Ocean Current Velocity Research Articles

Influence of Ocean Current Features on the Performance of Machine Learning and Dynamic Tracking Methods in Predicting Marine Drifter Trajectories

Accurately and rapidly predicting marine drifter trajectories under conditions of information scarcity is critical for addressing maritime emergencies and conducting marine surveys with resource-limited unmanned vessels. Machine learning-based tracking methods, such as Long Short-Term Memory networks (LSTM), offer a promising approach for trajectory prediction in such scenarios. This study combines satellite observations and idealized simulations to compare the predictive performance of LSTM with a resource-dependent dynamic tracking method (DT). The results indicate that when driven solely by historical drifter paths, LSTM achieves better trajectory predictions when trained and tested on relative trajectory intervals rather than the absolute positions of individual trajectory points. In general, LSTM provides a more accurate geometric pattern of trajectories at the initial stages of forecasting, while DT offers superior accuracy in predicting specific trajectory positions. The velocity and curvature of ocean currents jointly influence the prediction quality of both methods. In regions characterized by active sub-mesoscale dynamics, such as the fast-flowing and meandering Kuroshio Current and Kuroshio Current Extension, DT predicts more reliable trajectory patterns but lacks precision in detailed position estimates compared to LSTM. However, in areas dominated by the fast but relatively straight North Equatorial Current, the performance of the two methods reverses. The two methods also demonstrate different tolerances for noise and sampling intervals. This study establishes a baseline for selecting machine learning methods for marine drifter prediction and highlights the limitations of AI-based predictions under data-scarce and resource-constrained conditions.

Flow Field Analysis and Development of a Prediction Model Based on Deep Learning

The velocity of ocean currents significantly affects the trajectory prediction of ocean drifters and the safe navigation of intelligent vessels. Currently, most ocean current predictions focus on time-based forecasts at specific fixed points. In this study, deep learning based on the flow field prediction model (CNNs–MHA–BiLSTMs) is proposed, which predicts the changes in ocean currents by learning from historical flow fields. Unlike conventional models that focus on single-point current velocity data, the CNNs–MHA–BiLSTMs model focuses on the ocean surface current information within a specific area. The CNNs–MHA–BiLSTMs model integrates multiple convolutional neural networks (CNNs) in parallel, multi-head attention (MHA), and bidirectional long short-term memory networks (BiLSTMs). The model demonstrated exceptional modelling capabilities in handling spatiotemporal features. The proposed model was validated by comparing its predictions with those predicted by the MIKE21 flow model of the ocean area within proximity to Dalian Port (which used a commercial numerical model), as well as those predicted by other deep learning algorithms. The results showed that the model offers significant advantages and efficiency in simulating and predicting ocean surface currents. Moreover, the accuracy of regional flow field prediction improved with an increase in the number of sampling points used for training. The proposed CNNs–MHA–BiLSTMs model can provide theoretical support for maritime search and rescue, the control or path planning of Unmanned Surface Vehicles (USVs), as well as protecting offshore structures in the future.

Research on Digital Twin Method for Spaceborne Along-Track Interferometric Synthetic Aperture Radar Velocity Inversion of Ocean Surface Currents

In this paper, an end-to-end system framework is proposed for the Digital Twin study of spaceborne ATI-SAR ocean current velocity inversion. Within this framework, a fitting inversion approach is proposed to enhance the conventional spaceborne ATI-SAR ocean current velocity inversion algorithm. Consequently, the issue of possible local inversion errors stemming from the mismatch between the traditional spaceborne ATI-SAR inversion algorithm and various dual-antenna configurations is resolved to a certain extent. A simulated spaceborne ATI-SAR system, featuring a dual-antenna configuration comprising a baseline direction perpendicular to the track and a squint angle, is presented to validate the efficacy of the Digital Twin methodology. Under the specified simulation parameters, the average inversion error for the final ocean current velocity is recorded at 0.0084 m/s, showcasing a reduction of 0.0401 m/s compared with the average inversion error prior to optimization.

Design and Analysis of a Vertical Axis Ocean Current Turbine Tunnel Using SolidWorks Computational Fluid Dynamics

The development of renewable energy in the marine power generation sector presents a promising approach to producing electrical energy in a sustainable and environmentally friendly manner. Indonesia, with its vast oceanic territory, holds significant potential for harnessing marine energy. However, the relatively slow speed of ocean currents in the region, typically ranging from 0.1 m/s to 1.5 m/s, poses a challenge to the efficiency of marine power generation. To overcome this limitation, this research focuses on the design and analysis of a vertical-axis ocean current turbine tunnel aimed at increasing the speed of ocean currents, thereby enhancing the overall efficiency of energy production. The study combines a thorough literature review with experimental research methods, utilizing SolidWorks Computational Fluid Dynamics (CFD) software to simulate the tunnel's impact on ocean current velocity. The simulations reveal that the tunnel construction significantly boosts current speeds, increasing them from 1.0 m/s to 1.7 m/s, and from 1.5 m/s to 2.6 m/s. This increase in velocity directly translates to higher kinetic energy available for conversion into electrical power by the turbine. Moreover, the study shows that the tunnel construction contributes to a more uniform flow of ocean currents, as evidenced by the Reynolds numbers obtained—100.250 at a current speed of 1.0 m/s and 150.375 at 1.5 m/s. These values, being below 2000, indicate laminar flow conditions within the tunnel, which are beneficial for optimizing turbine performance by reducing turbulence and ensuring a stable energy output. The findings underscore the effectiveness of the tunnel design in improving the efficiency of vertical-axis ocean current turbines, making it a viable solution for enhancing renewable energy production in regions with low ocean current speeds.

Observed Diurnal Cycles of Near‐Surface Shear and Stratification in the Equatorial Atlantic and Their Wind Dependence

AbstractThe diurnal cycles of near‐surface velocity and temperature, also known as diurnal jet and diurnal warm layer (DWL), are ubiquitous in the tropical oceans, affecting the heat and momentum budget of the ocean surface layer, air‐sea interactions, and vertical mixing. Here, we analyze the presence and descent of near‐surface diurnal shear and stratification in the upper 20 m of the equatorial Atlantic as a function of wind speed using ocean current velocity and hydrographic data taken during two trans‐Atlantic cruises along the equator in October 2019 and May 2022, data from three types of surface drifters, and data from Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) moorings along the equator. The observations during two seasons with similar mean wind speeds but varying surface heat fluxes reveal similar diurnal jets with an amplitude of about 0.11 m s−1 and similar DWLs when averaging along the equator. We find that higher wind speeds lead to earlier diurnal peaks, deeper penetration depths, and faster descent rates of DWL and diurnal jet. While the diurnal amplitude of stratification is maximum for minimal wind speeds, the diurnal amplitude of shear is maximum at 6 m depth for moderate wind speeds of about 5 m s−1. The inferred wind dependence of the descent rates of DWL and diurnal jet is consistent with the earlier onset of deep‐cycle turbulence for higher wind speeds. The DWL and the diurnal jet not only trigger deep‐cycle turbulence but are also observed to modify the wind power input and thus the amount of energy available for mixing.

Mesoscale Eddies and Near-Inertial Internal Waves Modulate Seasonal Variations of Vertical Shear Variance in the Northern South China Sea

Abstract Diapycnal mixing in the South China Sea (SCS) is commonly attributed to the vertical shear variance S2 of horizontal ocean current velocity, but the seasonal modulation of S2 is still poorly understood due to the scarcity of long-term velocity observations. Here, this issue is explored in detail based on nearly 10-yr-long acoustic Doppler current profiler (ADCP) velocity data from a mooring in the northern SCS. We find that S2 in the northern SCS exhibits significant seasonal variations at both the near-surface (90–180 m) and subsurface (180–400 m) layers, but their seasonal cycles and modulation mechanisms are quite different. For the near-surface layer, S2 is stronger in late summer, autumn, and winter but weaker in spring and early summer, while in the subsurface layer, it is much stronger in winter than in other seasons. Further analysis suggests that in the near-surface layer, the stronger S2 in autumn and winter is primarily caused by typhoon-induced near-inertial internal waves (NIWs) and the large subinertial (SI) velocity shear of the baroclinic mesoscale eddies, respectively. With respect to the subsurface layer, the enhanced wintertime S2 is primarily associated with the “inertial chimney” effect of anticyclonic eddies, trapping wind-forced downward-propagating NIWs and significantly increasing the near-inertial shear at the critical layer. The findings in this study highlight the potentially important roles of mesoscale eddies and NIWs in modulating the seasonality of upper-ocean mixing in the northern SCS. This modulation is attributed not only to the strong shear of these features but also to their interactions. Significance Statement Vertical shear variance of velocity S2 significantly modulates turbulent mixing in the thermocline, but its climatologically seasonal variations and the associated mechanisms are still obscure due to the scarcity of long-term in situ velocity data. By analyzing nearly a decade of velocity data, we reveal significant seasonal variations in S2 at different ocean layers in the northern SCS and uncover different seasonal cycles and modulation mechanisms. The study sheds light on the pivotal roles of mesoscale eddies and near-inertial internal waves in modulating seasonality of S2 in the upper ocean. These findings have important implications for improving mixing parameterizations in numerical models.

Prediction of Drift Trajectory in the Ocean Using Double-Branch Adaptive Span Attention

The accurate prediction of drift trajectories holds paramount significance for disaster response and navigational safety. The future positions of underwater drifters in the ocean are closely related to their historical drift patterns. Additionally, leveraging the complex dependencies between drift trajectories and ocean currents can enhance the accuracy of predictions. Building upon this foundation, we propose a Transformer model based on double-branch adaptive span attention (DBASformer), aimed at capturing the multivariate time-series relationships within drift history data and predicting drift trajectories in future periods. DBASformer can predict drift trajectories more accurately. The proposed adaptive span attention mechanism exhibits enhanced flexibility in the computation of attention weights, and the double-branch attention structure can capture the cross-time and cross-dimension dependencies in the sequences. Finally, our method was evaluated using datasets containing buoy data with ocean current velocities and Autonomous Underwater Vehicle (AUV) data. The raw data underwent cleaning and alignment processes. Comparative results with five alternative methods demonstrate that DBASformer improves prediction accuracy.

Study on Fault Diagnosis Technology for Efficient Swarm Control Operation of Unmanned Surface Vehicles

The purpose of this study is to design a Swarm Control algorithm for the effective mission performance of multiple unmanned surface vehicles (USVs) used for marine research purposes at sea. For this purpose, external force information was utilized for the control of multiple USV swarms using a lead–follow-formation technique. At this time, to efficiently control multiple USVs, the LSTM algorithm was used to learn ocean currents. Then, the predicted ocean currents were used to control USVs, and a study was conducted on behavioral-based control to manage USV formation. In this study, a control system model for several USVs, each equipped with two rear thrusters and a front lateral thruster, was designed. The LSTM algorithm was trained using historical ocean current data to predict the velocity of subsequent ocean currents. These predictions were subsequently utilized as system disturbances to adjust the controller’s thrust. To measure ocean currents at sea as each USV moves, velocity, azimuth, and position data (latitude, longitude) from the GPS units mounted on the USVs were utilized to determine the speed and direction of the hull’s movement. Furthermore, the flow rate was measured using a flow rate sensor on a small USV. The movement and position of the USV were regulated using an Artificial Neural Network-PID (ANN-PID) controller. Subsequently, this study involved a comparative analysis between the results obtained from the designed USV model and those simulated, encompassing the behavioral control rules of the USV swarm and the path traced by the actual USV swarm at sea. The effectiveness of the USV mathematical model and behavior control rules were verified. Through a comparison of the movement paths of the swarm USV with and without the disturbance learning algorithm and the ANN-PID control algorithm applied to the designed simulator, we analyzed the position error and maintenance performance of the swarm formation. Subsequently, we compared the application results.

Observation of exceptionally strong near-bottom flows over the Atlantis II Seamounts in the northwest Atlantic

Knowledge of near-bottom ocean current velocities and especially their extreme values is necessary to understand geomorphology of the seafloor and composition of benthic biological communities and quantify mechanical energy dissipation by bottom drag. Direct measurements of near-bottom currents in deep ocean remain scarce because of logistical challenges. Here, we report the results of flow velocity and pressure fluctuation measurements at three sites with depths of 2573–4443 m in the area where the Gulf Stream interacts with the New England Seamounts. Repeated episodes of unexpectedly strong near-bottom currents were observed, with the current speed at 4443 m of more than 0.40 m/s. At 2573 m, current speeds exceeded 0.20 m/s approximately 5% of the time throughout the entire eight-week measurement period. The maximum flow speeds of over 1.10 m/s recorded at this site significantly surpass the fastest previously reported directly measured current speeds at comparable or larger depths. A strong correlation is found between the noise intensity in the infrasonic band and the measured current speed. The noise intensity and the characteristic frequency increase with the increasing current speed. Machine-learning tools are employed to infer current speeds from flow-noise measurements at the site not equipped with a current meter.

U*: GA-based path planning algorithm for surface floating garbage cleaning robot

Marine debris is a serious global problem that is not limited to areas where humans live but also drifts around the world with wind and currents. More than 10 million tons of plastic waste flow into the ocean every year, posing a major threat to humanity. This study designs a path planning algorithm for surface garbage-cleaning robots called U*, which aims to improve the efficiency of salvaging marine debris and reduce labor and time costs. The U* algorithm consists of two procedures: exploration and path-planning. The exploration procedure searches for marine debris, while the path-planning procedure predicts the possible location of marine debris using the velocity and direction of ocean currents and finds the shortest path by using a genetic algorithm (GA) to collect the found marine debris. According to the experimental results, the U* method is more efficient in terms of reducing path length and time costs.

The underwater soundscape of the North Sea

As awareness on the impact of anthropogenic underwater noise on marine life grows, underwater noise measurement programs are needed to determine the current status of marine areas and monitor long-term trends. The Joint Monitoring Programme for Ambient Noise in the North Sea (JOMOPANS) collaborative project was funded by the EU Interreg to collect a unique dataset of underwater noise levels at 19 sites across the North Sea, spanning many different countries and covering the period from 2019 to 2020. The ambient noise from this dataset has been characterised and compared - setting a benchmark for future measurements in the North Sea area. By identifying clusters with similar sound characteristics in three broadband frequency bands (25–160 Hz, 0.2–1.6 kHz, and 2–10 kHz), geographical areas that are similarly affected by sound have been identified. The measured underwater sound levels show a persistent and spatially uniform correlation with wind speed at high frequencies (above 1 kHz) and a correlation with the distance from ships at mid and high frequencies (between 40 Hz and 4 kHz). Correlation with ocean current velocity at low frequencies (up to 200 Hz), which are susceptible to nonacoustic contamination by flow noise, was also evaluated. These correlations were evaluated and simplified linear scaling laws for wind and current speeds were derived. The presented dataset provides a baseline for underwater noise measurements in the North Sea and shows that spatial variability of the dominant sound sources must be considered to predict the impact of noise reduction measures.

Research on ocean-current-prediction-based virtual mooring strategy for the portable underwater profilers

The Argo Global Ocean Observing Network has remained the preeminent ocean observation network worldwide. Since the Argo buoys lack freedom of lateral movement, their wave-following working characteristics prevent them from completing the long-term observations in the designated sea area. Long-term monitoring of designated waters is often required, for example, to study the impacts of seabed mining on the marine environment in the surrounding sea area. To solve this problem, a portable underwater profiler (PUP) that combines the functionalities of profiling buoys and underwater gliders was devised. The PUP employs a center of gravity adjustment mechanism utilizing the double-weight block approach, enabling the pitch angle adjustment within -90° to 0°. Remarkably, the PUP weighs only 11.25 kg, meeting the requirements for rapid deployment. Furthermore, the prediction method of the ocean current velocity was optimized by employing the full-depth-averaged ocean current velocity instead of the depth-averaged ocean current velocity. To ensure continuous observation operations of the PUP within designated sea areas, a horizontal displacement correction strategy (HDCS) based on the virtual mooring concept is proposed. The effectiveness of the HDCS has been preliminarily validated through simulations and further confirmed through lake trials conducted in Qiandao Lake.

A new path planning method for AUV based on the Navier–Stokes equations for ocean currents

The autonomous underwater vehicle (AUV), due to its flexibility, safety, and other characteristics, has been widely used in various marine applications such as underwater mapping, marine ecological monitoring and marine scientific. When navigating in complex and ever-changing ocean currents, one of the key challenges in AUV control is how to effectively avoid unknown obstacles such as sunken ships and reefs, and plan the route to reach the target point. In this paper, based on partially measured ocean current velocities, the Navier–Stokes equations (N–S equations) are used to characterize the ocean current velocities within a small-scale region. Furthermore, the improved artificial potential field method is used for path planning after integrating the velocity information. Simulation and experiments indicate that the proposed algorithm is better suited to complex marine environments.

Virtual Metrology Filter-Based Algorithms for Estimating Constant Ocean Current Velocity

The strap-down inertial navigation system (SINS) and Doppler velocity log (DVL) integrated navigation system are widely used for autonomous underwater vehicles (AUVs). Whereas DVL works in the water tracking mode, the velocity provided by DVL is relative to the current layer and cannot be directly used to suppress the divergence of SINS errors. Therefore, the estimation and compensation of the ocean current velocity play an essential role in improving navigation positioning accuracy. In recent works, ocean currents are considered constant over a short term in small areas. In the common KF algorithm with the ocean current as a state vector, the current velocity cannot be estimated because the current velocity and the SINS velocity error are coupled. In this paper, two virtual metrology filter (VMF) methods are proposed for estimating the velocity of ocean currents based on the properties that the currents remain unchanged at the adjacent moments. New measurement equations are constructed to decouple the current velocity and the SINS velocity error, respectively. Simulations and lake tests show that both proposed methods are effective in estimating the current velocity, and each has its advantages in estimating the ocean current velocity or the misalignment angle.

Patterns of wintertime Arctic sea-ice leads and their relation to winds and ocean currents

Abstract. We use a novel sea-ice lead climatology for the winters of 2002/03 to 2020/21 based on satellite observations with 1 km2 spatial resolution to identify predominant patterns in Arctic wintertime sea-ice leads. The causes for the observed spatial and temporal variabilities are investigated using ocean surface current velocities and eddy kinetic energies from an ocean model (Finite Element Sea Ice–Ice-Shelf–Ocean Model, FESOM) and winds from a regional climate model (CCLM) and ERA5 reanalysis, respectively. The presented investigation provides evidence for an influence of ocean bathymetry and associated currents on the mechanic weakening of sea ice and the accompanying occurrence of sea-ice leads with their characteristic spatial patterns. While the driving mechanisms for this observation are not yet understood in detail, the presented results can contribute to opening new hypotheses on ocean–sea-ice interactions. The individual contribution of ocean and atmosphere to regional lead dynamics is complex, and a deeper insight requires detailed mechanistic investigations in combination with considerations of coastal geometries. While the ocean influence on lead dynamics seems to act on a rather long-term scale (seasonal to interannual), the influence of wind appears to trigger sea-ice lead dynamics on shorter timescales of weeks to months and is largely controlled by individual events causing increased divergence. No significant pan-Arctic trends in wintertime leads can be observed.

Reliability modelling of ocean current energy conversion systems through both analytical and Monte Carlo methods

Nowadays, ocean and other renewable energies have capability to produce clean and sustainable electric energy. Thus, various countries have developed renewable power plants for providing significant part of own required demands. Ocean currents have kinetic energy that can produce electric energy by rotating stream-kind turbines installed undersea. The velocity of ocean currents varies over time that makes produced power of ocean current converters varies, too. This variation affects various aspects of electric grids containing significant ocean current generators. The current paper considers both hazard of assembled elements of converter and change of its produced power to analyzes reliability of ocean current converters. To compute reliability indices of electric grid containing ocean current converters, both analytical and Monte Carlo methodologies are applied. In analytical methodology, a several-state reliability presentation is created for ocean current converter to compute reliability indices by total capacity-probability table. In Monte Carlo methodology, we produce random numbers for assembled elements of ocean current converters, and other traditional production plants to compute reliability indices of electric network. To analyze the suitability of suggested methodologies, simulation outcomes are discussed.

Estimation of constant ocean current velocity based on SINS/DVL integrated navigation with an augmented observable quantities filter

Ideally, navigation and positioning information for Autonomous Underwater Vehicles (AUVs) can be provided by a combined navigation system based on a velocity-matching Strap-down Inertial Navigation System (SINS) and Doppler velocity log (DVL). However, the velocity accuracy of DVL is affected by ocean currents when the DVL is operating in water track mode in deep ocean scenarios. The velocity of the currents remains stable over small areas of the ocean within a few hours and can be considered a constant value. Estimation and compensation of the ocean current velocity is a prerequisite for high-precision navigation performance and an important topic for oceanographic research. In this paper, we propose an Augmented Observable Quantities (AOQ)-based filter that constructs a new measurement equation from the difference between the velocities of the previous state and the current state to decouple the ocean current velocity from the velocity error of SINS, and without introducing other sources to provide any information on velocity or position. The simulation and lake test show that the AOQ filter can estimate the ocean current velocity and mitigate its effect on navigation accuracy.

Numerical Study of the Local Scouring Process and Influencing Factors of Semi-Exposed Submarine Cables

Local scouring might result in the spanning of submarine cables, endangering their mechanical and electrical properties. In this contribution, a three-dimensional computational fluid dynamics simulation model is developed using FLOW-3D, and the scouring process of semi-exposed submarine cables is investigated. The effects of the sediment critical Shields number, sediment density, and ocean current velocity on local scouring are discussed, and variation rules for the submarine cables’ spanning time are provided. The results indicate that three scouring holes are formed around the submarine cables. The location of the bottom of the holes corresponds to that of the maximum shear velocity. The continuous development of scouring holes at the wake position leads to the spanning of the submarine cables. The increase in the sediment’s critical Shields number and sediment density, as well as the decrease in the ocean current velocity, will extend the time for maintaining the stability of the upstream scouring hole and retard the development velocity of the wake position and downstream scouring holes. The spanning time has a cubic relationship with the sediment’s critical Shields number, a linear relationship with the sediment density, and an exponential relationship with the ocean current velocity. In this paper, the local scouring process of semi-exposed submarine cables is studied, which provides a theoretical basis for the operation and maintenance of submarine cables.

Finite-time prescribed performance trajectory tracking control for the autonomous underwater helicopter

To address the complex trajectory tracking problem of AUH near the seabed when performing high-precision operations such as comprehensive underwater inspections for offshore engineering structures, a prescribed performance control strategy with a finite-time performance function (FTPF) and the fast terminal sliding surface is proposed. The influence of ocean current is fully considered by the combination of ocean current velocity estimated by a current observer and ocean current disturbance in the dynamic model. An arctan-type disturbance observer and neural network are designed to compensate for environmental disturbances, model uncertainty, and thruster failure effects on the system. The system stability is proven by the Lyapunov method, and the numerical simulation shows that the proposed scheme is effective, robust, and efficient, even under disturbances and thruster failure.

THE DETERMINATION OF OCEANOGRAPHIC PHYSICAL PROPERTIES AT THE COASTAL AREA OF TEGAL CITY

Throughout our observations, the coastal area of Tegal has not been equipped with geological data and oceanographic data, especially the distribution of ocean current patterns around the coastal area of Tegal City. It is very important to know the nature of the pattern of ocean currents near the coast because the coastal area is for recreational activities and shipping activities to and from the port. The speed of ocean currents and where they are heading needs to be described so that the users of the coastal area can monitor well about the danger or not. In the study of the current pattern at the coastal area of Tegal City, the tool used was designed based on the Lagrangian method and the Helmholtz method to obtain the direction and magnitude of the ocean current's velocity. The problem in determining the current pattern is that the current signal is the result of various signals having a certain frequency generated by several different forces. The next problem in determining the height of the waves, the initial coordinate point is needed as the zero point. From this problem, researchers are interested in the current pattern that is formed. Ocean currents are the mass movement of seawater from one place to another, either vertically (upward motion) or horizontally. The purpose of this study finds the energy of ocean currents and the physical properties of oceanography near the coast, to get the level of danger if used for recreational areas, shipping activities, and research activities.