽  
Reliable Design ◽  
Northern North Sea ◽  
Dependence Measures

Characterising the joint distribution of extremes of significant wave height and wind speed is critical for reliable design and assessment of marine structures. The extremal dependence of pairs of oceanographic variables can be characterised using one of a number of summary statistics, which describe the two different types of extremal dependence. Quantifying the type of extremal dependence is an essential pre-requisite to joint or spatial extreme value modelling, and ensures that appropriate model forms are employed. We estimate extremal dependence between storm peak significant wave height and storm peak wind speed (Hs, WS) for locations in a region of the northern North Sea. However, since the extremal dependence itself may vary with storm direction, we introduce new covariate-dependent forms of the extremal dependence measures that account for the direction of the storm. We discuss the implications of all of the estimates for marine design, including specification of joint design criteria for extended spatial domains, and statistical downscaling to incorporate the effects of climate change on design specification.

On the Sensitivity of Passive Multistatic Radar Amplitude and Doppler Measurements to Significant Wave Height

2021 ◽  
pp. 1-5
Author(s):  
Jeffrey D. Ouellette ◽  
William T. Bounds ◽  
David J. Dowgiallo ◽  
Jakov V. Toporkov ◽  
Paul A. Hwang
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Significant Wave ◽  
Multistatic Radar

scholarly journals Estimation of Significant Wave Heights from ASCAT Scatterometer Data via Deep Learning Network

2021 ◽  
Vol 13 (2) ◽  
pp. 195
Author(s):  
He Wang ◽  
Jingsong Yang ◽  
Jianhua Zhu ◽  
Lin Ren ◽  
Yahao Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wave Height ◽  
Significant Wave Height ◽  
Incidence Angle ◽  
Ocean Waves ◽  
Global Scale ◽  
Learning Technology ◽  
Sea State ◽  
Significant Wave ◽  
Wave Heights

Sea state estimation from wide-swath and frequent-revisit scatterometers, which are providing ocean winds in the routine, is an attractive challenge. In this study, state-of-the-art deep learning technology is successfully adopted to develop an algorithm for deriving significant wave height from Advanced Scatterometer (ASCAT) aboard MetOp-A. By collocating three years (2016–2018) of ASCAT measurements and WaveWatch III sea state hindcasts at a global scale, huge amount data points (>8 million) were employed to train the multi-hidden-layer deep learning model, which has been established to map the inputs of thirteen sea state related ASCAT observables into the wave heights. The ASCAT significant wave height estimates were validated against hindcast dataset independent on training, showing good consistency in terms of root mean square error of 0.5 m under moderate sea condition (1.0–5.0 m). Additionally, reasonable agreement is also found between ASCAT derived wave heights and buoy observations from National Data Buoy Center for the proposed algorithm. Results are further discussed with respect to sea state maturity, radar incidence angle along with the limitations of the model. Our work demonstrates the capability of scatterometers for monitoring sea state, thus would advance the use of scatterometers, which were originally designed for winds, in studies of ocean waves.

scholarly journals Physical Modelling of the Effect on the Wave Field of the WaveCat Wave Energy Converter

2021 ◽  
Vol 9 (3) ◽  
pp. 309
Author(s):  
James Allen ◽  
Gregorio Iglesias ◽  
Deborah Greaves ◽  
Jon Miles
Keyword(s):  
Wave Height ◽  
Wave Energy ◽  
Significant Wave Height ◽  
Wedge Angle ◽  
Wave Energy Converter ◽  
Wave Period ◽  
Surface Elevation ◽  
Energy Converter ◽  
Significant Wave ◽  
Model Scale

The WaveCat is a moored Wave Energy Converter design which uses wave overtopping discharge into a variable v-shaped hull, to generate electricity through low head turbines. Physical model tests of WaveCat WEC were carried out to determine the device reflection, transmission, absorption and capture coefficients based on selected wave conditions. The model scale was 1:30, with hulls of 3 m in length, 0.4 m in height and a freeboard of 0.2 m. Wave gauges monitored the surface elevation at discrete points around the experimental area, and level sensors and flowmeters recorded the amount of water captured and released by the model. Random waves of significant wave height between 0.03 m and 0.12 m and peak wave periods of 0.91 s to 2.37 s at model scale were tested. The wedge angle of the device was set to 60°. A reflection analysis was carried out using a revised three probe method and spectral analysis of the surface elevation to determine the incident, reflected and transmitted energy. The results show that the reflection coefficient is highest (0.79) at low significant wave height and low peak wave period, the transmission coefficient is highest (0.98) at low significant wave height and high peak wave period, and absorption coefficient is highest (0.78) when significant wave height is high and peak wave period is low. The model also shows the highest Capture Width Ratio (0.015) at wavelengths on the order of model length. The results have particular implications for wave energy conversion prediction potential using this design of device.

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