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.

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.

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.

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.

Simulation Based Estimation of Extreme Response of Floating Offshore Structures

2007 ◽  
Author(s):  
A. Naess ◽  
O. Gaidai ◽  
P. S. Teigen
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Saddle Point ◽  
Computation Time ◽  
Offshore Structures ◽  
Saddle Point Method ◽  
Response Level ◽  
Random Seas ◽  
The Monte Carlo Method ◽  
Extreme Response

The paper presents a study of the extreme response statistics of a tension leg platform (TLP) subjected to random seas. Two different approaches are compared: A numerical integration method based on saddle point integration and the Monte Carlo method. While the saddle point method is a mathematically attractive technique, which gives numerically very accurate results at low computational costs at any response level, the advantage of the Monte Carlo method is its simplicity and versatility. It is demonstrated in this paper that the commonly assumed obstacle against using the Monte Carlo method for estimating extreme responses, i.e. excessive CPU time, can be circumvented, bringing the computation time down to affordable levels. The agreement between the two approaches is shown to be remarkably good.

scholarly journals Application of the Monte Carlo Method for the Assessment of Long-term Success in Keratoprosthesis Surgery

2002 ◽  
Vol 4 (3) ◽  
pp. 183-190 ◽  
Author(s):  
W. Hitzl ◽  
G. Grabner
Keyword(s):  
Monte Carlo ◽  
Visual Acuity ◽  
Survival Analysis ◽  
Monte Carlo Method ◽  
Surgical Techniques ◽  
Distribution Functions ◽  
Time Interval ◽  
Specific Method ◽  
The Monte Carlo Method

The comparison of different methods of keratoprosthesis (KP) regarding their long-term success, as far as visual acuity is concerned, is difficult: this is the case both as a standardized reporting method agreed upon by all research groups has not been reported and far less accepted, and as the quality of life for the patient not only depends on the level of visual acuity, but also quite significantly on the “survival time” of the implant. Therefore, an analysis of a single series of patients with Osteo–Odonto–Keratoprosthesis (OOKP) was performed. Statistical analysis methods used by others in similar groups of surgical procedures have included descriptive statistics, survival analysis and ANOVA. These methods comprised comparisons of empirical densities or distribution functions and empirical survival curves. It is the objective of this paper to provide an inductive statistical method to avoid the problems with descriptive techniques and survival analysis. This statistical model meets four important standards: (1) the efficiency of a surgical technique can be assessed within an arbitrary time interval by a new index (VAT-index), (2) possible autocorrelations of the data are taken into consideration and (3) the efficiency is not only stated by a point estimator, but also 95% point-wise confidence limits are computed based on the Monte Carlo method, and finally, (4) the efficiency of a specific method is illustrated by line and range plots for quick illustration and can also be used for the comparison of different other surgical techniques such as refractive techniques, glaucoma and retinal surgery.

scholarly journals Reliability Updating of Offshore Structures Subjected to Marine Growth

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 414
Author(s):  
Franck Schoefs ◽  
Thanh-Binh Tran
Keyword(s):  
Limit State ◽  
Dynamic Bayesian Network ◽  
Offshore Structures ◽  
Drag Forces ◽  
The North ◽  
The North Sea ◽  
Inspection And Maintenance ◽  
Inspection Data ◽  
Atlantic Area

Marine growth is a known problem for oceanic infrastructure and has been shown to negatively impact the reliability of bottom-fixed or floating offshore structures submitted to fatigue or extreme loading. Among other effects, it has been shown to change drag forces by increasing member diameters and modifying the roughness. Bio-colonization being highly random, the objective of this paper is to show how one-site inspection data increases reliability by decreasing uncertainties. This can be introduced in a reliability-based inspection framework for optimizing inspection and maintenance (here, cleaning). The modeling and computation are illustrated through the reliability analysis of a monopile in the European Atlantic area subjected to marine growth and according to the plastic collapse limit state. Based on surveys of structures in the North Sea, long-term stochastic modeling (space and time) of the marine growth thickness is first suggested. A Dynamic Bayesian Network is then developed for reliability updating from the inspection data. Finally, several realistic (10–20 measurements) inspection strategies are compared in terms of reliability improvement and the accuracy of reliability assessment.

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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