Turkstra Models of Current Profiles

2009 ◽  
Author(s):  
Steven R. Winterstein ◽  
Sverre Haver ◽  
Einar Nygaard
Keyword(s):  
Deep Water ◽  
Time Domain ◽  
Current Speed ◽  
Empirical Orthogonal Functions ◽  
Orthogonal Functions ◽  
Direct Function ◽  
Extreme Loads ◽  
Spatial Correlation Structure ◽  
Domain Prediction ◽  
Basis Vectors

Design of deep-water structures requires accurate models of currents versus water depth. Common models are N-year profiles, which conservatively assume the N-year extreme current speed occurs simultaneously at each depth. To address this conservatism, we introduce Turkstra models of current profiles here. These yield a set of current profiles, each of which coincides with the N-year profile at a single depth and is reduced elsewhere. The degree of reduction is a direct function of the spatial correlation structure of the current process. Results are shown for a deep-water North Sea site, and compared with time-domain prediction of extreme loads for linear and drag load mechanisms. Extensions are suggested to combine these methods with procedures such as Empirical Orthogonal Functions, permitting the data to define the most economical set of basis vectors upon which the Turkstra logic is applied.

Turkstra Profiles of North Sea Currents: Wider Than You’d Think

2011 ◽  
Author(s):  
Steven R. Winterstein ◽  
Sverre Haver ◽  
Alok K. Jha ◽  
Borge Kvingedal ◽  
Einar Nygaard
Keyword(s):  
North Sea ◽  
Deep Water ◽  
Current Speed ◽  
Marine Structures ◽  
Marginal Analysis ◽  
Water Currents ◽  
Extreme Loads ◽  
Peak Over Threshold ◽  
Direct Contrast ◽  
Sea Currents

To design marine structures in deep water, currents must be modelled accurately as a function of depth. These models often take the form of T-year profiles, which assume the T-year extreme current speed occurs simultaneously at each depth. To better reflect the spatial correlation in the current speeds versus depth, we have recently introduced Turkstra current profiles. These assign the T-year speed at one depth, and “associated” speeds expected to occur simultaneously at other depths. Two essentially decoupled steps are required: (1) marginal analysis to estimate T-year extremes, and (2) some type of regression to find associated values. The result is a set of current profiles, each of which coincides with the T-year profile at a single depth and is reduced elsewhere. Our previous work with Turkstra profiles suggested that, when applied in an unbiased fashion, they could produce unconservative estimates of extreme loads. This is in direct contrast to the findings of Statoil, whose similar (“CCA”) current profiles have generally been found to yield conservative load estimates. This paper addresses this contradiction. In the process, we find considerable differences can arise in precisely how one performs steps 1 and 2 above. The net finding is to favor methods that properly emphasize the upper tails of the data—e.g., using peak-over-threshold (“POT”) data, and regression based on class means—rather than standard analyses that weigh all data equally. By applying such tail-sensitive methods to our dataset, we find the unconservative trend in Turkstra profiles to essentially vanish. For our data, these tail-fit results yield profiles with both larger marginal extremes, and broader profiles surrounding these extremes—hence the title of this paper.

Effects of Empirical Orthogonal Functions in Simplification of Structural Response of a Deep-Water Offshore System Under Current Profile Loading

2020 ◽  
Author(s):  
Michael Binsar Lubis ◽  
Mehrdad Kimiaei ◽  
Hongwei An ◽  
Reza Azarhoush
Keyword(s):  
Deep Water ◽  
Structural Response ◽  
Time History ◽  
Empirical Orthogonal Functions ◽  
Response Analysis ◽  
Good Representation ◽  
Current Profile ◽  
Orthogonal Functions ◽  
Recent Developments ◽  
Guidelines And Standards

Abstract Typical recommended current profiles for marine operations can be found in offshore engineering guidelines and standards. However, for some offshore components (e.g. risers, umbilicals, risers) typical simplified current profiles can easily lead to unrealistic and conservative results. Due to recent developments in current measuring technology, current speed for deep water location can be easily acquired. However, the current speeds are usually recorded for long periods and in many measurement points along the water column. Hence, finding the extreme current profile based on the recorded time-history data is not an easy task since it needs excessive computational efforts. To determine the overall response of an offshore system, various methods have been developed to minimize the required computational efforts in working with big number of irregular current profiles. Mode-based analysis using empirical orthogonal functions is one of these methods. Total number of the utilized modes plays an important role in the numerical complexity of the problem as well as the accuracy of the results. In this study, for a given deep water location, the effects of the reduced number of modes are investigated through response analysis of a simple vertical fixed slender structure under thousands of current profiles. It is found that the reduced-mode profile can produce a good representation of the measured current profile, however it tends to underestimate the structural response.

scholarly journals Modes of the BiOS-driven Adriatic Sea thermohaline variability

2021 ◽  
Author(s):  
Cléa Lumina Denamiel ◽  
Iva Tojčić ◽  
Petra Pranić ◽  
Ivica Vilibić
Keyword(s):  
Time Series ◽  
Thermohaline Circulation ◽  
Climate Model ◽  
Adriatic Sea ◽  
Decadal Variability ◽  
Current Speed ◽  
Empirical Orthogonal Functions ◽  
Climate Simulation ◽  
Orthogonal Functions ◽  
The Impact

Abstract In this study the impact of the Adriatic-Ionian Bimodal Oscillating System (BiOS) on the interannual to decadal variability of the Adriatic Sea thermohaline circulation is quantified during the 1987-2017 period with the numerical results of the Adriatic Sea and Coast (AdriSC) historical kilometer-scale climate simulation. The time series associated with the first five Empirical Orthogonal Functions (EOFs) computed from the salinity, temperature and current speed monthly detrended anomalies at 1-km resolution are correlated to the BiOS signal. First, it is found that the AdriSC climate model is capable to reproduce the BiOS-driven phases derived from in-situ observations along a long-term monitoring transect in the middle Adriatic. Then, for the entire Adriatic basin, high correlations to the 2-year delayed BiOS signal are obtained for the salinity and current speed first two EOF time series at 100 m depth and the sea-bottom Finally, the physical interpretation of the EOF spatial patterns reveals that Adriatic bottom temperatures are more influenced by the dense water circulation than the BiOS. These findings confirmed and generalized the known dynamics derived previously from observations, and the AdriSC climate model can thus be used to better understand the past and future BiOS-driven physical processes in the Adriatic Sea.

Empirical Methods in Short-Term Climate Prediction

2006 ◽  
Author(s):  
Huug van den Dool
Keyword(s):  
Climate Prediction ◽  
Empirical Orthogonal Functions ◽  
Data Sets ◽  
Seasonal Forecasts ◽  
Short Term ◽  
Orthogonal Functions ◽  
Ocean Atmosphere Interaction ◽  
Global Coverage ◽  
Atmosphere Interaction ◽  
Global Data

This clear and accessible text describes the methods underlying short-term climate prediction at time scales of 2 weeks to a year. Although a difficult range to forecast accurately, there have been several important advances in the last ten years, most notably in understanding ocean-atmosphere interaction (El Nino for example), the release of global coverage data sets, and in prediction methods themselves. With an emphasis on the empirical approach, the text covers in detail empirical wave propagation, teleconnections, empirical orthogonal functions, and constructed analogue. It also provides a detailed description of nearly all methods used operationally in long-lead seasonal forecasts, with new examples and illustrations. The challenges of making a real time forecast are discussed, including protocol, format, and perceptions about users. Based where possible on global data sets, illustrations are not limited to the Northern Hemisphere, but include several examples from the Southern Hemisphere.

scholarly journals Spatio-Temporal Patterns of Mass Changes in Himalayan Glaciated Region from EOF Analyses of GRACE Data

2021 ◽  
Vol 13 (2) ◽  
pp. 265
Author(s):  
Harika Munagapati ◽  
Virendra M. Tiwari
Keyword(s):  
Temperature Anomaly ◽  
Mass Gain ◽  
Snow Accumulation ◽  
Empirical Orthogonal Functions ◽  
Total Water ◽  
Orthogonal Functions ◽  
Grace Data ◽  
Grace Satellite ◽  
Spatio Temporal ◽  
Gravity Recovery

The nature of hydrological seasonality over the Himalayan Glaciated Region (HGR) is complex due to varied precipitation patterns. The present study attempts to exemplify the spatio-temporal variation of hydrological mass over the HGR using time-variable gravity from the Gravity Recovery and Climate Experiment (GRACE) satellite for the period of 2002–2016 on seasonal and interannual timescales. The mass signal derived from GRACE data is decomposed using empirical orthogonal functions (EOFs), allowing us to identify the three broad divisions of HGR, i.e., western, central, and eastern, based on the seasonal mass gain or loss that corresponds to prevailing climatic changes. Further, causative relationships between climatic variables and the EOF decomposed signals are explored using the Granger causality algorithm. It appears that a causal relationship exists between total precipitation and total water storage from GRACE. EOF modes also indicate certain regional anomalies such as the Karakoram mass gain, which represents ongoing snow accumulation. Our causality result suggests that the excessive snowfall in 2005–2008 has initiated this mass gain. However, as our results indicate, despite the dampening of snowfall rates after 2008, mass has been steadily increasing in the Karakorum, which is attributed to the flattening of the temperature anomaly curve and subsequent lower melting after 2008.

Identifying VIV Vibration Modes by Use of the Empirical Orthogonal Functions Technique

2002 ◽  
Author(s):  
Gudmund Kleiven
Keyword(s):  
Model Test ◽  
Vibration Mode ◽  
Multivariate Data ◽  
Empirical Orthogonal Functions ◽  
Mode Shapes ◽  
Data Sets ◽  
Vibration Modes ◽  
Ocean Engineering ◽  
Orthogonal Functions ◽  
Related Technique

The Empirical Orthogonal Functions (EOF) technique has widely being used by oceanographers and meteorologists, while the Singular Value Decomposition (SVD being a related technique is frequently used in the statistics community. Another related technique called Principal Component Analysis (PCA) is observed being used for instance in pattern recognition. The predominant applications of these techniques are data compression of multivariate data sets which also facilitates subsequent statistical analysis of such data sets. Within Ocean Engineering the EOF technique is not yet widely in use, although there are several areas where multivariate data sets occur and where the EOF technique could represent a supplementary analysis technique. Examples are oceanographic data, in particular current data. Furthermore data sets of model- or full-scale data of loads and responses of slender bodies, such as pipelines and risers are relevant examples. One attractive property of the EOF technique is that it does not require any a priori information on the physical system by which the data is generated. In the present paper a description of the EOF technique is given. Thereafter an example on use of the EOF technique is presented. The example is analysis of response data from a model test of a pipeline in a long free span exposed to current. The model test program was carried out in order to identify the occurrence of multi-mode vibrations and vibration mode amplitudes. In the present example the EOF technique demonstrates the capability of identifying predominant vibration modes of inline as well as cross-flow vibrations. Vibration mode shapes together with mode amplitudes and frequencies are also estimated. Although the present example is not sufficient for concluding on the applicability of the EOF technique on a general basis, the results of the present example demonstrate some of the potential of the technique.

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