Design Parameter Estimation of Wave Height and Wind Speed With Bivariate Copulas

2013 ◽  
Author(s):  
Shanshan Tao ◽  
Sheng Dong ◽  
Yinghui Xu
Keyword(s):  
Wind Speed ◽  
Wave Height ◽  
Random Variables ◽  
Joint Probability ◽  
Environmental Parameters ◽  
Gumbel Distribution ◽  
Bohai Bay ◽  
Base Shear ◽  
Extreme Wave ◽  
Probability Models

The data of annual extreme wave height and corresponding wind speed at a platform in Bohai Bay is hindcasted by a numerical model from 1970 to 1993. Common-used design probability distributions, such as Gumbel distribution, Weibull distribution, and lognormal distribution are applied to fit the data of extreme wave height and concomitant wind speed, respectively. Then the best-fitted marginal distributions of annual extreme wave height and wind speed can be selected. Bivariate normal copula and Frank copula are utilized to construct joint distribution of these two random variables. Based on empirical base shear equation of the on-site fixed jacket platform, the maximum base shear can be calculated under the same joint return period of the wave height and wind speed. The results show that the proposed joint probability models constructed by bivariate copulas result in lower the design environmental parameters because of the consideration of correlation between random variables. Eventually the investment cost of marginal oil fields could be relatively reduced.

Reliability analysis for a spar-supported floating offshore wind turbine

2017 ◽  
Vol 42 (1) ◽  
pp. 51-65 ◽  
Author(s):  
Abhinav Sultania ◽  
Lance Manuel
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Reliability Analysis ◽  
Wave Height ◽  
Return Period ◽  
Random Variables ◽  
Environmental Data ◽  
First Order Reliability Method ◽  
First Order ◽  
Reliability Method

The reliability analysis of a spar-supported floating offshore 5-MW wind turbine is the subject of this study. Environmental data from a selected site are employed in the numerical studies. Using time-domain simulations, the dynamic behavior of a coupled platform-turbine system is studied; statistics of tower and rotor loads as well as platform motions are estimated and critical combinations of wind speed and wave height identified. Long-term loads associated with a 50-year return period are estimated using statistical extrapolation based on loads derived from simulations. Inverse reliability procedures that seek appropriate fractile levels for underlying variables consistent with the target load return period are employed; these include use of (1) two-dimensional inverse first-order reliability method where extreme loads, conditional on wind speed and wave height random variables, are selected at median levels and (2) three-dimensional inverse first-order reliability method where variability in the environmental and load random variables is fully represented.

scholarly journals Calculation of Joint Return Period for Connected Edge Data

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 300 ◽  
Author(s):  
Guilin Liu ◽  
Baiyu Chen ◽  
Zhikang Gao ◽  
Hanliang Fu ◽  
Song Jiang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Correlation Analysis ◽  
Wave Height ◽  
Joint Distribution ◽  
Measured Data ◽  
Two Dimensional ◽  
Ocean Engineering ◽  
Extreme Wave ◽  
Correlation Measure ◽  
Copula Functions

For better displaying the statistical properties of measured data, it is particularly important to select a suitable multivariate joint distribution model in ocean engineering. According to the characteristics and properties of Copula functions and the correlation analysis of measured data, the nonlinear relationship between random variables can be captured. Additionally, the models based on the Copula theory have more general applicability. A series of correlation measure index, derived from Copula functions, can expand the correlation measure range among variables. In this paper, by means of the correlation analysis between the annual extreme wave height and the corresponding wind speed, their joint distribution models were studied. The newly established two-dimensional joint distribution functions of the extreme wave height and the corresponding wind speed were compared with the existing two-dimensional joint distributions.

scholarly journals Bayesian Wind Speed Estimation Conditioned on Significant Wave Height for GNSS-R Ocean Observations

2017 ◽  
Vol 34 (6) ◽  
pp. 1193-1202 ◽  
Author(s):  
Maria Paola Clarizia ◽  
Christopher S. Ruf
Keyword(s):  
Wind Speed ◽  
Wave Height ◽  
Significant Wave Height ◽  
Joint Probability ◽  
Satellite System ◽  
Bayesian Estimator ◽  
Significant Wave ◽  
Wind Speeds ◽  
Speed Estimator ◽  
Low Wind Speeds

AbstractSpaceborne Global Navigation Satellite System reflectometry observations of the ocean surface are found to respond to components of roughness forced by local winds and to a longer wave swell that is only partially correlated with the local wind. This dual sensitivity is largest at low wind speeds. If left uncorrected, the error in wind speeds retrieved from the observations is strongly correlated with the significant wave height (SWH) of the ocean. A Bayesian wind speed estimator is developed to correct for the long-wave sensitivity at low wind speeds. The approach requires a characterization of the joint probability of occurrence of wind speed and SWH, which is derived from archival reanalysis sea-state records. The Bayesian estimator is applied to spaceborne data collected by the Technology Demonstration Satellite-1 (TechDemoSat-1) and is found to provide significant improvement in wind speed retrieval at low winds, relative to a conventional retrieval that does not account for SWH. At higher wind speeds, the wind speed and SWH are more highly correlated and there is much less need for the correction.

Long-Term Reliability Analysis of a Spar Buoy-Supported Floating Offshore Wind Turbine

2011 ◽  
Author(s):  
A. Sultania ◽  
L. Manuel
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Reliability Analysis ◽  
Wave Height ◽  
Wind Turbines ◽  
Return Period ◽  
Random Variables ◽  
Offshore Wind ◽  
Floating Platform

Most offshore wind turbines constructed to date have support structures for the turbine towers that extend to the seabed. Such bottom-supported turbines are confined to shallow waters closer to the shore. Sites farther offshore provide a better wind resource (i.e., stronger wind and less turbulence) while also reducing concerns related to visual impact and noise. However, in deeper waters, bottom-supported turbines are less economical. Wind turbines mounted atop floating platforms are, thus, being considered for deepwater sites. Several floating platform concepts are being considered; they differ mainly in how they provide stability to counter the large mass of the rotor-nacelle assembly located high above the water. One of these alternative concepts is a spar buoy floating platform with a deep draft structure and a low center of gravity, below the center of buoyancy. The reliability analysis of a spar-supported 5MW wind turbine based on stochastic simulation is the subject of this study. Environmental data from a selected deepwater reference site are employed in the numerical studies. Using time-domain simulations, the dynamic behavior of the coupled platform-turbine system is studied; statistics of tower and rotor loads as well as platform motions are estimated and critical combinations of wind speed and wave height identified. Long-term loads associated with a 50-year return period are estimated using statistical extrapolation based on loads derived from the simulations. Inverse reliability procedures that seek appropriate load fractiles for the underlying random variables consistent with the target return period are employed; these include use of: (i) the 2D Inverse First-Order Reliability Method (FORM) where an extreme load is selected at its median level (conditional on a derived critical wind speed and wave height combination); and (ii) the 3D Inverse FORM where variability in the environmental and load random variables is fully represented to derive the 50-year load.

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.

scholarly journals Assessment of reliability of extreme wave height prediction models

2017 ◽  
Vol 17 (3) ◽  
pp. 409-421 ◽  
Author(s):  
Satish Samayam ◽  
Valentina Laface ◽  
Sannasiraj Sannasi Annamalaisamy ◽  
Felice Arena ◽  
Sundar Vallam ◽  
...  
Keyword(s):  
North America ◽  
Mediterranean Sea ◽  
Wave Height ◽  
Prediction Models ◽  
North Pacific Ocean ◽  
Coastal Hazards ◽  
Extreme Wave ◽  
Indian Water ◽  
Wave Hindcast ◽  
Buoy Data

Abstract. Extreme waves influence coastal engineering activities and have an immense geophysical implication. Therefore, their study, observation and extreme wave prediction are decisive for planning of mitigation measures against natural coastal hazards, ship routing, design of coastal and offshore structures. In this study, the estimates of design wave heights associated with return period of 30 and 100 years are dealt with in detail. The design wave height is estimated based on four different models to obtain a general and reliable model. Different locations are considered to perform the analysis: four sites in Indian waters (two each in Bay of Bengal and the Arabian Sea), one in the Mediterranean Sea and two in North America (one each in North Pacific Ocean and the Gulf of Maine). For the Indian water domain, European Centre for Medium-Range Weather Forecasts (ECMWF) global atmospheric reanalysis ERA-Interim wave hindcast data covering a period of 36 years have been utilized for this purpose. For the locations in Mediterranean Sea and North America, both ERA-Interim wave hindcast and buoy data are considered. The reasons for the variation in return value estimates of the ERA-Interim data and the buoy data using different estimation models are assessed in detail.

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