Extreme Value Analysis of the Response of a Turret-Moored FPSO

2010 ◽  
Author(s):  
Lui´s Volnei Sudati Sagrilo ◽  
Arvid Naess ◽  
Zhen Gao
Keyword(s):  
Time Series ◽  
Extreme Values ◽  
Distribution Model ◽  
Extreme Value Analysis ◽  
Coupled Analysis ◽  
System Response ◽  
Short Term ◽  
Utilization Factor ◽  
Value Analysis ◽  
Extreme Response

One of the standardized procedures used in the design of floating systems and their mooring and production lines is the so-called short-term design approach where the system is analyzed for some specific extreme environmental conditions. Along with this procedure, a nonlinear time-domain coupled dynamic analysis, considering the floater and its risers and mooring lines, is nowadays feasible to be employed in the design practice. One important and challenging aspect of this process is concerned with the estimation of the characteristic short-term extreme values of the system response parameters based on the sampled time-series. In this paper a common procedure used to establish these extreme values for floater system response parameters, which is based on a Weibull distribution model for the time-series peaks, is reviewed in the light of a recently proposed approach based on a general parametric model for the average conditional exceedance rate of peaks. It is shown that the former model corresponds to a particular case of the latter one. Numerical results are presented for the response parameters of a turret-moored FPSO considering a short-term coupled analysis of the whole system under an extreme environmental condition of wind, wave and current. Specifically, the extreme response of surge motion, top tension of the most loaded mooring line and DnV’s utilization factor for the most critical section of a 8″ SLWR (Steel Lazy Wave Riser) are investigated.

On the Extreme Value Analysis of the Response of a Turret Moored FPSO

2012 ◽  
Vol 134 (4) ◽  
Author(s):  
L. V. S. Sagrilo ◽  
A. Naess ◽  
Z. Gao
Keyword(s):  
Time Series ◽  
Extreme Values ◽  
Extreme Value Analysis ◽  
Coupled Analysis ◽  
System Response ◽  
Outer Diameter ◽  
Short Term ◽  
Utilization Factor ◽  
Value Analysis ◽  
Extreme Response

One of the standardized procedures used in the design of floating systems and their mooring and production lines is the so-called short-term design approach where the system is analyzed for some specific extreme environmental conditions. Along with this procedure, a nonlinear time-domain coupled dynamic analysis, considering the floater and its risers and mooring lines, can nowadays be incorporated as a feasible part of the design practice. One very important and challenging aspect of this process is concerned with the estimation of the characteristic short-term extreme values of the system response parameters based on the sampled time-series. In this paper a common procedure used to establish these extreme values for floater system response parameters, which is based upon a Weibull distribution model for the peaks of the time-series, is reviewed in the light of a recently proposed approach based on a general parametric model for the average conditional exceedance rate of peaks. It is shown that the former model corresponds to a particular case of the latter one. Numerical results are presented for the response parameters of a turret-moored Floating, Production, Storage and Offloading (FPSO) unit considering a short-term coupled analysis of the whole system under an extreme environmental condition of wind, wave, and current. Specifically, the extreme response of surge motion, top tension of the most loaded mooring line, and Det norske Veritas (DnV) codes utilization factor for the most critical section of an 0.20 m outer diameter SLWR (steel lazy wave riser) are investigated.

scholarly journals Non-stationary Extreme Value Analysis: a simplified approach for Earth science applications

2016 ◽  
Author(s):  
Lorenzo Mentaschi ◽  
Michalis Vousdoukas ◽  
Evangelos Voukouvalas ◽  
Ludovica Sartini ◽  
Luc Feyen ◽  
...  
Keyword(s):  
Time Series ◽  
Extreme Values ◽  
Extreme Value ◽  
Stationary Time Series ◽  
Alternative Methods ◽  
Extreme Value Analysis ◽  
Accurate Estimation ◽  
Residual Water ◽  
Value Analysis ◽  
Simplified Approach

Abstract. Statistical approaches to study extreme events require by definition long time series of data. The climate is subject to natural and anthropogenic variations at different temporal scales, leaving their footprint on the frequency and intensity of climatic and hydrological extremes, therefore assumption of stationarity is violated and alternative methods to conventional stationary Extreme Value Analysis (EVA) need to be adopted. In this study we introduce the Transformed-Stationary (TS) methodology for non-stationary EVA. This approach consists in (i) transforming a non-stationary time series into a stationary one to which the stationary EVA theory can be applied; and (ii) reverse-transforming the result into a non-stationary extreme value distribution. As a transformation we propose and discuss a simple time-varying normalization of the signal and show that it allows a comprehensive formulation of non stationary GEV/GPD models with constant shape parameter. A validation of the methodology is carried out on time series of significant wave height, residual water level, and river discharge, which show varying degrees of long-term and seasonal variability. The results from the proposed approach are comparable with the ones from (a) a stationary EVA on quasi-stationary slices of non stationary series and (b) the previously applied non stationary EVA approach. However, the proposed technique comes with advantages in both cases, as in contrast to (a) it uses the whole time horizon of the series for the estimation of the extremes, allowing for a more accurate estimation of large return levels; and with respect to (b) it decouples the detection of non-stationary patterns from the fitting of the extreme values distribution. As a result the steps of the analysis are simplified and intermediate diagnostics are possible. In particular the transformation can be carried out by means of simple statistical techniques such as low-pass filters based on running mean and standard deviation, and the fitting procedure is a stationary one with a few degrees of freedom and easy to implement and control. An open-source MATLAB toolbox has been developed to cover this methodology, available at https://bitbucket.org/menta78/tseva.

Covariate Extreme Value Analysis Using Wave Spectral Partitioning

2020 ◽  
Vol 37 (5) ◽  
pp. 873-888 ◽  
Author(s):  
Jesús Portilla-Yandún ◽  
Edwin Jácome
Keyword(s):  
Extreme Values ◽  
Wind Waves ◽  
Spectral Energy Distribution ◽  
Wave Spectrum ◽  
Wave Climate ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Spectral Energy ◽  
Value Analysis ◽  
Spectral Partitioning

AbstractAn important requirement in extreme value analysis (EVA) is for the working variable to be identically distributed. However, this is typically not the case in wind waves, because energy components with different origins belong to separate data populations, with different statistical properties. Although this information is available in the wave spectrum, the working variable in EVA is typically the total significant wave height Hs, a parameter that does not contain information of the spectral energy distribution, and therefore does not fulfill this requirement. To gain insight in this aspect, we develop here a covariate EVA application based on spectral partitioning. We observe that in general the total Hs is inappropriate for EVA, leading to potential over- or underestimation of the projected extremes. This is illustrated with three representative cases under significantly different wave climate conditions. It is shown that the covariate analysis provides a meaningful understanding of the individual behavior of the wave components, in regard to the consequences for projecting extreme values.

Determination of Short Term Extreme Response Values for Temporary Riser Systems: A Practical Approach

2016 ◽  
Author(s):  
Ali Cetin ◽  
Trond Pytte ◽  
Sveinung Eriksrud
Keyword(s):  
Time Series ◽  
Extreme Values ◽  
Classical Method ◽  
Practical Method ◽  
Rayleigh Distribution ◽  
Relevant Variable ◽  
Extreme Response ◽  
Non Gaussian ◽  
Probability Modelling

Operation limits for temporary riser system are determined according to some probability of exceedance of a relevant variable. Accordingly, consistent statistical analysis and probability modelling of the data is required. The common industry approach is to rely on the classical narrow-banded Gaussian process assumption when considering time series of variables of interest. Thus, the time series peaks are characterized by means of the Rayleigh distribution and the relevant extreme values are estimated based on this. However, non-linearities present in riser systems may yield non-Gaussian (wide-banded) processes, rendering the classical approach inappropriate. In the present work, an approximate and practical method is presented to address above issue. It is demonstrated that the approximate method is capable of consistently estimating the relevant extreme values, even where the classical method comes short.

Long-Term Area Statistics for Maximum Crest Height Under a Fixed Platform Deck

2018 ◽  
Author(s):  
Øistein Hagen ◽  
Jørn Birknes-Berg ◽  
Ida Håøy Grue ◽  
Gunnar Lian ◽  
Kjersti Bruserud ◽  
...  
Keyword(s):  
Time Series ◽  
North Sea ◽  
Extreme Values ◽  
Second Order ◽  
Irregular Waves ◽  
Scatter Diagram ◽  
Short Term ◽  
Wave Impact ◽  
Crest Height

As offshore reservoirs are depleted, the seabed may subside. Furthermore, the extreme crests estimates are now commonly higher than obtained previously due to improved understanding of statistics of non-linear irregular waves. Consequently, bottom fixed installations which have previously had sufficient clearance between the deck and the sea surface may be in a situation where wave impact with the deck must be considered at relevant probability levels. In the present paper, we investigate the long-term area statistics for maximum crest height under a fixed platform deck for 2nd order short crested and long crested sea based on numerical simulations as a function of platform deck dimension for jackets. The results are for one location in the northern North Sea, but some key results are also reported and verified for a more benign southern North Sea location. Time domain simulations for long crested and short crested waves over a spatial domain with dimension of a platform deck are performed, and relevant statistics for airgap assessment determined. Second order waves are simulated for the different cells in the (Hs, Tp) scatter diagram for Torsethaugen two-peak wave spectrum for long-crested and short-crested sea. A total of 1000 3-hour sea states are generated per cell, and time series generated for 160 spatial points under a platform deck. Short-term and long-term statistics are established for the maximum crest height as function of platform dimension; inline and transverse to the wave direction, and over the area. Results are given for the linear sea and for the second order time series. The annual q-probability estimates for the maximum crest height over area as a function of platform dimension is determined for a location at the Norwegian Continental Shelf by weighting the short-term statistics for the individual cells in the scatter diagram with the long-term probability of occurrence of the sea state. To reduce the number of numerical second order simulations, the effect of excluding cells that have a negligible effect on the long term extreme crest estimate is discussed. The percentiles in the distribution of maximum crest (over area) in design sea states that corresponds to the extreme values obtained from the long-term analysis are determined for long crested and short crested sea. The increase in the extreme crest over an area compared to the point in space estimate is estimated for both linear and second order surface elevation.

Comparison of the Environmental Contour Method and Response-Based Analysis Using Response Emulator for Estimating Extreme Ship Responses

2019 ◽  
Author(s):  
Erik Vanem ◽  
Bingjie Guo
Keyword(s):  
Time Series ◽  
Environmental Conditions ◽  
Structural Response ◽  
Bending Moment ◽  
Contour Method ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Long Time Series ◽  
Long Time ◽  
Extreme Responses

Abstract Environmental contours are often applied in probabilistic structural reliability analysis to identify extreme environ-mental conditions that may give rise to extreme loads and responses. They facilitate approximate long term analysis of critical structural responses in situations where computationally heavy and time-consuming response calculations makes full long-term analysis infeasible. The environmental contour method identifies extreme environmental conditions that are expected to give rise to extreme structural response of marine structures. The extreme responses can then be estimated by performing response calculations for environmental conditions along the contours. Response-based analysis is an alternative, where extreme value analysis is performed on the actual response rather than on the environmental conditions. For complex structures, this is often not practical due to computationally heavy response calculations. However, by establishing statistical emulators of the response, using machine learning techniques, one may obtain long time-series of the structural response and use this to estimate extreme responses. In this paper, the contour method will be compared to response-based estimation of extreme vertical bending moment for a tanker. A response emulator based on Gaussian processes regression with adaptive sampling has been established based on response calculations from a hydrodynamic model. Long time-series of sea-state parameters such as significant wave height and wave period are used to construct N-year environmental contours and the extreme N-year response is estimated from numerical calculations for identified sea states. At the same time, the response emulator is applied on the time series to provide long time-series of structural response, in this case vertical bending moment of a tanker. Extreme value analysis is then performed directly on the responses to estimate the N-year extreme response. The results from either method will then be compared, and it is possible to evaluate the accuracy of the environmental contour method in estimating the response.

scholarly journals EXTREME VALUE ANALYSIS WITHOUT THE LARGEST VALUES: WHAT CAN BE DONE?

2019 ◽  
Vol 34 (2) ◽  
pp. 200-220
Author(s):  
Jingjing Zou ◽  
Richard A. Davis ◽  
Gennady Samorodnitsky
Keyword(s):  
Order Statistics ◽  
Extreme Values ◽  
Sample Path ◽  
Estimation Procedure ◽  
Extreme Value ◽  
Hill Estimator ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Heavy Tailed ◽  
The Hill

AbstractIn this paper, we are concerned with the analysis of heavy-tailed data when a portion of the extreme values is unavailable. This research was motivated by an analysis of the degree distributions in a large social network. The degree distributions of such networks tend to have power law behavior in the tails. We focus on the Hill estimator, which plays a starring role in heavy-tailed modeling. The Hill estimator for these data exhibited a smooth and increasing “sample path” as a function of the number of upper order statistics used in constructing the estimator. This behavior became more apparent as we artificially removed more of the upper order statistics. Building on this observation we introduce a new version of the Hill estimator. It is a function of the number of the upper order statistics used in the estimation, but also depends on the number of unavailable extreme values. We establish functional convergence of the normalized Hill estimator to a Gaussian process. An estimation procedure is developed based on the limit theory to estimate the number of missing extremes and extreme value parameters including the tail index and the bias of Hill's estimator. We illustrate how this approach works in both simulations and real data examples.

scholarly journals Method Consideration of Variation Diagnosis and Design Value Calculation of Flood Sequence in Yiluo River Basin, China

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2722
Author(s):  
Xinxin Li ◽  
Xixia Ma ◽  
Xiaodong Li ◽  
Wenjiang Zhang
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Human Activities ◽  
Extreme Values ◽  
Frequency Estimation ◽  
Synthesis Method ◽  
Estimation Method ◽  
Stationary Time Series ◽  
Distribution Model ◽  
Design Flood

The conventional approaches of the design flood calculation are based on the assumption that the hydrological time series is subject to the same distribution in the past, present, and future, i.e., the series should be consistent. However, the traditional methods may result in overdesign in the water conservancy project since the series has non-stationary variations due to climate change and human activities. Therefore, it is necessary to develop a new approach for frequency estimation of non-stationary time series of extreme values. This study used four kinds of mutation test methods (the linear trend correlation coefficient, Mann–Kendall test, sliding t-test, and Pettitt test) to identify the trend and mutation of the annual maximum flow series (1950–2006) of three hydrological stations in the Yiluo River Basin. Then we evaluated the performance of two types of design flood methods (the time series decomposition-synthesis method, the mixed distribution model) under the impacts of climate change and human activities on hydro-meteorological conditions. The results showed that (a) the design flood value obtained by the time series decomposition-synthesis method based on the series of the backward restore is larger than that obtained by the decomposition synthesis method based on the series of the forward restore; (b) when the return period is 100 years or less, the design flood value obtained by the mixed distribution model using the capacity ratio parameter estimation method is less than that obtained by the hybrid distribution model with simulated annealing parameter estimation method; and (c) both methods can overcome sequence inconsistency in design frequencies. This study provides insight into the frequency estimation of non-stationary time series of extreme values under the impacts of climate change and human activities on hydro-meteorological conditions.

Sign in / Sign up

Export Citation Format

Share Document