Sea State Conditions Research Articles

Assessment of Running Safety of Developed Train–Bridge–Wave Model Under One-Direction and Two Bidirectional High-Speed Train Passing Subjected to Rail Irregularities

A study about the running safety of trains moving over bridges subjected to hydrodynamic loads under one-direction and two bidirectional high-speed train passing is presented. Dynamic performance of the developed 3D train–bridge–wave system (TBW model) subjected to different hydrodynamic loads and applying Association of American Railroads track irregularities is established in this study. The dynamic responses of the bridge, which is under one-direction and two bidirectional high-speed train passing and subjected to several hydrodynamic loads and rail irregularities, are analyzed. The dynamic responses of the bridge and the running safety indices of the trains on the bridge under several types of sea states in one-direction and two bidirectional train passing are analyzed. Results of developed TBW’s sensitivity analyses have shown the importance of sea-state conditions for safe train running. Irregularities have an obvious effect on the dynamic responses of the bridge. Between comparing one-direction and two bidirectional high-speed train passing modes, bidirectional train passing has a greater effect on vertical acceleration and displacement than horizontal ones in the presence of a hydrodynamic load. Therefore, the combination of wave forces, rail irregularities, and bidirectional train passing jeopardizes the running safety of high-speed trains by increasing both lateral and vertical displacements and accelerations. Furthermore, the effect of wave force on the running safety indices of the train is investigated by considering different train speeds. As expected, the tendency observed in all the derailment criteria is similar, indicating that the risk of derailment increases with the increasing of the running speed and wave height.

Error Characterization of Significant Wave Heights in Multidecadal Satellite Altimeter Product, Model Hindcast, and In Situ Measurements Using the Triple Collocation Technique

Abstract Ocean wave measurements are of major importance for a number of applications including climate studies, ship routing, marine engineering, safety at sea, and coastal risk management. Depending on the scales and regions of interest, a variety of data sources may be considered (e.g., in situ data, Voluntary Observing Ship observations, altimeter records, numerical wave models), each one with its own characteristics in terms of sampling frequency, spatial coverage, accuracy, and cost. To combine multiple source of wave information (e.g., for data assimilation scheme in numerical weather prediction models), the error characteristics of each measurement system need to be defined. In this study, we use the triple collocation technique to estimate the random error variance of significant wave heights from a comprehensive collection of collocated in situ, altimeter, and model data. The in situ dataset is a selection of 122 platforms provided by the Copernicus Marine Service In Situ Thematic Center. The altimeter dataset is the ESA Sea State CCI version1 L2P product. The model dataset is the WW3-LOPS hindcast forced with bias-corrected ERA5 winds and an adjusted T475 parameterization of wave generation and dissipation. Compared to previous similar analyses, the extensive (∼250 000 entries) triple collocation dataset generated for this study provides some new insights on the error variability associated to differences in in situ platforms, satellite missions, sea state conditions, and seasonal variability.

A Generalized Two–Scale Approximation for Ocean Wave Models

The two-scale approximation (hereafter, TSA) was previously presented as a new method to approximate and estimate transfer rates in wind – wave spectra. It was shown to perform well for a variety of idealized and observed sea state conditions and to compare well with respect to the total Boltzmann integral for nonlinear quadruplet wave-wave interactions. Here, we present a generalized formulation of TSA, allowing for multiple peaked spectra, sheared spectra, sea – swell combinations, etc. This formulation is implemented in a modern operational wave model, WAVEWATCHIII™, and shown to provide a significant improvement over the standard approximation used in wave models, the discrete interaction approximation (DIA). Additional tests involve the simulation of waves generated in Hurricane Teddy (2020).

Fatigue analysis and reliability assessment of tether system of an offshore oil and gas platform operating in extreme sea-state conditions

Fatigue analysis and reliability assessment of tether system of an offshore oil and gas platform operating in extreme sea-state conditions

Proposal of Optimal Operation in Ship Rolling Motion Considering Sea State Conditions

Most of the research on ship rolling motion concerns a specific type of ship, such as containerships, or analyzes sailing during certain wave characteristics, rather than the full spectrum of waves that can be encountered during the entire sea route. To date, the most frequent merchant ships in the world are general cargo ships, where the ship’s behaviour at any sea condition has a great influence on overall safety, as well as the lashing design of non-standardized cargo. For this reason, the present paper aimed to study ship-performance models, starting with concepts of the basic physics of ships and waves. Firstly, the ship’s behaviour was analyzed from a theoretical point of view, both in calm waters and when sailing in waves, but independently. Afterwards, the ship-waves system was analyzed during rolling with all variables accounted for at the same moment, with the objective of obtaining roughly realistic models. Relevant results are shown in each of the models, which may be of great interest for ship operators and, in general, for the shipping industry, so as to improve the safety of maritime transport. Finally, these results were validated with a case study.

Shipborne GNSS-Determined Sea Surface Heights Using Geoid Model and Realistic Dynamic Topography

With an increasing demand for accurate and reliable estimates of sea surface heights (SSH) from coastal and marine applications, approaches based on GNSS positioning have become favored, to bridge the gap between tide gauge (TG) and altimetry measurements in the coastal zone, and to complement offshore altimetry data. This study developed a complete methodology for jointly deriving and validating shipborne GNSS-determined SSH, using a geoid model and realistic dynamic topography estimates. An approach that combines the properties of hydrodynamic models and TG data was developed to obtain the latter. Tide gauge data allow estimating the spatiotemporal bias of a hydrodynamic model and, thus, linking it to the used vertical datums (e.g., a novel geoid-based Baltic Sea Chart Datum 2000). However, TG data may be erroneous and represent different conditions than offshore locations. The qualities of spatiotemporal bias are, hence, used to constrain TG data errors. Furthermore, a rigid system of four GNSS antennas was used to ensure SSH accuracy. Besides eliminating the vessel’s attitude effect on measurement data, the rigid system also provides a means for internal validation, suggesting a 4.1 cm height determination accuracy in terms of standard deviation. The methodology also involves eliminating the effect of sea state conditions via a low-pass filter and empirical estimation of vessel sailing-related corrections, such as the squat effect. The different data validation (e.g., examination of residual values and intersection analyses) results, ranging from 1.8 cm to 5.5 cm in terms of standard deviation, indicate an SSH determination accuracy of around 5 cm.

Parameter Estimation for Sea Clutter Pareto Distribution Model Based on Variable Interval

The generalized Pareto (GP) distribution model is often used to describe the amplitude statistical feature of sea clutter. Generally, the parameters of GP distribution are estimated by moments estimators. However, when the sea state is high, the appearance of sea spikes will increase the echo of the anomalous scattering units, which leads to a decrease in the parameter estimation accuracy and target detection performance. To improve the parameter estimation accuracy, this paper proposes a novel parameter estimation method based on variable intervals. Considering the local properties of sea clutter, we take a variable interval of the entire sea clutter series for parameter estimation, where the interval position is selected according to the sea state conditions. For contrast, the bipercentile parameter estimation and truncate moment estimation are also introduced. Finally, the experiment based on the real measured X-band sea clutter datasets indicates that the proposed method outperforms the state-of-the-art moments estimators.

Maximizing tidal energy conversion by adopting hydraulic transformation and LMI based robust control

This paper explores the maximization of tidal energy conversion by adopting hydraulic transformation and through the design of a robust H∞ control scheme. A hydraulic transformation based tidal turbine applicable to realistic tidal states is presented. In this turbine, a hydraulic transformer is developed to have well-adapted characteristics to turbulent tidal stream flows. The dynamics of the tidal turbine is represented as a state space model based on the equations of its main components. The H∞ control scheme is then synthesized by introducing a new state variable that represents the “filtered” of the turbine rotor speed tracking errors. The control problem is formulated as a convex optimization problem with linear matrix inequality (LMI) that is solved to satisfy H∞ performance and obtain good dynamic characteristics. In addition, a robust H∞ observer for estimating state variables along with the online implementations of the robust controller is also designed by using the LMI approach. A case study of a 150 kW hydraulic transformation based tidal turbine system is conducted and detailed analyses of the dynamic behavior of the tidal turbine are presented. According to the test results, approximately up to 40% total power conversion efficiency can be achieved by the proposed turbine under the typical energetic sea state conditions.

A novel Nonlinear Model Predictive Controller for Power Maximization on Floating Offshore Wind Turbines

Reducing the Levelized Cost of Energy is the main objective of wind turbine industry, in particular for the emerging sector of floating offshore turbines. In this work, a novel Economic Nonlinear Model Predictive Control (ENMPC) strategy is developed to maximize the power production of floating offshore wind turbines. The control problem is solved through an indirect method, which achieves the computational efficiency required to apply it in real world cases. A non-linear Reduced Order Model of the floating turbine predicts aerodynamic power, generator temperature and platform motions inside the controller. A set of constraints, including a bound on the generator temperature, the thrust and platform velocities are imposed. Simulations using the open-source engineering tool OpenFAST on the 5MW NREL wind turbine supported by the OC3 spar buoy platform [1] are performed to validate the turbine model and then to assess the controller performances in realistic wind and sea state conditions. With respect to the standard controller, a 4.3% increase of generated power in rated conditions is achieved with a more stable generator temperature.

Distribution of average extreme wave parameters in the North Atlantic from numerical simulations

This study is focused on the North Atlantic Ocean where the maritime activity is intense and accidents have occurred due to dangerous sea state conditions, including the occurrence of abnormal waves. The study is based on a 7-year period of numerical simulations performed with a 3rd generation wave model aiming to shed light on the spatial distribution of the extreme wave parameters, as indicators of a high probability of occurrence of abnormal waves. Monthly spatial distributions of the most important averaged wave parameters are discussed and analyzed. From the hindcast it is observed that the maxima of the significant wave height are related with the well-known North Atlantic storm track region, where extratropical cyclones occur more often, namely in the Newfoundland – Greenland – Iceland corridor. The highest Benjamin-Feir Index values are found in the western side of the North Atlantic in the Canadian and American continental shelfs. In addition, it is observed that the peak period often experiences a reduction in the 12 h period before the occurrence of a Benjamin-Feir Index peak. This relationship is important as it could very well constitute a warning indicator for the occurrence of extreme sea states.

Context-informed conditional anomaly detection approach for wave power plants: The case of air turbines

The reliability and energy production of wave power plants (WPPs) depend on sea-state conditions, operation efficiency and degradation of its constituent assets. Air turbines are key assets for the efficient and reliable operation of WPPs and ensuring their correct operation leads to enhance the efficiency of WPPs. However, the lack of run-to-failure data and scarce fault records hampers the development of predictive condition monitoring solutions. In this context, focusing on unsupervised health monitoring methods, this paper presents an air turbine conditional anomaly detection (CAD) approach with a practical case study tested and validated on the Mutriku wave power plant. In contrast to anomaly detection models, which model the health-state without taking into account the influence of the operating context, the proposed CAD approach learns the expected air turbine operation conditioned on specific sea-states information modelled through wave energy flux concepts. This is achieved through an ensemble of Gaussian Mixture models and the expectation–maximization algorithm. Results show that, the integration of sea-states in the anomaly detection learning process improves the discrimination capability of the CAD model compared with the anomaly detection model without sea-state information, reducing false positive events and improving the accuracy of the CAD model.

Availability analysis of a ship-to-ground FSO link

This paper analyzes the availability of a free-space optical (FSO) communication link, established between a maritime platform and a land base. In this analysis, the impact of the laser beam misalignment, caused by the vessel’s six oscillatory motions in a seaway, is considered. The hydrodynamic system is modeled, through a ship’s response amplitude operator (RAO), under a typical sea-state condition. Finally, the link availability is calculated based on FSO losses, considering a beam with Gaussian power distribution.

Quad-polarimetric SAR sea state retrieval algorithm from Chinese Gaofen-3 wave mode imagettes via deep learning

Synthetic aperture radar (SAR) is a powerful tool for monitoring sea states in terms of the significant wave height (SWH). Regarding the specific wave mode, to date, the previous empirical models for estimating SWH from SAR data rely on single polarization. In the emerging deep learning era, few published quad-polarized SAR SWH retrieval algorithms have been based on machine learning technique, and whether quad-polarimetry improves the skill of wave height estimation remains a question. Here we propose a deep residual convolutional neural network-based SAR SWH retrieval algorithm in quad-polarization. By collocating WaveWatch III sea state hindcasts and all available archives of quad-polarized Chinese Gaofen-3 SAR imagettes in wave mode, a database with approximately 30,000 matchups was employed to establish our deeply-learned network. The GaoFen-3 significant wave height retrievals were validated against the hindcast dataset independent of training along with altimeter observations. The result of good consistency in terms of a root mean square error of 0.32 m (under sea state conditions of approximately 0.5–7.0 m) outperforms the existing Gaofen-3 wave height retrieval algorithms. Additionally, this paper introduces a discussion about the contribution of polarizations by comparing SWH derived from single-, dual- and quad-polarized deep convolutional neural networks. Single-polarized Gaofen-3 SAR data are found to be sufficient to provide accurate estimates compared to quad-polarization via a deep learning model under moderate sea conditions. Exploitation of SAR quad-polarimetry information will improve SAR wave height retrievals under high sea conditions.

A Wideband Satellite Maritime Channel Model Simulator

In this article, we present implementation details of a generative satellite maritime propagation channel model at L-band. In an earlier paper, we presented the parameterization of the channel model for the narrowband case based on recordings of GPS L1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C/N_{0}$ </tex-math></inline-formula> time-series in different sea state conditions. Additionally, we performed wider band measurements using GPS E5a (≈10 Mcps—chips per second— > 20 MHz bandwidth) and obtained estimations of power delay profiles. Due to lack of dynamic range and delay resolution, in these measurements, no conclusive information was gathered on the time spreading caused by the channel. What was clear is that the time spreading was not significant outside the first signal chip, i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 0.1~\mu \text{s}$ </tex-math></inline-formula> . Still wideband modeling is required within this spreading range. Now, we want to extend the model to the wideband case. To achieve this goal, we have used a rough surface scattering model applicable to sea surfaces and employed it to characterize the time dispersion to be expected. Additionally, we also used this scattering model to help verify the validity of the existing Doppler spread model. Furthermore, ship movements in the waves play a role. We try and analyze this issue. Finally, we present a detailed discussion on various implementation issues we came across when coding the simulator.

A new approach to predict dynamic mooring tension using LSTM neural network based on responses of floating structure

The prediction of dynamic mooring line tension using the responses of floating structures is of great significance for the safety monitoring of the station-keeping. Due to the mooring line tension usually has the typical low-frequency and wave-frequency characteristics, this paper proposes a method of Low-frequency adds wave-frequency responses (LAWR) to predict the mooring line tension. Firstly, the optimal Long-short term memory (LSTM) neural network structure is determined through conducting the parameter sensitivity analysis on the performance of the LSTM model. Secondly, the low-frequency and wave-frequency tension are predicted using the corresponding low-frequency and wave-frequency responses as the input data, respectively. Then, the total mooring line tension is predicted by superposing the low-frequency tension and wave-frequency tension. Finally, the feasibility of the LAWR method is verified by considering the different data relevance between the training sets and validating sets, including the sea state condition contains different current direction, wave height and spectral peak frequency. The method proposed in this paper could further improve the prediction accuracy of mooring line tension more than 30%, which has great engineering significance.

On berm design of Icelandic-type breakwaters (case study: Sirevåg berm breakwater)

An experimental model of an Icelandic-berm breakwater has been set up in the hydraulic laboratory of Tarbiat Modares University using irregular waves. Experimental model tests have been carried out using a wide range of sea-state conditions and geometrical parameters to measure different parameters, such as the eroded area (Ae), the berm recession (Rec) and the depth of the intersection point of initial and reshaped profiles (hf). A stability formula is proposed to establish the relationship between the damage parameter of the Icelandic-berm breakwater (Sd) and the non-dimensional form of Rec and hf. The application of the given formula for estimating the berm recession in the phenomenon and practical cases was evaluated employing the Sirevåg berm breakwater. The results of the evaluation highlight the appropriate estimation of berm recession. Moreover, some recommendations are suggested to consider berm parameters, such as the berm width, the berm elevation from still water level and the height of class I stones, as an Icelandic-type berm breakwater design criterion for future engineers. Eventually, taking all the proposed formulae and recommendations into consideration, a design tool for engineers in berm design of berm breakwaters has been provided.

Swell Conditions at Potential Sites for the Colombian Antarctic Research Station

The objective of this paper was to characterize swell conditions in the coastal zone of the South Shetland Islands, where our preliminary analyses evaluated potential locations for the Colombian scientific station. The Simulating Waves Nearshore (SWAN) spectral model was implemented for the Bransfield Strait. The boundary conditions were selected by a cluster analysis of the wave climate from global hindcasting obtained with the WAVEWATCH III model. Some comparisons between the model and wavemeter measurements were made. The results demonstrated that optimal sea state conditions for the scientific base are present in the South Bay, Livingston Island.

MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data.

Currently, a significant amount of research is focused on detecting Marine Debris and assessing its spectral behaviour via remote sensing, ultimately aiming at new operational monitoring solutions. Here, we introduce a Marine Debris Archive (MARIDA), as a benchmark dataset for developing and evaluating Machine Learning (ML) algorithms capable of detecting Marine Debris. MARIDA is the first dataset based on the multispectral Sentinel-2 (S2) satellite data, which distinguishes Marine Debris from various marine features that co-exist, including Sargassum macroalgae, Ships, Natural Organic Material, Waves, Wakes, Foam, dissimilar water types (i.e., Clear, Turbid Water, Sediment-Laden Water, Shallow Water), and Clouds. We provide annotations (georeferenced polygons/ pixels) from verified plastic debris events in several geographical regions globally, during different seasons, years and sea state conditions. A detailed spectral and statistical analysis of the MARIDA dataset is presented along with well-established ML baselines for weakly supervised semantic segmentation and multi-label classification tasks. MARIDA is an open-access dataset which enables the research community to explore the spectral behaviour of certain floating materials, sea state features and water types, to develop and evaluate Marine Debris detection solutions based on artificial intelligence and deep learning architectures, as well as satellite pre-processing pipelines.

Characteristics of the Wind and Wave Climate along the European Seas Focusing on the Main Maritime Routes

European seas have a strong economic role both in terms of transport and tourism. Providing more knowledge, regarding the mean and extreme values of the wind and sea state conditions in the areas characterized by high maritime traffic, helps to improve navigational safety. From this perspective, six zones with high maritime traffic are studied. ERA5 database, a state-of-the-art global reanalysis dataset provided by ECMWF (European Centre for Medium-Range Weather Forecasts), is used to assess the average values and the percentiles for the wind speed and the main wave parameters in the target areas considering the period 2001–2020. The main European routes and the extreme conditions along them as well as the areas characterized by high values of wind speed and high waves were also identified. A more comprehensive picture of the expected dynamics of the environmental matrix along the most significant shipping routes is useful because in this way the most dangerous areas could be avoided by ships for the safety of passengers and transported goods.

Comparing the Currents Measured by CARTHE, CODE and SVP Drifters as a Function of Wind and Wave Conditions in the Southwestern Mediterranean Sea.

Instruments drifting at the ocean surface are quasi-Lagrangian, that is, they do not follow exactly the near-surface ocean currents. The currents measured by three commonly-used drifters (CARTHE, CODE and SVP) are compared in a wide range of sea state conditions (winds up to 17 m/s and significant wave height up to 3 m). Nearly collocated and simultaneous drifter measurements in the southwestern Mediterranean reveal that the CARTHE and CODE drifters measure the currents in the first meter below the surface in approximately the same way. When compared to SVP drogued at 15 m nominal depth, the CODE and CARTHE currents are essentially downwind (and down-wave), with a typical speed of 0.5–1% of the wind speed. However, there is a large scatter in velocity differences between CODE/CARTHE and SVP for all wind and sea state conditions encountered, principally due to vertical and horizontal shears not related to the wind. For the CODE drifter with wind speed larger than 10 m/s and significant wave height larger than 1 m, about 30–40% of this difference can be explained by Stokes drift.