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.

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.

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.

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.

An Assessment of Load and Response for Horizontal Slamming Loads From Model Scale Experiments

2018 ◽  
Author(s):  
Martin Storheim ◽  
Gunnar Lian
Keyword(s):  
Structural Response ◽  
Breaking Waves ◽  
Offshore Structures ◽  
Load Level ◽  
Material Behavior ◽  
Wave Impact ◽  
Large Variability ◽  
Slamming Load ◽  
Reasonable Approach ◽  
Wave Slamming

Steep breaking waves can result in high impact loads on offshore structures, and several model test campaigns have been conducted to assess the effect of horizontal wave slamming. High loads have been measured, and they can be challenging to withstand without significant deformation. For wave slamming problems it is common to estimate the characteristic slamming load and assume that this will give an equivalent characteristic response. One challenge related to the slamming load is that it has a large variability in load level, the duration of the load and the shape of the overall load pulse. This variability can have a large impact on the estimated response to the characteristic load, causing a similar or larger variability in response. Due to the sensitivity to the structural response, it may be difficult to interpret large amounts of such data to arrive at a relevant design load without making overly conservative assumptions. This paper investigates the sensitivity of the structural response to assumptions made in the material modelling and how the short term variability is affected if we instead of load use response indicators such as plastic strain and max deformation to arrive at a characteristic load. For this purpose, a simplified dynamic response model is created, and the recorded wave impact events can then be evaluated based on the predicted structural response from the simplified model. It was found that the structural response is sensitive to the structural configuration. The assumed material behavior and hydro-elastoplastic effects were identified to greatly affect the structural response. A reasonable approach to arrive at the q-annual response seems to be to first estimate the q-annual extreme slamming load, and then run the structural analysis on several of the measured slamming time series with the estimated q-annual extreme pressure.

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.

scholarly journals A Monte Carlo Simulation Approach in Non-linear Structural Dynamics Using Convolutional Neural Networks

2021 ◽  
Vol 7 ◽  
Author(s):  
Franz Bamer ◽  
Denny Thaler ◽  
Marcus Stoffel ◽  
Bernd Markert
Keyword(s):  
Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Convolutional Neural Network ◽  
Ground Motion ◽  
Structural Response ◽  
Structural Failure ◽  
Non Linear ◽  
Time Histories ◽  
Sample Set

The evaluation of the structural response statistics constitutes one of the principal tasks in engineering. However, in the tail region near structural failure, engineering structures behave highly non-linear, making an analytic or closed form of the response statistics difficult or even impossible. Evaluating a series of computer experiments, the Monte Carlo method has been proven a useful tool to provide an unbiased estimate of the response statistics. Naturally, we want structural failure to happen very rarely. Unfortunately, this leads to a disproportionately high number of Monte Carlo samples to be evaluated to ensure an estimation with high confidence for small probabilities. Thus, in this paper, we present a new Monte Carlo simulation method enhanced by a convolutional neural network. The sample-set used for this Monte Carlo approach is provided by artificially generating site-dependent ground motion time histories using a non-linear Kanai-Tajimi filter. Compared to several state-of-the-art studies, the convolutional neural network learns to extract the relevant input features and the structural response behavior autonomously from the entire time histories instead of learning from a set of hand-chosen intensity inputs. Training the neural network based on a chosen input sample set develops a meta-model that is then used as a meta-model to predict the response of the total Monte Carlo sample set. This paper presents two convolutional neural network-enhanced strategies that allow for a practical design approach of ground motion excited structures. The first strategy enables for an accurate response prediction around the mean of the distribution. It is, therefore, useful regarding structural serviceability. The second strategy enables for an accurate prediction around the tail end of the distribution. It is, therefore, beneficial for the prediction of the probability of failure.

On the Influence of Environmental Contour Method in Estimating Extreme Structural Response

2017 ◽  
Author(s):  
Erik Vanem
Keyword(s):  
Structural Reliability ◽  
Structural Response ◽  
Response Surfaces ◽  
Contour Method ◽  
Environmental Models ◽  
Structural Problems ◽  
Extreme Loads ◽  
Alternative Approach ◽  
Reliability Method ◽  
Extreme Response

Environmental contours are often applied in probabilistic structural reliability analysis in order to identify extreme environmental conditions that may give rise to extreme loads and responses. The perhaps most common way of establishing such environmental contours are based on the Rosenblatt transform and the IFORM approximation (Inverse First Order Reliability Method), but recently an alternative approach based on direct Monte Carlo simulations with importance sampling has been proposed. A recent comparison study revealed that there might be rather large differences in certain parts of the contours and for certain joint environmental models. In particular, the alternative contour method yields convex contours by design, whereas the traditional contours may be convex or non-convex. In this paper, comparison studies that include applications on a few structural examples are presented. Comparing the contours with known response surfaces, one may investigate how large the differences between the contour methods may be, and compare this to the correct extreme response estimated by simulation studies. These case studies clearly illustrate the influence of the environmental contour calculation method on the estimated extreme response. Whereas the different methods yield comparable results for some structural problems, they may give very different estimates of the extreme response for other. It is demonstrated that in certain cases, the estimates from some of the contour methods are highly conservative, whereas they in other cases might be very optimistic. The reason for these results are discussed and some requirements on the response functions for obtaining conservative estimates will be stated.

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.

scholarly journals Extended Environmental Contour Methods for Long-Term Extreme Response Analysis of Offshore Wind Turbines1

2020 ◽  
Vol 142 (5) ◽  
Author(s):  
Xiaolu Chen ◽  
Zhiyu Jiang ◽  
Qinyuan Li ◽  
Ye Li ◽  
Nianxin Ren
Keyword(s):  
Turbulence Intensity ◽  
Offshore Structures ◽  
Offshore Wind ◽  
Contour Method ◽  
Offshore Wind Turbine ◽  
Response Analysis ◽  
Modified Method ◽  
Extreme Response ◽  
Wind Turbulence

Abstract Environmental contour method is an efficient method for predicting the long-term extreme response of offshore structures. The traditional environmental contour is obtained using the joint distribution of mean wind speed, significant wave height, and spectral peak period. To improve the accuracy of traditional environmental contour method, a modified method was proposed considering the non-monotonic aerodynamic behavior of offshore wind turbines. Still, the modified method assumes constant wind turbulence intensity. In this paper, we extend the existing environmental contour methods by considering the wind turbulence intensity as a stochastic variable. The 50-year extreme responses of a monopile-based offshore wind turbine are compared using the extended environmental contour methods and the full long-term method. It is found that both the environmental contour method and the modified environmental contour method, with the wind turbulence intensity included as an individual variable, give more accurate predictions compared with those without. Using the full long-term method as a benchmark, this extended approach could reduce the nonconservatism of the environmental contour method and conservatism of the modified environmental contour method. This approach is effective under wind-dominated or combined wind-wave loading conditions, but may not be as important for wave-dominated conditions.

Sign in / Sign up

Export Citation Format

Share Document