Extreme Response Prediction of Steel Risers Using a Four Parameter Distribution

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Miguel Alfonso Calderon Ibarra ◽  
Fernando Jorge Mendes de Sousa ◽  
Luís Volnei Sudati Sagrilo ◽  
Ying Min Low
Keyword(s):  
Water Depth ◽  
Extreme Values ◽  
Extreme Value Distribution ◽  
Response Prediction ◽  
Parameter Distribution ◽  
New Approach ◽  
Offshore Engineering ◽  
Extreme Response ◽  
Hermite Transformation ◽  
Non Gaussian

Short-term extreme response estimates are required in many areas of ocean and offshore engineering, such as steel risers design. As in many cases, the response in non-Gaussian, a theoretical solution, is usually not readily available for this purpose. Hermite transformation and Weibull-based models, among others, are some alternatives that have been used in connection with sampled response time series. In this work, a new approach is investigated. Recently, a four-parameter distribution known as the shifted generalized lognormal distribution (SGLD) has been presented in the literature. One of its main advantages is that it covers regions of skewness–kurtosis not covered by other distributions of common use in engineering. In this paper, the performance of this distribution is evaluated in the extreme values' estimation of the utilization ratios of steel riser sections. Three alternatives for using SGLD are investigated in two case studies of different dynamic behavior. The first one is a steel-lazy wave riser (SLWR) connected to a turret-moored FPSO (floating, production, storage and offloading unit) in 914 m water depth, and the second is a SLWR connected to a spread-mooring FPSO in a water depth of 1400 m. The results obtained by the SGLD-based analysis, which considered several simulation lengths, are compared to those obtained by means of an extreme value distribution fitted to episodical extremes obtained from many distinct realizations. The results of a traditional Weibull-fitting approach to the response peaks and those obtained with a Hermite transformation-based model are also presented for comparison.

Extreme Response Prediction of Steel Risers Using a Four Parameter Distribution

2017 ◽  
Author(s):  
Miguel Alfonso Calderon Ibarra ◽  
Fernando Jorge Mendes de Sousa ◽  
Luís Volnei Sudati Sagrilo ◽  
Ying Min Low
Keyword(s):  
Case Studies ◽  
Dynamic Behavior ◽  
Water Depth ◽  
Lognormal Distribution ◽  
Extreme Values ◽  
Extreme Value Distribution ◽  
Response Prediction ◽  
Parameter Distribution ◽  
Extreme Response ◽  
Hermite Transformation

Recently, a four-parameter distribution known as the shifted generalized lognormal distribution (SGLD) has been presented in the literature. One of its main advantages is that it covers regions of skewness-kurtosis not covered by other distributions of common use in engineering. In this paper, the performance of this distribution is evaluated in the extreme values’ estimation of the utilization ratios of steel riser sections. Three alternatives for using SGLD are investigated in two case studies of different dynamic behavior. The first one is a SLWR (steel-lazy wave riser) connected to a turret-moored FPSO in 914m water depth, and the second is a SLWR connected to a spread-mooring FPSO in a water depth of 1400m. The results obtained by the SGLD-based analysis, which considered several simulation lengths, are compared to those obtained by means of an extreme value distribution fitted to episodical extremes obtained from many distinct realizations. The results of a traditional Weibull-fitting approach to the response peaks and those obtained with and Hermite transformation-based model are also presented for comparison.

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.

Extreme Response of Nonlinear Structures With Low Damping Subjected to Stochastic Loading

1999 ◽  
Vol 121 (4) ◽  
pp. 255-260 ◽  
Author(s):  
A. Naess
Keyword(s):  
Extreme Values ◽  
Narrow Band ◽  
First Passage Times ◽  
Random Loading ◽  
First Passage ◽  
Stochastic Loading ◽  
Extreme Response ◽  
Non Gaussian ◽  
Extreme Responses ◽  
Passage Times

The response of structures with low damping to random loading is generally characterized by significant clumping of the large response peaks. This clumping is known to affect the extreme responses of the structure. In the paper, we shall propose an approximate method to account for this effect on first passage times and extreme values of narrow-band random vibrations, both Gaussian and non-Gaussian. The method is based on the concept of joint crossing rates of a stochastic process. This makes it possible to introduce a correlation structure to the sequence of peak values, allowing the introduction of an approximate estimate of the effect of clumping on large excursions of the underlying narrow-band process. The advantage of the proposed method is that explicit, closed-form expressions for the clumping effect on first passage times and extreme values are obtained. The method is illustrated by application to specific examples.

Assessment of Full-Scale Measurements With Regard to Extreme Hogging and Sagging Condition of Container Ships

2011 ◽  
Author(s):  
Wengang Mao ◽  
Jonas W. Ringsberg ◽  
Zhiyuan Li ◽  
Igor Rychlik
Keyword(s):  
Ultimate Strength ◽  
Full Scale ◽  
Practical Applications ◽  
Atlantic Trade ◽  
The North ◽  
Rice's Formula ◽  
Extreme Response ◽  
Hermite Transformation ◽  
Rice’S Formula ◽  
Non Gaussian

In the design of a vessel’s ultimate strength the extreme hogging condition is of great concern. Due to special properties of container ship structures, such as large bow flare and overhanging stern, wave-induced slamming makes the ship responses more skewed to sagging conditions. In particular in large sea states, the ratio between maximum sagging and hogging can be quite high. Hence, the sagging condition might be very crucial with respect to a ship’s ultimate strength. In this study, the extreme response caused by hogging and sagging is derived from upcrossing spectrums of ship responses. The Weibull fitting method and Rice’s formula for the computation of the upcrossing spectrum are discussed using full-scale measurements from a container vessel on the North Atlantic trade. The extreme ship responses are therefore predicted using the long-term upcrossing spectrum. In the case where the ship response can be approximately treated as a series of stationary Gaussian processes, the corresponding upcrossings are computed by the explicit Rice’s formula. For the non-Gaussian ship response, it is shown that the 4-moment Hermite transformation is an efficient approach to compute the corresponding upcrossing spectrums. The parameters in the transformation mainly depend on the wave environments and operation profiles. The relations between these parameters and the wave environments are needed if no measurement is available. However, according to the full-scale measurements, it is not possible to find general formulas to estimate the parameters in terms of wave environments or operation profiles for the practical applications.

Analyzing of UVLC system considering the effect of water depth

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Mustafa B. Al-Deen ◽  
Mazin Ali A. Ali ◽  
Zeyad A. Saleh
Keyword(s):  
Coastal Water ◽  
Water Depth ◽  
Bit Error Rate ◽  
Multiple Input Multiple Output ◽  
Visible Light Communications ◽  
New Approach ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Depth Water

Abstract This paper presents a new approach to discover the effect of depth water for underwater visible light communications (UVLC). The quality of the optical link was investigated with varying water depth under coastal water types. The performance of the UVLC with multiple input–multiple output (MIMO) techniques was examined in terms of bit error rate (BER) and data rate. The theoretical result explains that there is a good performance for UVLC system under coastal water.

New approach for quantifying water depth applied to the enigma of drowning of carbonate platforms

Geology ◽  
2002 ◽  
Vol 30 (9) ◽  
pp. 783 ◽  
Author(s):  
Gianni Mallarino ◽  
Robert H. Goldstein ◽  
Pietro Di Stefano
Keyword(s):  
Water Depth ◽  
Carbonate Platforms ◽  
New Approach

scholarly journals Quantification of operating reserves with high penetration of wind power considering extreme values

2020 ◽  
Vol 10 (2) ◽  
pp. 1693
Author(s):  
Johan S. Obando ◽  
Gabriel González ◽  
Ricardo Moreno
Keyword(s):  
Power Systems ◽  
Wind Power ◽  
Extreme Values ◽  
Extreme Value Distribution ◽  
Test System ◽  
Generalized Extreme Value Distribution ◽  
Mixed Integer ◽  
Linear Quadratic ◽  
High Penetration ◽  
Range Of Values

The high integration of wind energy in power systems requires operating reserves to ensure the reliability and security in the operation. The intermittency and volatility in wind power sets a challenge for day-ahead dispatching in order to schedule generation resources. Therefore, the quantification of operating reserves is addressed in this paper using extreme values through Monte-Carlo simulations. The uncertainty in wind power forecasting is captured by a generalized extreme value distribution to generate scenarios. The day-ahead dispatching model is formulated as a mixed-integer linear quadratic problem including ramping constraints. This approach is tested in the IEEE-118 bus test system including integration of wind power in the system. The results represent the range of values for operating reserves in day-ahead dispatching.

Consideration of Epistemic Uncertainty in Extreme Wave Height Estimation

2014 ◽  
Author(s):  
Ryota Wada ◽  
Takuji Waseda
Keyword(s):  
Gulf Of Mexico ◽  
Observational Data ◽  
Wave Height ◽  
Extreme Values ◽  
Epistemic Uncertainty ◽  
Extreme Value Distribution ◽  
Predictive Distribution ◽  
Extreme Value ◽  
Posterior Predictive Distribution ◽  
Value Estimation

Extreme value estimation of significant wave height is essential for designing robust and economically efficient ocean structures. But in most cases, the duration of observational wave data is not efficient to make a precise estimation of the extreme value for the desired period. When we focus on hurricane dominated oceans, the situation gets worse. The uncertainty of the extreme value estimation is the main topic of this paper. We use Likelihood-Weighted Method (LWM), a method that can quantify the uncertainty of extreme value estimation in terms of aleatory and epistemic uncertainty. We considered the extreme values of hurricane-dominated regions such as Japan and Gulf of Mexico. Though observational data is available for more than 30 years in Gulf of Mexico, the epistemic uncertainty for 100-year return period value is notably large. Extreme value estimation from 10-year duration of observational data, which is a typical case in Japan, gave a Coefficient of Variance of 43%. This may have impact on the design rules of ocean structures. Also, the consideration of epistemic uncertainty gives rational explanation for the past extreme events, which were considered as abnormal. Expected Extreme Value distribution (EEV), which is the posterior predictive distribution, defined better extreme values considering the epistemic uncertainty.

Probabilistic Modeling and Analysis of Riser Collision

2002 ◽  
Vol 124 (3) ◽  
pp. 132-138 ◽  
Author(s):  
Bernt J. Leira ◽  
Tore Holma˚s ◽  
Kjell Herfjord
Keyword(s):  
Water Depth ◽  
Deep Water ◽  
Time Domain ◽  
Collision Frequency ◽  
Joint Modeling ◽  
Fatigue Lifetime ◽  
Modeling And Analysis ◽  
Analysis And Design ◽  
Extreme Response ◽  
Probabilistic Context

Analysis and design of deep-water riser arrays requires that both collision frequency and resulting stresses in the pipes are addressed. Within a probabilistic context, the joint modeling of the current magnitude and surface floater motions must be taken into account. The present paper gives an outline of the general analysis setup, and response statistics obtained as a result of time domain simulations are described. Utilization of the analysis is also discussed in relation to estimation of extreme response and fatigue lifetime. As an example of application, a specific Spar buoy riser configuration at a water depth of 900m is considered.

scholarly journals Understanding and Predicting Nonlinear Turbulent Dynamical Systems with Information Theory

Atmosphere ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 248
Author(s):  
Nan Chen ◽  
Xiao Hou ◽  
Qin Li ◽  
Yingda Li
Keyword(s):  
Information Theory ◽  
Dynamical Systems ◽  
Information Criterion ◽  
Gaussian Mixture ◽  
Model Error ◽  
High Dimensions ◽  
New Approach ◽  
Fluctuation Dissipation ◽  
Spectrum Density ◽  
Non Gaussian

Complex nonlinear turbulent dynamical systems are ubiquitous in many areas. Quantifying the model error and model uncertainty plays an important role in understanding and predicting complex dynamical systems. In the first part of this article, a simple information criterion is developed to assess the model error in imperfect models. This effective information criterion takes into account the information in both the equilibrium statistics and the temporal autocorrelation function, where the latter is written in the form of the spectrum density that permits the quantification via information theory. This information criterion facilitates the study of model reduction, stochastic parameterizations, and intermittent events. In the second part of this article, a new efficient method is developed to improve the computation of the linear response via the Fluctuation Dissipation Theorem (FDT). This new approach makes use of a Gaussian Mixture (GM) to describe the unperturbed probability density function in high dimensions and avoids utilizing Gaussian approximations in computing the statistical response, as is widely used in the quasi-Gaussian (qG) FDT. Testing examples show that this GM FDT outperforms qG FDT in various strong non-Gaussian regimes.

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