Assessment of methods for extreme value analysis of non-Gaussian wind effects with short-term time history samples

2014 ◽  
Vol 80 ◽  
pp. 75-88 ◽  
Author(s):  
Jie Ding ◽  
Xinzhong Chen
Keyword(s):  
Time History ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Wind Effects ◽  
Short Term ◽  
Value Analysis ◽  
Non Gaussian

Extreme Value Analyses of Dynamic Response Parameters of a Wind Tower Structure Under Short-Term Nonlinear Irregular Seastate

2017 ◽  
Author(s):  
Leonardo Nascimento ◽  
Luis Sagrilo ◽  
Gilberto Ellwanger
Keyword(s):  
Nonlinear Behavior ◽  
Nonlinear Effects ◽  
Extreme Value ◽  
Irregular Waves ◽  
Wind Loading ◽  
Extreme Value Analysis ◽  
Short Term ◽  
Gaussian Stochastic Process ◽  
Value Analysis ◽  
Non Gaussian

In the assessment of marine structures in shallow waters domain it is important to take into account the nonlinear (or non-Gaussian) nature of the irregular waves when predicting short and long-term responses of such structures. Other sources of nonlinearities in the response are also present due to some nonlinear effects such as: wet-dry surface effects, wind force on dry parts of the structure, drag term in Morison hydrodynamic force equation, etc. The estimation of the characteristic short-term extreme responses requires the extreme value analysis of a non-Gaussian stochastic process. There are many approaches available in literature which can be employed, such as: Hermite-based model, Weibull-fitting model, etc. In this paper two distinct Weibull fitting models (one based on the first two and other based on the first three moments of the response peaks sample) and Hermite-based models using both conventional and linear moments (L-moments) are investigated for the prediction of extreme short-term response of mono-column wind tower installed in a water depth of 20m and subject to wave, current and wind loading. The tower responses (load effects) time-histories are obtained by means of a time-domain finite element-based program using 3-D geometric nonlinear beam elements developed for the dynamic analysis of this type of structure. In this program, the nonlinear behavior of the irregular waves is modelled by means of the second order Sharma and Dean theory [1] and the wind forces are represented by a very simplified load model based on wind velocity simulated time-series and the obstruction area of the tower and blades.

scholarly journals Assessment of Methods for Short-Term Extreme Value Analysis of Riser Collision

2018 ◽  
Author(s):  
Ping Fu ◽  
Bernt J. Leira ◽  
Dag Myrhaug
Keyword(s):  
Time History ◽  
Extreme Value ◽  
Probability Analysis ◽  
Collision Probability ◽  
Extreme Value Analysis ◽  
Wave Loads ◽  
Short Term ◽  
Wake Effect ◽  
Time Step ◽  
Value Analysis

Risers are commonly arranged as clusters with relatively small spacing due to economic necessity. As a consequence, collision between risers becomes an essential problem. This study presents a comprehensive assessment of various methods for riser collision probability analysis. A pair of tandem arrangement risers subjected to combined current and wave loads is modelled. Three hours short-term simulation is performed in order to obtain the time history samples for the collision probability analysis. The wake effect due to the presence of the upstream riser is considered. The shortest distance between risers is calculated at each time step. Four methods for estimation of the extreme value distribution, e.g. Gumbel probability paper method, Weibull based method, average conditional exceedance rate method and moment based Hermit method, are presented, and the results obtained from different methods are compared and discussed.

Modeling European hot spells using extreme value analysis

2014 ◽  
Vol 58 (3) ◽  
pp. 193-207 ◽  
Author(s):  
C Photiadou ◽  
MR Jones ◽  
D Keellings ◽  
CF Dewes
Keyword(s):  
Extreme Value ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Hot Spells

scholarly journals Threshold selection in univariate extreme value analysis

Extremes ◽  
2021 ◽  
Author(s):  
Laura Fee Schneider ◽  
Andrea Krajina ◽  
Tatyana Krivobokova
Keyword(s):  
Mean Squared Error ◽  
Extreme Value ◽  
Hill Estimator ◽  
Small Samples ◽  
Extreme Value Analysis ◽  
Threshold Selection ◽  
Value Analysis ◽  
Wide Range ◽  
Asymptotic Mean Squared Error ◽  
The Hill

AbstractThreshold selection plays a key role in various aspects of statistical inference of rare events. In this work, two new threshold selection methods are introduced. The first approach measures the fit of the exponential approximation above a threshold and achieves good performance in small samples. The second method smoothly estimates the asymptotic mean squared error of the Hill estimator and performs consistently well over a wide range of processes. Both methods are analyzed theoretically, compared to existing procedures in an extensive simulation study and applied to a dataset of financial losses, where the underlying extreme value index is assumed to vary over time.

Spatial stochastic simulation to aid local extreme value analysis of cyclone-induced wave heights when numerical hydrodynamic simulations are scarce

2021 ◽  
Author(s):  
Jeremy Rohmer ◽  
Rodrigo Pedreros ◽  
Yann Krien
Keyword(s):  
Confidence Interval ◽  
Stochastic Simulation ◽  
Extreme Value ◽  
Return Level ◽  
Extreme Value Analysis ◽  
Percentage Error ◽  
Value Analysis ◽  
Return Levels ◽  
Wave Heights ◽  
Local Extreme

<p>To estimate return levels of wave heights (Hs) induced by tropical cyclones at the coast, a commonly-used approach is to (1) randomly generate a large number of synthetic cyclone events (typically >1,000); (2) numerically simulate the corresponding Hs over the whole domain of interest; (3) extract the Hs values at the desired location at the coast and (4) perform the local extreme value analysis (EVA) to derive the corresponding return level. Step 2 is however very constraining because it often involves a numerical hydrodynamic simulator that can be prohibitive to run: this might limit the number of results to perform the local EVA (typically to several hundreds). In this communication, we propose a spatial stochastic simulation procedure to increase the database size of numerical results with synthetic maps of Hs that are stochastically generated. To do so, we propose to rely on a data-driven dimensionality-reduction method, either unsupervised (Principal Component Analysis) or supervised (Partial Least Squares Regression), that is trained with a limited number of pre-existing numerically simulated Hs maps. The procedure is applied to the Guadeloupe island and results are compared to the commonly-used approach applied to a large database of Hs values computed for nearly 2,000 synthetic cyclones (representative of 3,200 years – Krien et al., NHESS, 2015). When using only a hundred of cyclones, we show that the estimates of the 100-year return levels can be achieved with a mean absolute percentage error (derived from a bootstrap-based procedure) ranging between 5 and 15% around the coasts while keeping the width of the 95% confidence interval of the same order of magnitude than the one using the full database. Without synthetic Hs maps augmentation, the error and confidence interval width are both increased by nearly 100%. A careful attention is paid to the tuning of the approach by testing the sensitivity to the spatial domain size, the information loss due to data compression, and the number of cyclones. This study has been carried within the Carib-Coast INTERREG project (https://www.interreg-caraibes.fr/carib-coast).</p>

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