Estimating Extreme Response of Drag Dominated Offshore Structures From Simulated Time Series of Structural Response

2007 ◽  
Author(s):  
A. Naess ◽  
O. Gaidai ◽  
S. Haver
Keyword(s):  
Response Time ◽  
Structural Response ◽  
Weather Conditions ◽  
Response Prediction ◽  
Offshore Structures ◽  
Rate Function ◽  
Extreme Response ◽  
Time Histories ◽  
Short Time

The paper presents a study of extreme response statistics of drag dominated offshore structures, showing a pronounced dynamic behaviour when subjected to harsh weather conditions. The key quantity for extreme response prediction is the mean up-crossing rate function, which can be simply extracted from simulated stationary response time histories. Present practise for obtaining adequate extremes for design purposes requires a number — say 20 or more — of 3-hour time domain analyses for several extreme sea states. For early phase considerations, it would be convenient if extremes of a reasonable accuracy could be obtained based on shorter and fewer simulations. It is therefore of interest to develop specific methods which make it possible to extract the necessary information from relatively short time histories. The method proposed in this paper opens up the possibility to predict simply and efficiently long-term extreme response statistics, which is an important issue for the design of offshore structures. A short description of this is given, but in the present paper the emphasis is on short-term analyses. The results presented are based on extensive simulation results for the Kvitebjo̸rn jacket structure, in operation on the Norwegian Continental Shelf. Specifically, deck response time histories for different sea states simulated from a MDOF model were used as the basis for our analyses.

Extreme Response Statistics of Fixed Offshore Structures Subjected to Ringing Loads

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
Oleg Gaidai ◽  
Jørgen Krokstad
Keyword(s):  
Structural Response ◽  
Offshore Structures ◽  
Wave Impact ◽  
Structural Support ◽  
Random Seas ◽  
Wave Basin ◽  
Extreme Response ◽  
Time Histories ◽  
Short Time ◽  
Diffraction Effects

This paper describes an efficient Monte Carlo based method for prediction of extreme response statistics of fixed offshore structures subjected to random seas. The nonlinear structural response known as “ringing” is studied, which is caused by the wave impact force on structural support units. Common challenge for design of such structures is a sound estimate of the hydrodynamic load including diffraction effects. The aim of the work was to develop specific methods which make it possible to extract the necessary information about the extreme response from relatively short time histories. The method proposed in this paper opens up the possibility to predict simply and efficiently both short-term and long-term extreme response statistics. The results presented are based on extensive simulation results for the large fixed platform operating on the Norwegian continental shelf. Structural response time histories, measured in MARINTEK (MT) wave basin lab, were used to validate numerical results.

A Monte Carlo Approach to Prediction of Extreme Response Statistics of Drag Dominated Offshore Structures

2007 ◽  
Author(s):  
A. Naess ◽  
O. Gaidai
Keyword(s):  
Monte Carlo ◽  
Weather Conditions ◽  
Offshore Structures ◽  
Drag Forces ◽  
Wave Elevation ◽  
The Monte Carlo Method ◽  
Strong Current ◽  
Highly Nonlinear ◽  
Extreme Response

The focus of the present paper is the extreme response statistics of drag dominated offshore structures subjected to harsh weather conditions. More specifically, severe sea states both with and without strong current are considered. The nature of the hydrodynamic forces acting on the structure becomes highly nonlinear. Additionally to the drag forces, the so called inundation effect due to the wave elevation, corrected to include second order waves, is also taken into account. In the present paper the Monte Carlo method along with a special extrapolation technique is applied. The proposed method opens up the possibility to predict simply and efficiently long-term extreme response statistics, which is an important issue for the offshore structures design.

Extreme Response Statistics of Fixed Offshore Structures Subjected to Ringing Loads

2009 ◽  
Author(s):  
Oleg Gaidai ◽  
Jo̸rgen Krokstad
Keyword(s):  
Monte Carlo ◽  
Structural Response ◽  
Offshore Structures ◽  
Statistical Technique ◽  
Computational Effort ◽  
Contour Method ◽  
Wave Impact ◽  
Random Seas ◽  
Extreme Response ◽  
Time Histories

Paper describes a method for prediction of extreme response statistics of fixed offshore structures subjected to random seas by Monte Carlo simulation. The nonlinear structural response know as “ringing” is studied, caused by the wave impact force on structural support units. Common challenge for design of such structures is a sound estimate of the hydrodynamic load inclusive diffraction effects. Structure is modeled as a multi-degree of freedom (MDOF) system and number of Monte Carlo simulations was performed to highlight extreme response in severe random seas. Since MDOF numerical simulation is costly, an efficient statistical technique was adopted, minimizing required computational effort. Environmental contour method was combined with accurate distribution tail extrapolation. The aim of the work was to develop specific methods which make it possible to extract the necessary information about the extreme response from relatively short time histories. The method proposed in this paper opens up the possibility to predict simply and efficiently both short-term and long-term extreme response statistics. The results presented are based on extensive simulation results for the large fixed platform operating on the Norwegian Continental Shelf. Measured response time histories were used to validate numerical results.

Long-Term Extreme Response Prediction of Mooring Lines Using Subset Simulation

2018 ◽  
Author(s):  
Darrell Leong ◽  
Ying Min Low ◽  
Youngkook Kim
Keyword(s):  
Monte Carlo ◽  
Response Prediction ◽  
Offshore Structures ◽  
Lead Times ◽  
Subset Simulation ◽  
Mooring Lines ◽  
Promising Alternative ◽  
Probabilistic Description ◽  
Extreme Response

Rigorous methods of probabilistic evaluations on long-term extremes are integral components in reliability research of offshore structures against overload events. Assessment across all conceivable sea states requires accounting for variabilities of long-term environmental loads and short-term stochastics, traditionally captured through extensive sampling or numerical expectation integration. The amount of environmental load variables render numerical integrations across high dimensions computationally prohibitive, while industry requirements of high return periods demand large Monte Carlo samples of timedomain dynamic analyses. Subset simulation offers a promising alternative to classic methods of statistical analysis, dividing ultra-low probability problems into subsets of intermediate probabilities. The methodology is uniquely advantageous for the assessment of heavy-tail overload events, which are unpredictably severe and occur at exceedingly rare frequencies. Subset simulation is experimented on a mooring case study situated in the hurricane-prone Gulf of Mexico, with the structure exposed to a joint-probabilistic description of wave, wind and current loads. The devised methodology is found to successfully evaluate hurricane-stimulated extreme events at ultra-low probabilities, beyond the feasible reach of Monte Carlo simulation at reasonable lead times.

Auto-control model building using machine learning regression for extreme response prediction

2020 ◽  
Vol 60 (1) ◽  
pp. 155
Author(s):  
Darrell Leong ◽  
Anand Bahuguni
Keyword(s):  
Machine Learning ◽  
Model Building ◽  
Response Prediction ◽  
Offshore Structures ◽  
Practical Implementation ◽  
Monte Carlo Data ◽  
Sea State ◽  
Extreme Load ◽  
Extreme Response

The long-term forecast of extreme response presents a daunting practical problem for offshore structures. These installations are subject to varying sea conditions, which amplify the need to account for the uncertainties of wave heights and periods across a given sea state. Analysis of each sea state involves numerically intensive non-linear dynamic analysis, leading to massive computational expense across the environmental scatter diagram. Recent research has proposed several effective solutions to predict long-term extreme responses, but not without drawbacks, such as the limitation to specific failure locations and the absence of error estimates. This paper explores the practical implementation of control variates as an efficiency enhancing post-processing technique. The model building framework exhibits the advantage of being fully defined from existing simulation results, without the need for external inputs to set up a control experiment. A composite machine learning regression model is developed and investigated for performance in correlating against Monte Carlo data. The sampling methodology presented possesses a crucial advantage of being independent of failure characteristics, allowing for the concurrent extreme response analyses of multiple components across the global structure without the need for re-analysis. The approach is applied on a simulated floating production storage and offloading unit in a site located in the hurricane-prone Gulf of Mexico, vulnerable to heavy-tailed extreme load events.

Long-Term Probability Distribution of Extreme Offshore Structural Response via an Efficient Time Simulation Method

2013 ◽  
Author(s):  
M. K. Abu Husain ◽  
N. I. Mohd Zaki ◽  
L. Lambert ◽  
Y. Wang ◽  
G. Najafian
Keyword(s):  
Probability Distribution ◽  
Extreme Values ◽  
Structural Response ◽  
Simulation Method ◽  
Offshore Structures ◽  
Random Wave ◽  
Wave Loading ◽  
Sampling Variability ◽  
Extreme Response

Offshore structures are exposed to random wave loading in the ocean environment and hence the probability distribution of the extreme values of their response to wave loading is required for their safe and economical design. To this end, the conventional simulation technique (CTS) is frequently used for predicting the probability distribution of the extreme values of response. However, this technique suffers from excessive sampling variability and hence a large number of simulated response extreme values (hundreds of simulated response records) are required to reduce the sampling variability to acceptable levels. A more efficient method (ETS) was recently introduced which takes advantage of the correlation between the extreme values of linear response and their corresponding response extreme values. The method has proved to be very efficient for both low and high-intensity sea states. In this paper, further development of this technique, which leads to more accurate estimates of the long term probability distribution of the extreme response, is reported.

A Monte Carlo Approach to Prediction of Extreme Response Statistics of Drag Dominated Offshore Structures

2008 ◽  
Vol 130 (4) ◽  
Author(s):  
Arvid Naess ◽  
Oleg Gaidai
Keyword(s):  
Monte Carlo ◽  
Weather Conditions ◽  
Offshore Structures ◽  
Drag Forces ◽  
Wave Elevation ◽  
The Monte Carlo Method ◽  
Strong Current ◽  
Highly Nonlinear ◽  
Extreme Response

The focus of the present paper is the extreme response statistics of drag dominated offshore structures subjected to harsh weather conditions. More specifically, severe sea states both with and without strong current are considered. The nature of the hydrodynamic forces acting on the structure becomes highly nonlinear. In addition to the drag forces, the so called inundation effect due to the wave elevation, corrected to include second order waves, is also taken into account. In the present paper, the Monte Carlo method along with a special extrapolation technique is applied. The proposed method opens up for the possibility to predict simply and efficiently long-term extreme response statistics, which is an important issue for the design of offshore structures.

Persistent Food Insecurity from Policy Failures in Pakistan

2008 ◽  
Vol 47 (4II) ◽  
pp. 817-834 ◽  
Author(s):  
Zakir Hussain ◽  
Waqar Akram
Keyword(s):  
Food Security ◽  
Food Insecurity ◽  
Green Revolution ◽  
Weather Conditions ◽  
Healthy Life ◽  
Food Grains ◽  
The People ◽  
Short Time ◽  
Policy Failures

Food security means, “All the people, all the time, have physical and economic access to sufficient, safe and nutritious food to meet their dietary needs and food preference for an active and healthy life” [FAO (1996)]. Three types of food insecurity generally exist in any country, which are: transitory food insecurity that is short time food insecurity occurs due to sporadic crises; chronic food insecurity that arises as a result of long term but not easily changed conditions; cyclic food insecurity that arises due to seasonal fluctuations. If cyclic food insecurity existed in any country for at least six months than it was called as chronic cyclic food insecurity and if it persisted less than six months than called as transitory cyclic food insecurity. Pakistan has made a lot of progress since independence in the field of agriculture in terms of production, yields, and growth in area under cultivation. Indus agriculture has experienced a Green Revolution and is striving for yellow and blue revolutions. However, it could have done far better. Though the overall growth of the Pakistan’s economy has largely been dependent upon the performance of agriculture, over the years, not much investment has been made for the development of this sector. Agriculture performance still depends upon, quite a lot, upon the weather conditions every year. The yields of most of crops are far below the levels achieved at the progressive farms (extension gap). From the Figure 1 it is evident that in the last decade (90s) food availability was increasing and then went down and formed the inverted u-shape. After that again fluctuating means there is no surety about food security. It is also comparable with agriculture growth rate. According to latest statistics in Pakistan as many as 50 million people are engaged in agriculture operations and produce only 25 million tons of food grains. As against this in India, 546 million people are engaged in agricultural operations and produce 176 million tons of food grains, in USA only 6 million people engaged in agriculture, produce 347 million of food grains.

On Efficient Long-Term Extreme Response Estimation for a Moored Floating Structure

2018 ◽  
Author(s):  
HyeongUk Lim ◽  
Lance Manuel ◽  
Ying Min Low
Keyword(s):  
Wave Train ◽  
Offshore Structures ◽  
Offshore Structure ◽  
Floating Structure ◽  
Sea State ◽  
Peak Period ◽  
Frequency Wave ◽  
Extreme Response ◽  
Term Response

This study investigates the use of efficient surrogate model development with the help of polynomial chaos expansion (PCE) for the prediction of the long-term extreme surge motion of a simple moored offshore structure. The structure is subjected to first-order and second-order (difference-frequency) wave loading. Uncertainty in the long-term response results from the contrasting sea state conditions, characterized by significant wave height, Hs, and spectral peak period, Tp, and their relative likelihood of occurrence; these two variables are explicitly included in the PCE-based uncertainty quantification (UQ). In a given sea state, however, response simulations must be run for any sampled Hs and Tp; in such simulations, typically, a set of random phases (and deterministic amplitudes) define a wave train consistent with the defined sea state. These random phases for all the frequency components in the wave train introduce additional uncertainty in the simulated waves and in the response. The UQ framework treats these two sources of uncertainty — from Hs and Tp on the one hand, and the phase vector on the other — in a nested manner that is shown to efficiently yield long-term surge motion extreme predictions consistent with more expensive Monte Carlo simulations, which serve as the truth system. Success with the method suggests that similar inexpensive surrogate models may be developed for assessing the long-term response of various offshore structures.

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