A Simple and Robust Method for Calculating Return Periods of Ocean Waves

2018 ◽  
Author(s):  
Edward B. L. Mackay ◽  
Lars Johanning
Keyword(s):  
Ocean Waves ◽  
New Method ◽  
Threshold Analysis ◽  
Return Periods ◽  
Peaks Over Threshold ◽  
Short Term ◽  
Sea State ◽  
Individual Wave ◽  
Buoy Measurements

A new method is introduced for combining the long-term distribution of sea states with the short-term distribution of individual wave or crest heights, conditional on sea state. The method uses a Monte Carlo approach to simulate random realisations of the maximum wave or crest height in each sea state. A peaks-over-threshold analysis is conducted on the random maxima in each sea state in order to estimate the long-term distribution of individual wave or crest heights. The new method is significantly simpler than existing methods such as the equivalent storm approach, requires fewer assumptions and has similar computational times. The new method is applied to a 35 year dataset of wave buoy measurements and is shown to produce almost identical estimates of return values of individual crest heights to the equivalent storm method.

Analysis of Short-Term and Long-Term Wave Statistics by Time Domain Simulations Statistics

2017 ◽  
Author(s):  
Øistein Hagen ◽  
Ida Håøy Grue ◽  
Jørn Birknes-Berg ◽  
Gunnar Lian ◽  
Kjersti Bruserud
Keyword(s):  
Wave Height ◽  
Time Domain ◽  
Statistical Distributions ◽  
Short Term ◽  
Sea State ◽  
Wave Statistics ◽  
Individual Wave ◽  
Wave Heights ◽  
Crest Height

In the design of new structures and assessment of existing structures, short- and long term statistical distributions of wave height, crest height and wave periods, as well as joint distributions, are important for structural integrity assessment. It is important to model the statistical distributions accurately to calculate wave design criteria and to assess fatigue life. A detailed study of the wave statistics for an offshore location at the Norwegian Continental Shelf field is carried out. Extensive time domain simulations for the complete scatter diagram of possible sea states are carried out by a second order wave model. Time series of the surface elevation are generated for JONSWAP and Torsethaugen wave spectra, and for several wave spreading models. Statistics for individual wave heights, crest heights and wave periods are established. The simulated results for the short-term statistics are compared with existing short term models that are commonly used, viz. the Forristal, Næss and Rayleigh wave height distributions, and the Forristall 2nd order crest height distribution. Also, parameterized distributions for wave height and for crest height are fitted to the simulated data. The long-term distributions F(H) and F(C) of all simulated individual wave heights H and crest heights C are determined by weighting the simulations with the long-term probability of occurrence of the sea state. Likewise, the long-term distributions F(Hmax) and F(Cmax) of the maximum simulated individual wave heights Hmax and crest heights Cmax in the sea states are determined. The design criteria for return periods R = 1, 10, 100 and 10 000 years are determined from the appropriate quantile levels. The effect of statistical uncertainty is investigated by comparing the confidence intervals for the estimated extreme values results as function of the number N of 3-hour time domain simulations per sea state for 10<N<500.

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

Strength Performance of Steel Catenary Riser Using Short Term and Long Term Analysis Methodologies

2016 ◽  
Author(s):  
Feng Wang ◽  
Roger Burke ◽  
Anil Sablok ◽  
Kristoffer H. Aronsen ◽  
Oddgeir Dalane
Keyword(s):  
Bending Moment ◽  
Current Profile ◽  
Short Term ◽  
Sea State ◽  
Strength Performance ◽  
Steel Catenary Riser ◽  
Analysis Methodology ◽  
Riser Design ◽  
Term Analysis

Strength performance of a steel catenary riser tied back to a Spar is presented based on long term and short term analysis methodologies. The focus of the study is on response in the riser touch down zone, which is found to be the critical region based on short term analysis results. Short term riser response in design storms is computed based on multiple realizations of computed vessel motions with various return periods. Long term riser response is based on vessel motions for a set of 45,000 sea states, each lasting three hours. The metocean criteria for each sea state is computed based on fifty six years of hindcast wind and wave data. A randomly selected current profile is used in the long term riser analysis for each sea state. Weibull fitting is used to compute the extreme riser response from the response of the 45,000 sea states. Long term analysis results in the touch down zone, including maximum bending moment, minimum effective tension, and maximum utilization using DNV-OS-F201, are compared against those from the short term analysis. The comparison indicates that the short term analysis methodology normally followed in riser design is conservative compared to the more accurate, but computationally more expensive, long term analysis methods. The study also investigates the important role that current plays in the strength performance of the riser in the touch down zone.

Estimation of Extreme Ship Response

2012 ◽  
Vol 56 (01) ◽  
pp. 23-34
Author(s):  
Wengang Mao ◽  
Igor Rychlik
Keyword(s):  
Gaussian Model ◽  
Container Ship ◽  
Short Term ◽  
Sea State ◽  
High Quantiles ◽  
Alternative Approach ◽  
Extreme Response ◽  
Heading Angle ◽  
Ship Speed

In practice the severity of ship response is measured by high quantiles of long-term distribution of the response. The distribution is estimated by combining the short-term distribution of the response with a long-term probability distribution of encountered sea states. The paper describes an alternative approach, the so-called Rice's method, based on estimation of expected number of upcrossings of high levels by stress during 1 year. The method requires description of long-term variability of the standard deviation, skewness, kurtosis, and zero upcrossing frequency of ship response. It is assumed that the parameters are functions of encountered significant wave height, heading angle, and ship speed. The relation can be estimated from the measured stresses or computed by dedicated software assuming rigid ship hull model. Then Winterstein's transformed Gaussian model is used to estimate the upcrossing rates of response during a sea state. The proposed method is validated using the full-scale measurements of a 2,800 TEU container ship during the first 6 months of 2008. Numerical estimation of 4,400 TEU container ship extreme of the extreme response for a 4400 TEU container ship illustrates the approach when no measurements are available.

How Should We Combine Long and Short Term Wave Height Distributions?

2008 ◽  
Author(s):  
George Z. Forristall
Keyword(s):  
Time Series ◽  
Extreme Value Distribution ◽  
Ocean Engineering ◽  
Short Term ◽  
Method Of Calculation ◽  
Individual Wave ◽  
Long Time ◽  
Wave Heights ◽  
The Individual

Estimating the maximum wave or crest height that will occur in a long return interval is one of the fundamental problems for ocean engineers. Long time series of individual wave heights are not available. The calculations must start with measured or hindcast time series of significant wave heights. An extreme value distribution is fit to that data. The resulting long term distribution is then combined with a short term distribution for the individual heights. This study is concerned with finding the most accurate methods for that calculation. The basic tool is the Borgman integral, but it has been applied in many different ways. Theoretical derivations do not clearly indicate which method is most accurate, and time series of measurements long enough for accurate tests do not exist. These problems were circumvented in this study by constructing very long simulated time series with known distributions. Both initial value and storm based methods were tested. The correct method of calculation depends on what question is being asked. The distribution of the maximum wave heights in a six hour interval is different than the distribution of the maxima of all of the waves. The distribution of the maxima in a storm is different than the distribution of the maxima in an interval. We believe that the finding the maximum in a storm is the most appropriate question for ocean engineering design. The Tromans and Vanderschuren (1995, Proc. Offshore Tech. Conf., OTC 7683) method accurately matches the results from our storm simulations.

Prediction of Extreme Roll Motion on an FPSO Using Long Term Statistics

2002 ◽  
Author(s):  
Tone M. Vestbo̸stad ◽  
Sverre Haver ◽  
Odd Jan Andersen ◽  
Arne Albert
Keyword(s):  
Probability Distribution ◽  
Wind Waves ◽  
Weather Conditions ◽  
Full Scale ◽  
Roll Motion ◽  
Short Term ◽  
Sea State ◽  
Term Analysis

This paper presents a method for predicting extreme roll motion on an FPSO using long-term statistics. The method consists of a long-term simulation where a database of consecutive short-term sea states with combined weather conditions, including direction and magnitude of wind, wind waves and swell waves, is used. The vessel heading in given weather conditions is simulated. For each combined sea state, the short-term roll motion maxima are calculated to form a long-term probability distribution, and the extreme roll motion, e.g. the 100-year value, can be estimated from the distribution. For an example FPSO, the results from the long-term analysis have been compared with full-scale measurements, giving a validation of the method. This paper is a shortened version of [1].

Wave Distributions and Sampling Variability

2007 ◽  
Author(s):  
O̸istein Hagen
Keyword(s):  
Wave Height ◽  
Extreme Values ◽  
Height Distribution ◽  
Short Term ◽  
Sea State ◽  
Sampling Variability ◽  
Wave Statistics ◽  
Individual Wave ◽  
Wave Heights ◽  
Crest Height

The paper describes the effect of sampling variability on the predicted extreme individual wave height and the predicted extreme individual crests height for long return periods, such as for the 100-year maximum wave height and 100-year maximum crest height. We show that the effect of sampling variability is different for individual crest or wave height as compared to for significant wave height. The short term wave statistics is modeled by the Forristall crest height distribution and the Forristall wave height distribution [3,4]. Samples from the 3-hour Weibull distribution are simulated for 100.000 years period, and the 100-year extreme values for wave heights and crest heights determined for respectively 20 minute and 3 hour sea states. The simulations are compared to results obtained by probabilistic analysis. The paper shows that state of the art analysis approaches using the Forristall distributions give about unbiased estimates for extreme individual crest or wave height if implemented appropriately. Direct application of the Forristall distributions for 3-hour sea state parameters give long term extremes that are biased low, and it is shown how the short term distributions can be modified such that consistent results for 20 minute and 3 hour sea states are obtained. These modified distributions are expected applicable for predictions based on hindcast sea state statistics and for the environmental contour approach.

Wave Sector Dependent Contour Lines

2007 ◽  
Author(s):  
Gro Sagli Baarholm ◽  
Sverre Haver ◽  
Carl M. Larsen
Keyword(s):  
Linear Response ◽  
Contour Line ◽  
Exceedance Probability ◽  
Wave Direction ◽  
Short Term ◽  
Sea State ◽  
Extreme Loads ◽  
Contour Lines ◽  
Term Analysis

This paper is concerned with estimating the response value corresponding to given annual exceedance probability. In principle, this requires that a full long term analysis is executed. For a linear response this can easily be done. For a non-linear response quantity however, where time domain simulations are required in order to obtain the short term stochastic structure a full long term analysis will be time consuming. An approximate method to determine the long-term extremes by considering only a few short term sea states is outlined. All sea states corresponding to a certain probability of occurrence and are given by a contour line of Hs, Tp for each wave direction. The advantage of the method is that a proper estimate of the long term extreme can be obtained by considering the most unfavourable sea state along the contour line. This will make possible practical estimation of the extreme loads the structure is exposed to. The purpose of the present paper is to illustrate how to apply directional contour lines in order to obtain a characteristic design value according to requirements regarding the marginal exceedance probability.

scholarly journals Sea State Decadal Variability in the North Atlantic: A Review

Climate ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 173
Author(s):  
Antoine Hochet ◽  
Guillaume Dodet ◽  
Fabrice Ardhuin ◽  
Mark Hemer ◽  
Ian Young
Keyword(s):  
Ocean Circulation ◽  
North Atlantic ◽  
Decadal Variability ◽  
Ocean Waves ◽  
Sea State ◽  
Ship Routing ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

Long-term changes of wind-generated ocean waves have important consequences for marine engineering, coastal management, ship routing, and marine spatial planning. It is well-known that the multi-annual variability of wave parameters in the North Atlantic is tightly linked to natural fluctuations of the atmospheric circulation, such as the North Atlantic Oscillation. However, anthropogenic climate change is also expected to influence sea states over the long-term through the modification of atmospheric and ocean circulation and melting of sea ice. Due to the relatively short duration of historical sea state observations and the significant multi-decadal variability in the sea state signal, disentangling the anthropogenic signal from the natural variability is a challenging task. In this article, the literature on inter-annual to multi-decadal variability of sea states in the North Atlantic is reviewed using data from both observations and model reanalysis.

Long Term Analysis of Steep and Breaking Wave Properties by Event Matching

2018 ◽  
Author(s):  
Thomas B. Johannessen ◽  
Øystein Lande
Keyword(s):  
Wave Breaking ◽  
Offshore Structures ◽  
Return Periods ◽  
Short Term ◽  
Nonlinear Load ◽  
Load Effect ◽  
Strongly Nonlinear ◽  
Load Effects ◽  
Term Analysis

Offshore structures are typically required to withstand extreme and abnormal load effects with annual probabilities of occurrence of 10−2 and 10−4 respectively. For linear or weakly nonlinear problems, the load effects with the prescribed annual probabilities of occurrence are typically estimated as a relatively rare occurrence in the short term distribution of 100 year and 10 000 year seastates. For strongly nonlinear load effects, it is not given that an extreme seastate can be used reliably to estimate the characteristic load effect. The governing load may occur as an extremely rare event in a much lower seastate. In attempting to model the load effect in an extreme seastate, the relevant short term probability level is not known nor is it known whether the physics of the wave loading is captured correctly in an extreme seastate. Examples of such strongly nonlinear load effects are slamming loads on large volume offshore structures or wave in deck loads on jacket structures subject to seabed subsidence. The present paper is concerned with the long term distribution of strongly nonlinear load effects and a methodology is proposed which incorporates CFD analysis in a long term Monte Carlo analysis of crest elevations and wave kinematics. Based on a long term time domain simulation of a linear surface elevation, a selection of events is run in CFD in order to obtain a database of linear and corresponding fully nonlinear wave fields with the possibility of wave breaking included. In the subsequent long term analysis, a large linear event is then replaced by the closest matching event in the database. A technique is developed to Froude scale the database results and shift the origin in time and plane so that the database of typically only 100 events give a close match to all the events in the simulation. The method is applied to the simple case of drag loading on a cylinder which is truncated above the still water level such that only the largest waves impact with the structure. It is observed that whereas the Event Matching method agree well with a second order model for return periods lower than 100 years, the loading on the cylinder is significantly larger for longer return periods. The deviation is caused by the increasing dominance of wave braking in the largest crest and illustrates the importance of incorporating wave breaking in the analysis of wave in deck loading problems.

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