Response Statistics of Moored Offshore Structures

1995 ◽  
Vol 117 (3) ◽  
pp. 159-165 ◽  
Author(s):  
T. Kinoshita ◽  
S. Takase
Keyword(s):  
Experimental Data ◽  
Time Series ◽  
Extreme Values ◽  
Offshore Structures ◽  
Theoretical Estimation ◽  
Floating Structure ◽  
Correlation Parameter ◽  
Random Seas ◽  
Limited Length ◽  
Time Series Simulation

This paper discusses the prediction of extreme values for total first and second-order responses of a floating structure moored in random seas. It is hard to estimate the extreme values from time series simulation or experimental data of limited length. Several methods of theoretical estimation of the extreme values are examined. They are the SRSS formula introduced by Naess (1987, 1989), the modified SRSS with a correlation parameter, the SRSS with Naess (1987, 1989) corection factor, the approximate SRSS (Naess, 1989), and the formula proposed by authors previously. The results of those methods are compared, and it is confirmed that the last one is very promising.

Time Series Simulations of Wide-Band Spectra for Fatigue Tests of Offshore Structures

1984 ◽  
Vol 106 (4) ◽  
pp. 466-470 ◽  
Author(s):  
N. K. Lin ◽  
W. H. Hartt
Keyword(s):  
Time Series ◽  
Fatigue Testing ◽  
Wide Band ◽  
Simulation Method ◽  
Offshore Structures ◽  
Fatigue Tests ◽  
Time Series Model ◽  
Long Time ◽  
Band Spectra ◽  
Time Series Simulation

A time-series simulation method, based on the principle of time series modeling for dynamic systems, is used to reproduce a wide-band stress history from a prescribed stress spectral model for fatigue testing of offshore structures. The optimization procedures and stability of the time series model for the prescribed spectrum are presented and discussed. The optimization procedures are developed on the basis of the Levison-Durbin algorithm, which usually produces a stable time series model if the order of the time series model is even. An example is presented to demonstrate the applicability of the proposed method to long-time, high-cycle fatigue testing.

Analysis and Probabilistic Modeling of the Unstationary Ice Loads Stochastic Process, Based on Experiments With Models of Offshore Structures

2015 ◽  
Author(s):  
Petr Zvyagin ◽  
Kirill Sazonov
Keyword(s):  
Stochastic Process ◽  
Time Series ◽  
Statistical Analysis ◽  
Probabilistic Modeling ◽  
Offshore Structures ◽  
Offshore Structure ◽  
Sample Mean ◽  
Ice Loads ◽  
Time Series Simulation ◽  
Fuzzy C Means Algorithm

Experiments with models of platforms and offshore structures with vertical and inclined panels, which were conducted at Krylov Research Center (St. Petersburg), demonstrated that sometimes ice loads time series registered in these experiments cannot be considered as stationary. At the same time until nowadays methods and algorithms of probabilistic modeling were mainly based on the assumption of ice loads time series stationarity. That is because the analysis and modeling for stationary stochastic process is easier than for those unstationary. In the paper the method for determining the presence of unstationarity in ice loads time series, based on statistical analysis, is described. This method employs sample mean normality. Fuzzy C-means algorithm is used to cluster autocorrelation vectors, which are built for different fragments of time series. In the paper ice loads time series, got in experiments in ice tank with offshore structure columns and basement models, are investigated on their unstationarity. The algorithm of unstationary ice loads time series simulation is offered.

Correlation in the heart rate data

1998 ◽  
Vol 2 ◽  
pp. 141-148
Author(s):  
J. Ulbikas ◽  
A. Čenys ◽  
D. Žemaitytė ◽  
G. Varoneckas
Keyword(s):  
Nonlinear Dynamics ◽  
Experimental Data ◽  
Heart Rate ◽  
Time Series ◽  
Cardiovascular Function ◽  
Statistical Properties ◽  
Rate Data ◽  
Heart Rate Data ◽  
Dynamical Nature ◽  
Analysis Of Time Series

Variety of methods of nonlinear dynamics have been used for possibility of an analysis of time series in experimental physiology. Dynamical nature of experimental data was checked using specific methods. Statistical properties of the heart rate have been investigated. Correlation between of cardiovascular function and statistical properties of both, heart rate and stroke volume, have been analyzed. Possibility to use a data from correlations in heart rate for monitoring of cardiovascular function was discussed.

EXTREME VALUES OF SEA SURFACE TEMPERATURE ASSOCIATED WITH LONG-PERIOD PHENOMENA OCCURRED DURING 1960-2015 IN THE COLOMBIAN PACIFIC OCEAN

2017 ◽  
Author(s):  
Diaz Juan Navia ◽  
Diaz Juan Navia ◽  
Bolaños Nancy Villegas ◽  
Bolaños Nancy Villegas ◽  
Igor Malikov ◽  
...  
Keyword(s):  
Time Series ◽  
Pacific Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Extreme Values ◽  
Sea Surface ◽  
Absolute Maximum ◽  
Data Set ◽  
Arctic Oscillation Index ◽  
Colombian Pacific

Sea Surface Temperature Anomalies (SSTA), in four coastal hydrographic stations of Colombian Pacific Ocean, were analyzed. The selected hydrographic stations were: Tumaco (1°48'N-78°45'W), Gorgona island (2°58'N-78°11'W), Solano Bay (6°13'N-77°24'W) and Malpelo island (4°0'N-81°36'W). SSTA time series for 1960-2015 were calculated from monthly Sea Surface Temperature obtained from International Comprehensive Ocean Atmosphere Data Set (ICOADS). SSTA time series, Oceanic Nino Index (ONI), Pacific Decadal Oscillation index (PDO), Arctic Oscillation index (AO) and sunspots number (associated to solar activity), were compared. It was found that the SSTA absolute minimum has occurred in Tumaco (-3.93°C) in March 2009, in Gorgona (-3.71°C) in October 2007, in Solano Bay (-4.23°C) in April 2014 and Malpelo (-4.21°C) in December 2005. The SSTA absolute maximum was observed in Tumaco (3.45°C) in January 2002, in Gorgona (5.01°C) in July 1978, in Solano Bay (5.27°C) in March 1998 and Malpelo (3.64°C) in July 2015. A high correlation between SST and ONI in large part of study period, followed by a good correlation with PDO, was identified. The AO and SSTA have showed an inverse relationship in some periods. Solar Cycle has showed to be a modulator of behavior of SSTA in the selected stations. It was determined that extreme values of SST are related to the analyzed large scale oscillations.

scholarly journals Comprehensive Wind and Wave Statistics and Extreme Values for Design and Analysis of Marine Structures in the Adriatic Sea

2021 ◽  
Vol 9 (5) ◽  
pp. 522
Author(s):  
Marko Katalinić ◽  
Joško Parunov
Keyword(s):  
Adriatic Sea ◽  
Extreme Values ◽  
Wave Climate ◽  
Offshore Structures ◽  
Model Parameters ◽  
Peak Period ◽  
Significant Wave ◽  
Wave Statistics ◽  
Wave Heights ◽  
Almost All

Wind and waves present the main causes of environmental loading on seagoing ships and offshore structures. Thus, its detailed understanding can improve the design and maintenance of these structures. Wind and wave statistical models are developed based on the WorldWaves database for the Adriatic Sea: for the entire Adriatic Sea as a whole, divided into three regions and for 39 uniformly spaced locations across the offshore Adriatic. Model parameters are fitted and presented for each case, following the conditional modelling approach, i.e., the marginal distribution of significant wave height and conditional distribution of peak period and wind speed. Extreme significant wave heights were evaluated for 20-, 50- and 100-year return periods. The presented data provide a consistent and comprehensive description of metocean (wind and wave) climate in the Adriatic Sea that can serve as input for almost all kind of analyses of ships and offshore structures.

Prediction of Surge Motion Response of Floating Structure Using Kalman Smoother Adaptive Filters Based Time-Domain Volterra Model

2014 ◽  
Vol 660 ◽  
pp. 799-803
Author(s):  
Edwar Yazid ◽  
M.S. Liew ◽  
Setyamartana Parman ◽  
V.J. Kurian ◽  
C.Y. Ng
Keyword(s):  
Time Series ◽  
Transfer Function ◽  
Time Domain ◽  
Adaptive Filters ◽  
Low Frequency ◽  
Volterra Model ◽  
Floating Structure ◽  
Frequency Responses ◽  
Kalman Smoother ◽  
System Output

This work presents an approachto predict the low frequency and wave frequency responses (LFR and WFR) of afloating structure using Kalman smoother adaptive filters based time domain Volterramodel. This method utilized time series of a measured wave height as systeminput and surge motion as system output and used to generate the linear andnonlinear transfer function (TFs). Based on those TFs, predictions of surgemotion in terms of LFR and WFR were carried out in certain frequency ranges ofwave heights. The applicability of the proposed method is then applied in ascaled 1:100 model of a semisubmersible prototype.

scholarly journals Precision of continuous GPS velocities from statistical analysis of synthetic time series

2018 ◽  
Author(s):  
Christine Masson ◽  
Stephane Mazzotti ◽  
Philippe Vernant
Keyword(s):  
Time Series ◽  
Extreme Values ◽  
Regression Tree ◽  
Simple Method ◽  
Position Time Series ◽  
Gps Velocities ◽  
Synthetic Time Series ◽  
Continuous Gps ◽  
Automatic Treatment ◽  
Gps Noise

Abstract. We use statistical analyses of synthetic position time series to estimate the potential precision of GPS velocities. The synthetic series represent the standard range of noise, seasonal, and position offset characteristics, leaving aside extreme values. This analysis is combined with a new simple method for automatic offset detection that allows an automatic treatment of the massive dataset. Colored noise and the presence of offsets are the primary contributor to velocity variability. However, regression tree analyses show that the main factors controlling the velocity precision are first the duration of the series, followed by the presence of offsets and the noise (dispersion and spectral index). Our analysis allows us to propose guidelines, which can be applied to actual GPS data, that constrain the velocity accuracies (expressed as 95 % confidence limits) based on simple parameters: (1) Series durations over 8.0 years result in high velocity accuracies in the horizontal (0.2 mm yr−1) and vertical (0.5 mm yr−1); (2) Series durations of less than 4.5 years cannot be used for high-precision studies since the horizontal accuracy is insufficient (over 1.0 mm yr−1); (3) Series of intermediate durations (4.5–8.0 years) are associated with an intermediate horizontal accuracy (0.6 mm yr-1) and a poor vertical one (1.3 mm yr−1), unless they comprise no offset. Our results suggest that very long series durations (over 15–20 years) do not ensure a better accuracy compare to series of 8–10 years, due to the noise amplitude following a power-law dependency on the frequency. Thus, better characterizations of long-period GPS noise and pluri-annual environmental loads are critical to further improve GPS velocity precisions.

scholarly journals Identifying global patterns of stochasticity and nonlinearity in the Earth System

2016 ◽  
Author(s):  
Fernando Arizmendi ◽  
Marcelo Barreiro ◽  
Cristina Masoller
Keyword(s):  
Time Series ◽  
Extreme Values ◽  
Surface Air Temperature ◽  
Reanalysis Data ◽  
Earth System ◽  
Entropy Analysis ◽  
Solar Forcing ◽  
The Earth ◽  
Geographical Regions ◽  
Global Patterns

Abstract. By comparing time-series of surface air temperature (SAT, monthly reanalysis data from NCEP CDAS1 and ERA Interim) with respect to the top-of-atmosphere incoming solar radiation (the insolation), we perform a detailed analysis of the SAT response to solar forcing. By computing the entropy of SAT time-series, we also quantify the degree of stochasticity. We find spatial coherent structures which are characterized by high stochasticity and nearly linear response to solar forcing (the shape of SAT time-series closely follows that of the isolation), or vice versa. The entropy analysis also allows to identify geographical regions in which there are significant differences between the NCEP CDAS1 and ERA Interim datasets, which are due to the presence of extreme values in one dataset but not in the other. Therefore, entropy maps are a valuable tool for anomaly detection and model inter-comparisons.

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