Extreme Responses of An Integrated System with A Semi-Submersible Wind Turbine and Four Torus-Shaped Wave Energy Converters in Different Survival Modes

Offshore wind power is a kind of important clean renewable energy and has attracted increasing attention due to the rapid consumption of non-renewable energy.To reduce the high cost of energy,a possible try is to utilize the combination of wind and wave energy considering their natural correlation.A combined concept consisting of a semi-submersible wind turbine and four torus-shaped wave energy converters was proposed and numerically studied under normal operating conditions.However,the dynamic behavior of the integrated system under extreme sea conditions has not been studied yet.In the present work,extreme responses of the integrated system under two different survival modes are evaluated.Fully coupled time-domain simulations with consideration of interactions between the semi-submersible wind turbine and the torus-shaped wave energy converters are performed to investigate dynamic responses of the integrated system,including mooring tensions,tower bending moments,end stop forces,and contact forces at the Column-Torus interface.It is found that the addition of four tori will reduce the mean motions of the yaw,pitch and surge.When the tori are locked at the still water line,the whole integrated system is more suitable for the survival modes.

Prediction of Short-Term Distributions of Load Extremes of Offshore Wind Turbines

This paper proposes a new methodology to select an optimal threshold level to be used in the peak over threshold （POT） method for the prediction of short-term distributions of load extremes of offshore wind turbines. Such an optimal threshold level is found based on the estimation of the variance-to-mean ratio for the occurrence of peak values, which characterizes the Poisson assumption. A generalized Pareto distribution is then fitted to the extracted peaks over the optimal threshold level and the distribution parameters are estimated by the method of the maximum spacing estimation. This methodology is applied to estimate the short-term distributions of load extremes of the blade bending moment and the tower base bending moment at the mudline of a monopile-supported 5MW offshore wind turbine as an example. The accuracy of the POT method using the optimal threshold level is shown to be better, in terms of the distribution fitting, than that of the POT methods using empirical threshold levels. The comparisons among the short-term extreme response values predicted by using the POT method with the optimal threshold levels and with the empirical threshold levels and by using direct simulation results further substantiate the validity of the proposed new methodology.

Probabilistic Methods for Reliability Assessment of Floating Structures Based on Extreme Wave Simulation

With the increasing application of floating platforms in deep waters and harsh environments,a proper assessment of the reliability of floating structures is important to ensure that these structures can operate safely during their design lives.This study outlines a practical methodology for reliability analysis of a semi-submersible platform based estimating the probability distribution of the extreme response in rough sea conditions(survival conditions).The Constrained NewWave(CNW)theory combined with Monte Carlo simulations was first applied to simulate the random wave surface elevation process in the time domain.A Gumbel distribution was the best fitting to describe the dynamically sensitive extreme response statistics under extreme waves(drift and mooring tension).The derived probability distribution of the extreme response was subsequently used in estimation of the associated limit state func-tion,and a reliability analysis of the floating structure was conducted using the Monte Carlo method.A semi-submersible platform in a water depth of 1500 m subjected to extreme wave loads was used to demonstrate the efficiency of the proposed methodology.The probability of failure of the semi-submersible when considering mooring lines tension is greater than considering drift.

Estimating Design Loads for Floating Structures Using Environmental Contours

Nonlinear time-domain simulations are often used to predict the structural response at the design stage to ensure the acceptable operation and/or survival of floating structures under extreme conditions.An environmental contour(EC)is commonly employed to identify critical sea states that serve as the input for numerical simulations to assess the safety and performance of marine structures.In many studies,marginal and conditional distributions are defined to construct bivariate joint probability distributions for variables,such as significant wave height and zero-crossing period.Then,ECs can be constructed using the inverse first-order reliability method(IFORM).This study adopts alternative models to describe the generalized dependence structure between environmental variables using copulas and discusses the Nataf transformation as a special case.ECs are constructed using measured wave data from moored buoys.Derived design loads are applied on a semisubmersible platform to assess possible differences.In addition,a linear interpolation scheme is utilized to establish a parametric model using short-term extreme tension distribution parameters and wave data,and the long-term tension response is estimated using Monte Carlo simulation.A 3D IFORM-based approach,in which the short-term extreme response that is ignored in the EC approach is used as the third variable,is proposed to help establish accurate design loads with increased accuracy.Results offer a clear illustration of the extreme responses of floating structures based on different models.

Statistical extremes and peak factors in wind-induced vibration of tall buildings

In the structural design of tall buildings, peak factors have been widely used to predict mean extreme responses of tall buildings under wind excitations. Vanmarcke＇s peak factor is directly related to an explicit measure of structural reliability against a Gaussian response process. We review the use of this factor for time-variant reliability design by comparing it to the conven- tional Davenport＇s peak factor. Based on the asymptotic theory of statistical extremes, a new closed-form peak factor, the so-called Gamma peak factor, can be obtained for a non-Gaussian resultant response characterized by a Rayleigh distribution process. Using the Gamma peak factor, a combined peak factor method was developed for predicting the expected maximum resultant responses of a building undergoing lateral-torsional vibration. The effects of the standard deviation ratio of two sway components and the inter-component correlation on the evaluation of peak resultant response were also investigated. Utilizing wind tunnel data derived from synchronous multi-pressure measurements, we carried out a wind-induced time history response analysis of the Common- wealth Advisory Aeronautical Research Council （CAARC） standard tall building to validate the applicability of the Gamma peak factor to the prediction of the peak resultant acceleration. Results from the building example indicated that the use of the Gamma peak factor enables accurate predictions to be made of the mean extreme resultant acceleration responses for dynamic service- ability performance design of modem tall buildings.

A new structural reliability analysis method in presence of mixed uncertainty variables

For structures with both random and fuzzy uncertainty,this paper presents a novel method for determining the membership function in fuzzy reliability with the Automatic Updating Extreme Response Surface(AUERS)method.In the proposed method,fuzzy variables are initially converted into a value domain under the given cut level and the extreme point in the domain where the reliability reaches its extreme value is considered.Second,the Particle Swarm Optimization(PSO)algorithm is used to determine the extreme point according to the extreme responses for different sets of random sample inputs.A kriging response surface is subsequently constructed between the random variables and the corresponding extreme points.An automatic updating strategy is then introduced based on the Relative Mean Square Predicted Error(RMSPE)before performing every iteration of reliability analysis.By adding new sample points,the approximate quality of the kriging response surface is improved.Finally,reliability analysis is used to determine the reliability bound under the given cut level.The proposed method assures the accuracy and computation efficiency of the mixed uncertainty reliability analysis results while it prevents the solution from becoming trapped in a local optimum,which occurs in classical optimization methods.Two example analyses are used to demonstrate the validity and advantages of the proposed method.