Fatigue Analysis of Offshore Structures

A primitive equation ocean model has been applied to the study of mesoscale ocean dynamics and prediction using sea surface height information derived from a satellite‐borne altimeter. Results from a model of the Gulf of Mexico were directly compared with the altimeter data from GEOS 3 and Seasat reported by Marsh et al. (1984) and in situ hydrographic data analyzed by Maul and Herman (1985). In the eastern Gulf the amplitude and position of relative maxima of sea surface height variability were found to be similar for model, altimeter, and in situ data. In the central Gulf the model and the altimeter‐derived sea surface height variability maps were similar but differed considerably from the in situ results. The mean sea surface in the model and from the in situ data also agreed well in the eastern Gulf, but there was little agreement in the central Gulf. While there is no geoid adequate for determining an independent altimetric estimate of the mean dynamic height of the sea surface in the Gulf by differencing the geoid with the altimeter‐derived mean sea surface, the model dynamic mean sea surface can be subtracted from the mean surface of Marsh et al. (1984) to obtain a new estimate of the Gulf geoid, which may be the best geoid available in that region for several years. A benchmark experiment (“truth”) was integrated to statistical equilibrium and compared with results from four experiments in which the model was initialized with fields modified from archived benchmark data. The experiments differed only in the initialization fields. Each experiment was integrated for 100 days with inflow transport remaining constant throughout the integration. The idealized experiments were initialized geostrophically (1) with the exact sea surface and pycnocline height fields, (2) with only the exact sea surface heights and a pycnocline assumed to compensate it such that the deep pressure perturbations vanished, (3) just as in case 2 but with a geoid error component added on small spatial scales, and (4) just as in case 3 except the geoid error model included an additional contribution in strong geoid gradient regions. The sequence of four numerical experiments showed that (1) geostrophic initialization with exact sea surface and pycnocline height information provided accurate forecasts to 100 days, (2) even when only the sea surface height information was provided to the numerical model for geostrophic initialization and deep pressure perturbations assumed to vanish at the initial time, the forecasts were superior to persistence or climatology over the 100‐day forecast period, and (3) errors in the geoid on spatial scales comparable to the grid resolution of the model did not seriously degrade the forecast, even in dynamically active regions with large gradients in the geoid height where instability processes were observed to occur.

The development of accurate codes for the design of bridges and the evaluation of existing structures requires adequate assessment of site-specific heavy traffic loading and also the dynamic interaction that may occur as this traffic traverses the structure. Shortcomings in current design codes occur due to the relatively independent manner in which critical static loading values and the corresponding allowance for dynamic amplification factor (DAF) are obtained. It is important that an approach is adopted that allows for the reduced probability of both high static loading and high dynamic amplification occurring simultaneously. Consideration of only relevant critical loading events will allow for efficient and accurate determination of independent values for characteristic (lifetime-maximum) static and total load effects. This paper proposes a method whereby initially the critical static loading scenarios for a chosen bridge are determined, from Monte Carlo simulation using weigh-in-motion (WIM) data from a typical European route. The development of a database of 3-dimensional finite element bridge and truck models allows for the analysis of these various different combinations of vehicular loading patterns. Thus the bridge specific critical loading scenarios are modelled and analysed individually to obtain the critical total (dynamic + static) load effect. It will then be possible to obtain a correlation between critical static load effect and corresponding total load effect/DAF and to extrapolate a characteristic DAF. This approach can lead to significant savings in structural design/assessment where site-specific maximum design load effects are determined from measured traffic data and experimental bridge-truck dynamic interaction.

The extraction of physical information about the subsurface ocean from surface information obtained from satellite measurements is both important and challenging. We introduce a back-propagation neural network (BPNN) method to determine the subsurface temperature of the North Pacific Ocean by selecting the optimum input combination of sea surface parameters obtained from satellite measurements. In addition to sea surface height (SSH), sea surface temperature (SST), sea surface salinity (SSS) and sea surface wind (SSW), we also included the sea surface velocity (SSV) as a new component in our study. This allowed us to partially resolve the non-linear subsurface dynamics associated with advection, which improved the estimated results, especially in regions with strong currents. The accuracy of the estimated results was verified with reprocessed observational datasets. Our results show that the BPNN model can accurately estimate the subsurface (upper 1000 m) temperature of the North Pacific Ocean. The corresponding mean square errors were 0.868 and 0.802 using four (SSH, SST, SSS and SSW) and five (SSH, SST, SSS, SSW and SSV) input parameters and the average coefficients of determination were 0.952 and 0.967, respectively. The input of the SSV in addition to the SSH, SST, SSS and SSW therefore has a positive impact on the BPNN model and helps to improve the accuracy of the estimation. This study provides important technical support for retrieving thermal information about the ocean interior from surface satellite remote sensing observations, which will help to expand the scope of satellite measurements of the ocean.

MURPHY Sabah Oil Co. Ltd. has developed the Kikeh Field located offshore Malaysia in the South China Sea in a water depth of 1325m. This field development is based on a Floating Production Storage and Offloading unit (FPSO) and a Spar Dry Tree Unit (DTU). Fluids are transported in fluid transfer lines (FTL) using SBM’s newly developed and patented Gravity Actuated Pipe (GAP) system. The GAP is an interesting combination of mooring (tether chains), dynamic steel riser (carrier pipe and flowlines) and steel structures (towheads). Design codes and standards usually address the design of these components separately. One of the challenges of the GAP project is to have a consistent design philosophy for all the components so that the GAP can be treated as an integrated system with homogeneous quality and safety levels. GAP component fatigue analysis is a good example of integrated system design. In the GAP, fatigue loading is applied by the floaters, through the tether chains, to the towheads into the carrier pipe. The fatigue analysis of individual GAP components cannot be performed in isolation — it must be the result of an integrated GAP fatigue analysis. A global model of the GAP is built with towheads modelled as rigid bodies and tether chains and carrier pipe modelled as dynamic lines. This model is used to obtain time series of loads on all components of the GAP. The fatigue of each component is calculated using the same methodology based on stress Response Amplitude Operators (RAOs) for a selected number of combinations of FPSO headings, wave directions, FPSO drafts and fluid densities. This methodology is classical for chains and steel pipes. It is less classical to apply such a detailed methodology for large structures like towheads. The towhead structures are key components that provide connection between tether chains and carrier pipe, flexible jumpers and steel flow lines, carrier pipe and decoupling overhead buoyancy tank. As such, the fatigue analysis of the towhead is as critical as for the tether chains and the carrier pipe. Finite element models of the towheads have been subjected to unit loads from all components attached to them and from the dynamic fluid pressures generated by unit towhead accelerations. Using the loads extracted from hydrodynamics calculations on the global GAP model and the matrix of stress for unit loads, the time domain approach is kept throughout the complete structural assessment of the towheads. This is in order to maintain a high degree of accuracy in the stress prediction. Given the criticality of the carrier pipe, a very detailed Engineering Criticality Assessment (ECA) is performed to define flaw acceptance criteria to be used during the Non Destructive Examination (NDE) campaign.

Sea surface height (SSH) is routinely measured from satellites and used to infer ocean currents, including eddies, that affect the distribution of organisms and substances in the ocean. SSH not only reflects the dynamics of the surface layer, but also is sensitive to the fluctuations of the main pycnocline; thus it is linked to events of nutrient upwelling. Beyond episodic upwelling events, it is not clear if and how SSH is linked to broader changes in the biogeochemical state of marine ecosystems. Our analysis of 23 years of satellite observations and biogeochemical measurements from the North Pacific Subtropical Gyre shows that SSH is associated with numerous biogeochemical changes in distinct layers of the water column. From the sea surface to the depth of the chlorophyll maximum, dissolved phosphorus and nitrogen enigmatically increase with SSH, enhancing the abundance of heterotrophic picoplankton. At the deep chlorophyll maximum, increases in SSH are associated with decreases in vertical gradients of inorganic nutrients, decreases in the abundance of eukaryotic phytoplankton, and increases in the abundance of prokaryotic phytoplankton. In waters below ∼100 m depth, increases in SSH are associated with increases in organic matter and decreases in inorganic nutrients, consistent with predicted consequences of the vertical displacement of isopycnal layers. Our analysis highlights how satellite measurements of SSH can be used to infer the ecological and biogeochemical state of open-ocean ecosystems.

Reconstructing the vertical structures of the ocean from sea surface information is of great importance for ocean and climate studies. In this study, an ensemble machine learning (Ens-ML) model is proposed to retrieve ocean subsurface thermal structure (OSTS) by using satellite-derived sea surface data and Argo data in the South China Sea (SCS). The input data include sea surface height (SSH), sea surface temperature (SST), sea surface salinity (SSS), sea surface wind (SSW), and geographic information (including longitude and latitude). We select three stable machine learning models, namely, extreme gradient boosting (XGBoost), RandomForest and light gradient boosting machine (LightGBM) as our benchmark models, and then use an artificial neural network (ANN) technique to combine outputs from the three individual models. The proposed Ens-ML model using sea surface data only by SSH, SST, SSS, and SSW performs less satisfactorily than that considering the contribution of geographical information, indicating that the geographical information is essential to estimate the OSTS accurately. The estimated OSTS from the Ens-ML model are compared with Argo data. The results show that the proposed Ens-ML model can accurately estimate the OSTS (upper 1000 m) in the SCS, which is relatively more accurate and precise than the individual models. The performance of the Ens-ML model also varies with season, and better estimation is obtained in winter, which is probably due to stronger mixing and weaker stratification. This study shows the great potential and advantage of the multi-model ensemble of machine learning algorithm for the ocean’s interior information retrieving, showing great potential in expanding the scope of ocean observations.

Over the last decades, space oceanography missions, particularly altimeter missions, have greatly advanced our ability to observe sea surface dynamics. However, they still struggle to resolve spatial scales below ~ 100 km. On a global scale, sea surface current are derived from sea surface height by a geostrophical assumption. While future altimeter missions should improve the observation of sea surface height, the observation of sea surface current using altimetry techniques would remains indirect. In the other hands, recent works have considered the use of AIS (automated identification system) as a new mean to reconstruct sea surface current : AIS data streams provide an indirect observational models of total currents including ageostrophic phenomenas. In this work we consider the use of the supervised learning framework 4DVARNet, a supervised data driven approach that allow us to perform multi-modal experiments : We focus on an Observing System Simulation Experiment (OSSE) in a region of the Gulf-Stream and we show that the joint use of AIS and sea surface height (SSH) measurement could improve the reconstruction of sea surface current with respect to product derived solely from AIS or SSH observations in terms of physical and time scale resolved. 

In recent decades, satellites have played a pivotal role in observing ocean dynamics, providing diverse datasets with varying spatial resolutions. Notably, within these datasets, sea surface height (SSH) data typically exhibit low resolution, while sea surface temperature (SST) data have significantly higher resolution. This study introduces a Transfer Learning-enhanced Generative Adversarial Network (TLGAN) for reconstructing high-resolution SSH fields through the fusion of heterogeneous SST data. In contrast to alternative deep learning approaches that involve directly stacking SSH and SST data as input channels in neural networks, our methodology utilizes bifurcated blocks comprising Residual Dense Module and Residual Feature Distillation Module to extract features from SSH and SST data, respectively. A pixelshuffle module-based upscaling block is then concatenated to map these features into a common latent space. Employing a hybrid strategy involving adversarial training and transfer learning, we overcome the limitation that SST and SSH data should share the same time dimension and achieve significant resolution enhancement in SSH reconstruction. Experimental results demonstrate that, when compared to interpolation method, TLGAN effectively reduces reconstruction errors and fusing SST data could significantly enhance in generating more realistic and physically plausible results.

Satellite sensors increasingly provide high-resolution (HR) observations of the ocean. They supply observations of sea surface height (SSH) and of tracers of the dynamics such as sea surface salinity (SSS) and sea surface temperature (SST). In particular, the Surface Water Ocean Topography (SWOT) mission will provide measurements of the surface ocean topography at very high-resolution (HR) delivering unprecedented information on the meso-scale and submeso-scale dynamics. This study investigates the feasibility to use these measurements to reconstruct meso-scale features simulated by numerical models, in particular on the vertical dimension. A methodology to reconstruct three-dimensional (3D) multivariate meso-scale scenes is developed by using a HR numerical model of the Solomon Sea region. An inverse problem is defined in the framework of a twin experiment where synthetic observations are used. A true state is chosen among the 3D multivariate states which is considered as a reference state. In order to correct a first guess of this true state, a two-step analysis is carried out. A probability distribution of the first guess is defined and updated at each step of the analysis: (i) the first step applies the analysis scheme of a reduced-order Kalman filter to update the first guess probability distribution using SSH observation; (ii) the second step minimizes a cost function using observations of HR image structure and a new probability distribution is estimated. The analysis is extended to the vertical dimension using 3D multivariate empirical orthogonal functions (EOFs) and the probabilistic approach allows the update of the probability distribution through the two-step analysis. Experiments show that the proposed technique succeeds in correcting a multivariate state using meso-scale and submeso-scale information contained in HR SSH and image structure observations. It also demonstrates how the surface information can be used to reconstruct the ocean state below the surface.

Estimating the ocean subsurface thermal structure (OSTS) based on multisource sea surface data in the western Pacific Ocean is of great significance for studying ocean dynamics and El Niño phenomenon, but it is challenging to accurately estimate the OSTS from sea surface parameters in the area. This paper proposed an improved neural network model to estimate the OSTS from 0–2000 m from multisource sea surface data including sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), and sea surface wind (SSW). In the model experiment, the rasterized monthly average data from 2005–2015 and 2016 were selected as the training and testing set, respectively. The results showed that the sea surface parameters selected in the paper had a positive effect on the estimation process, and the average RMSE value of the ocean subsurface temperature (OST) estimated by the proposed model was 0.55 °C. Moreover, there were pronounced seasonal variation signals in the upper layers (the upper 200 m), however, this signal gradually diminished with increasing depth. Compared with known estimation models such as the random forest (RF), the multiple linear regression (MLR), and the extreme gradient boosting (XGBoost), the proposed model outperformed these models under the data conditions of the paper. This research can provide an advanced artificial intelligence technique for estimating subsurface thermohaline structure in major sea areas.

The Pacific saury (Cololabis saira) is one of the most commercially important pelagic fishes in Asia–Pacific countries. The oceanographic environment, especially the Oyashio Current, significantly affects the distribution of Pacific saury, and may lead to variations in their migration route and the formation of fishing grounds in Japanese coastal region and the high seas. In this study, six oceanographic factors, sea surface temperature (SST), sea surface chlorophyll-a concentration (SSC), sea surface salinity (SSS), sea surface height (SSH), mixed layer depth (MLD), and eddy kinetic energy (EKE), were associated with the monthly catch per unit effort 1 (monthly CPUE1, ton/vessel) and the monthly CPUE2 (ton/day) of Pacific saury from Chinese fishing vessels during the optimal fishing periods (September–November) in 2014–2017. The gradient forest analysis showed that the performance of monthly CPUE1 was higher than monthly CPUE2 and SST was the most important oceanographic factor influencing monthly CPUE1, followed by EKE. The generalized additive model indicated that SST, SSH, and EKE negatively affected monthly CPUE1, whereas SSC, SSS, and MLD induced dome-shaped increases in monthly CPUE1. The distributions of fishing locations are likely to form along Offshore Oyashio current and meanders, especially in October and November. Synchronous trends in the relationship between the intrusion area of the Oyashio and relative abundance variation index suggest that an increase in the intrusion area of the Oyashio causes more Pacific saury to migrate to the Japanese coastal region, and vice versa. These findings extend our understanding of the effects of the oceanographic environment on Pacific saury.

A consistency between seasonal fluctuation of actual sea surface height (SSH) and those caused by mass and density variations in gyre-scale regions is examined. The SSH obtained from satellite altimetry (altimetric SSH) is adopted as the actual SSH. SSH caused by mass variation (mass-related SSH) is simulated using a barotropic global ocean model forced by water flux, wind stress and surface pressure. SSH caused by density variation (steric SSH) is calculated from water density profile, i.e. temperature and salinity profiles. The model SSH well represents mass-related SSH for gyre-scale regional means, and seasonal fluctuation of the altimetric SSH corrected for the model SSH is similar to that of steric SSH above a pressure level larger than 300 dbar. The results indicate that the mass-related SSH does not much respond to the baroclinic adjustment to the seasonally varying wind stress curl. The mass-related SSH forced by wind stress and surface pressure should be accounted for regional evaluation, though it is not necessary for global mean evaluation. Detection of steric SSH from altimetric SSH would be useful for assimilation approaches in which the altimetric SSH is treated as the variable reflecting subsurface temperature and salinity.

The Murchison structure is barge-launched steel drilling and production platform which is to be floated out in Spring 1979 and installed in the Northern North Sea in 156m of water. In the past, offshore steel structures have been designed on the basis of an extreme static design load. Subsequently, the completed design has been checked for dynamic amplification and fatigue life. As structures are designed for deeper waters, from 150m depth their natural periods become longer and dynamic excitation can have a profound effect on the stress levels achieved in all wave- conditions. In order to meet the design requirements for dynamic and fatigue analysis a new suite of computer programs called FATJACK has been developed which embodies several innovations in the analysis of steel structures. The whole system is centered on the Atkins Stress Analysis System (ASAS). It uses the subspace iteration technique to handle very large eigen-vector extractions which are necessary if the structure is to be modeled with sufficient accuracy to find the dynamic stress levels in the smaller diameter members. This is followed by a deterministic analysis using a steady-state response to relate the climatic conditions to the dynamic response and hence fatigue life duration. The application of this program to the Murchison structure is described in detail. For example, the extraction of 25 eigen-vectors from an uncondensed structural idealisation shows significant low frequencies in the conductor tube bracing which have previously been undetected and this could explain the relatively high failure rates of this type of structural component found in the platforms of the Southern North Sea. In addition, a transient analysis using a Newmark-ß integration scheme has been undertaken to establish the dynamic amplification factors by comparison with the base reactions and deck level deflections for corresponding static load cases. INTRODUCTION The escalating demand for hydrocarbon fuels has necessitated the exploration and development of offshore fields in ever increasing water depths. The enormous size of the steel structures required for these depths has lead to a new breed of problem and, in particular, the fatigue and dynamic response of such structures has become increasingly important. Experience in the North Sea has shown that some structures are more prone to fatigue damage from small, low period waves rather than overload failures due to the long period storm waves. In previous static analyses of offshore platforms the environmental conditions specified for extreme loading and fatigue life calculations have been related but different. In the case of the extreme '100 year' storm condition a theoretical design-life wave, with a specified height and period, is applied to the structure at a point in time usually chosen by reference to the maximum mud-line horizontal shear force or overturning moment. It is arbitrarily assumed that this criterion will define the worst load case for all members and joints. In addition the design-life current is applied to the structure simultaneously to give a combined maximum load condition.

SARAL/AltiKA (SRL) is the first altimetry satellite with a Ka-band altimeter. To validate the advantages of the Ka-band altimeter over traditional Ku-band altimeters in marine geodetic applications, a comprehensive analysis is carried out over the South China Sea (SCS) (0–30° N, 105–125° E) from three aspects, namely the influence of load on waveforms, the precision of sea surface heights (SSHs), and the precision of altimeter-derived marine gravity field. Coastal waveforms of SRL, Jason-2, and CryoSat-2 are separately compared with corresponding ocean-type waveforms. The radius of coastal influence on SSHs of SRL/exact repeat mission (SRL/ERM) is the smallest, being about 3 km. Crossover discrepancies, global mean sea surface models, and tide gauge data are used to assess the precision of altimetric SSHs. Compared with the SSH precision of Ku-band Jason-2/ERM, the SSH precision of Ka-band SRL/ERM is 4.6% higher over the SCS and 10% higher in offshore areas. Gridded gravity anomalies are derived from measurements of SRL/drifting phase (SRL/DP) and CryoSat-2 through the inverse Vening-Meinesz formula, respectively. According to the assessment by shipborne gravity data and global marine gravity models, the precision of SRL/DP-derived gravity is higher than that of CryoSat-2-derived gravity over the SCS, especially in offshore areas. In some cycles, ground tracks of SRL/ERM have large drifting of more than 10 km from nominal tracks. The results show that the Ka-band altimeter of SRL has better precision in SSHs and marine gravity recovery than the Ku-band altimeter over the SCS, particularly in offshore areas.

Recent approaches in statistical modeling on fisheries research have focused on addressing the relationship between fish catch per unit effort (CPUE) data and remotely sensed oceanographic data. This study used multiple statistical modeling approaches to ensure a satisfied result in determining the most important parameter that influences the CPUE distribution of Rastrelliger kanagurta. Oceanographic data including water depth (WD), chlorophyll-a (CHL), sea surface temperature (SST), sea surface height (SSH) and surface wind (SW) are the environmental parameters which were extracted from 570 locations within EEZ of Malaysia by using GIS techniques. The combination of CPUE and oceanographic data was analyzed and then fitted simultaneously in each performance of Generalized Additive Model (GAM), Generalized Linear Model (GLM), Boosted Regression Tree (BRT) and Multivariate Adaptive Regression Splines (MARS). Each statistical modeling approach resulted difference ways to describe the relationship between fish and its environment. Results showed the non-linear behavior fish CPUE having the strongest overall relationship with SSH as SSH was always at the top. This indicated that SSH was the most important parameter affecting CPUE based on the highest percentage of relative influence and variable importance with great significant relationship (p<0.001) compared to other parameters. This study showed the capability of GIS and statistical modeling to understand the R. kanagurta distribution and its environment parameters in South China Sea.