Real Ocean Research Articles

Multi-scale concurrent design of a 100 kW wave energy converter

Wave energy converters (WEC) are complex systems comprising multiple subsystems including wave capture structure and station keeping, power takeoff (PTO), and control. Designing the whole WEC system requires an effective design approach that considers mutual couplings among them throughout the entire design process. Moreover, the traditional serial design approach, transitioning from small-scale to full-scale designs incrementally, often overlooks issues related to scaling factors. This can lead to unexpected challenges and delays towards real ocean deployment. To address system-level considerations and scaling challenges in WEC design, this study introduces a novel multi-scale concurrent design approach. It facilitates full-scale WEC design from the early concept to ocean test planning. This approach ensures a holistic and effective design process that considers interactions among subsystems at each design stage and incorporates control co-design starting with early concept development. To demonstrate the presented approach, we introduce a case study focused on the design of a 100 kW floating oscillating surge wave energy converter (FOSWEC) for PacWave South ocean test site. This includes the design of wave capture structure and station keeping, PTO, control, ocean test planning, and techno-economic analysis. The case study showcases the effectiveness of the proposed approach, offering invaluable guidance and insights for future WEC development and support efficient, cost-effective collaboration in WEC design and testing.

The suppression of ocean waves by biogenic slicks.

Ocean waves are significantly damped by biogenic surfactants, which accumulate at the sea surface in every ocean basin. The growth, development, and breaking of short wind-driven surface waves are key mediators of the air-sea exchange of momentum, heat and trace gases. The mechanisms through which surfactants suppress waves have been studied in great detail through careful laboratory experimentation in quasi-one-dimensional wave tanks. However, the spatial scales over which this damping occurs in structurally complex surfactant slicks on the real ocean have not been resolved. Here, we present the results of field observations of the spatial response of decimetre- to millimetre-scale waves to biogenic surfactant slicks. We found that wave damping in organic material-rich coastal waters resulted in a net (spatio-temporally averaged) reduction of approximately 50% in wave slope variance relative to the open ocean for low to moderate wind speeds. This reduction of wave slope variance is understood to result in a corresponding reduction in momentum input to the wave field. This significant effect had thus far evaded quantification due in large part to the enormous range of scales required for its description-spanning the sea surface microlayer to the ocean submesoscale.

Prediction of Sea Surface Current Around the Korean Peninsula Using Artificial Neural Networks

AbstractThe prediction of sea surface currents is essential for various marine activities such as disaster monitoring, fishing industries, and search and rescue operations. Continuous improvements in numerical models have made it possible to predict more realistic oceans using data assimilation and fine spatial resolution. However, such complicated ocean models require high computational power and time, making them difficult to use for near‐real time forecasting. To compensate for these high computational costs, novel approaches with efficient computational costs combined with numerical model outputs need to be developed. Artificial neural networks could be one of the solutions as they require low computational power for prediction, owing to pre‐trained networks. Here, we present a prediction framework applicable to surface current prediction in the seas around the Korean Peninsula using three‐dimensional (3‐D) convolutional neural networks. The network is based on a 3‐D U‐shaped network structure and is modified to predict sea surface currents using oceanic and atmospheric variables. The forecast procedure is optimized to minimize the error of the next day's sea surface current field with a spatial resolution of 1/24° and its recursively predicting structure allows more days to be predicted. The network performance is evaluated by changing the input days and variables to find the optimal surface‐current‐prediction artificial neural network model. The optimized model shows a root mean squared error of 2.3 cm/s for the first forecast day, demonstrating its strong potential for practical use in the near future.

Nonlinear analysis of hydrodynamics of a shallow-draft floating wind turbine

AbstractThis study investigates numerically the dynamic responses of the T-Omega Wind novel concept of Floating Offshore Wind Turbine. The turbine is light-weight, has a shallow-draft and a relatively high centre of gravity that allows it to glide over harsh marine environments. The turbine responses are studied under regular wave excitation, considering most probable ranges of discrete sea wave heights and periods representative of real ocean conditions. A multibody virtual model is developed, simplified to a rigid 6 DOF system and experimentally validated in the state-of-art Marine Simulator to define the types of dynamical responses for both “Low” and “High” Sea States. The dynamics of coupled heave and pitch DOFs are evaluated with time histories, phase-plane portraits, Poincaré sections and FFT analyses to conclude that period-1 stable solutions exist for all studied cases of “Low Sea States”, whereas period-2, period-3 and period-4 periodic responses are identified for short wave periods of excitation under “High Sea States” conditions. Simulation results show that regions where period-1 responses exist are highly sensitive to wave height and can widen as the wave amplitude reduces. Finally, the turbines’ nonlinearities generated by the floats’ geometry are observed in this dynamical system, which are identified to be related to variation in float waterplane area and particularly observable for “High Sea States”.

PID Controller Based on Improved DDPG for Trajectory Tracking Control of USV

When navigating dynamic ocean environments characterized by significant wave and wind disturbances, USVs encounter time-varying external interferences and underactuated limitations. This results in reduced navigational stability and increased difficulty in trajectory tracking. Controllers based on deterministic models or non-adaptive control parameters often fail to achieve the desired performance. To enhance the adaptability of USV motion controllers, this paper proposes a trajectory tracking control algorithm that calculates PID control parameters using an improved Deep Deterministic Policy Gradient (DDPG) algorithm. Firstly, the maneuvering motion model and parameters for USVs are introduced, along with the guidance law for path tracking and the PID control algorithm. Secondly, a detailed explanation of the proposed method is provided, including the state, action, and reward settings for training the Reinforcement Learning (RL) model. Thirdly, the simulations of various algorithms, including the proposed controller, are presented and analyzed for comparison, demonstrating the superiority of the proposed algorithm. Finally, a maneuvering experiment under wave conditions was conducted in a marine tank using the proposed algorithm, proving its feasibility and effectiveness. This research contributes to the intelligent navigation of USVs in real ocean environments and facilitates the execution of subsequent specific tasks.

The enhanced performance of NaFe2PO4(SO4)2/C electrode materials in the desalination of brackish water by capacitive deionization

In this study partial substitution of phosphates in Na3Fe2(PO4)3/C (NFP/C) by sulphates to prepare Na2Fe2(PO4)2SO4 (NFP2S/C) and NaFe2PO4(SO4)2/C (NFPS/C) was carried out by dissolution-evaporation method. The idea is to adjust the properties of NFP/C owing to the differences in the inductive effect and sizes of sulphates and phosphates which results the obtained NFPS/C to record excellent electrochemical and desalination performance. Several characterization techniques used to analyse the synthesized materials confirm the successful doping and structure integrity. Electrochemical study reveals NFPS/C to exhibit the highest capacitance of 127.4 F/g when scanned at 10 mV/s. The results show that when the potential of 1.2 V is applied to desalinate 500 mg/L salt solution the salt removal of about 17.3 mg/g is achieved by NFPS/C while NFP/C attaining 9.8 mg/g. The NFPS/C was then tested in the desalination of real Ocean water and promising results obtained. The salt removal performance presented makes the developed materials promising for application in brackish water desalination by capacitive deionization.

A Framework for Assessing Ocean Mixed Layer Depth Evolution

AbstractThe ocean surface mixed layer plays a crucial role as an entry or exit point for heat, salt, momentum, and nutrients from the surface to the deep ocean. In this study, we introduce a framework to assess the evolution of the mixed layer depth (MLD) for realistic forcings and preconditioning conditions. Our approach involves a physically‐based parameter space defined by three dimensionless numbers: λs representing the relative contribution of the buoyancy flux and the wind stress at the air‐sea interface, Rh the Richardson number which characterizes the stability of the water column relative to the wind shear, and f/Nh which characterizes the importance of the Earth's rotation (ratio of the Coriolis frequency f and the pycnocline stratification Nh). Four MLD evolution regimes (“restratification,” “stable,” “deepening,” and “strong deepening”) are defined based on the values of the normalized temporal evolution of the MLD. We evaluate the 3D parameter space in the context of 1D simulations and we find that considering only the two dimensions (λs, Rh) is the best choice of 2D projection of this 3D parameter space. We then demonstrate the utility of this two‐dimensional λs − Rh parameter space to compare 3D realistic ocean simulations: we discuss the impact of the horizontal resolution (1°, 1/12°, or 1/60°) and the Gent‐McWilliams parameterization on MLD evolution regimes. Finally, a proof of concept of using observational data as a truth indicates how the parameter space could be used for model calibration.

A Study on the Impact of Vertical Grid Parameter Perturbations in the Regional Ocean Modeling System

In this study, the Regional Ocean Modeling System (ROMS) is employed to construct a three-dimensional barotropic ocean model with a monodirectional upper boundary and homogeneous and steady wind covering the entire computation area. Eight perturbation experiments are designed to determine the vertical grid distribution difference with high resolution at the surface and bottom. Two types are considered in the model, including removing the Coriolis force (type 1) and employing a different Coriolis force (type 2). According to the experiments, the velocity of the current in type 1 yields uncertainty, and wind energy could penetrate the upper ocean and reach the abyss. The surface velocity in type 2 is fundamentally compatible with the empirical relationship constructed by Ekman, and the curved lines of the vertical distribution of horizontal currents nearly match. For type 1, the velocity is very strong from the sea surface to the bottom. When comparing type 1 and type 2 cases, the Coriolis force obstructs the wind energy transfer into the deep ocean. In addition, the European Centre for Medium-Range Weather Forecasts (ECMWF)’s global surface wind distribution indicates that the realistic ocean upper wind boundary is similar to the numerical experiment in the Pacific and Atlantic oceans, where the wind direction is along the latitude line at the equator. In order to make the experimental situation as close as possible to the real ocean, validation experiments are conducted in this study to consider the uncertainty in the current profile at the equator. The simulation results of type 1 differ significantly from the data obtained from the real ocean. This uncertainty may transfer the signal to higher latitudes, causing incorrect simulation results, especially in the critical region. Overall, this research not only makes discoveries in physical ocean theory but also guides predictive and forecasting techniques for ocean modeling.

Ocean Complexity Shapes Sea Surface Temperature Variability in a CESM2 Coupled Model Hierarchy

Abstract To improve understanding of ocean processes impacting monthly sea surface temperature (SST) variability, we analyze a Community Earth System Model, version 2, hierarchy in which models vary only in their degree of ocean complexity. The most realistic ocean is a dynamical ocean model, as part of a fully coupled model (FCM). The next most realistic ocean, from a mechanically decoupled model (MDM), is like the FCM but excludes anomalous wind stress–driven ocean variability. The simplest ocean is a slab ocean model (SOM). Inclusion of a buoyancy coupled dynamic ocean as in the MDM, which includes temperature advection and vertical mixing absent in the SOM, leads to dampening of SST variance everywhere and reduced persistence of SST anomalies in the high latitudes and equatorial Pacific compared to the SOM. Inclusion of anomalous wind stress–driven ocean dynamics as in the FCM leads to higher SST variance and longer persistence time scales in most regions compared to the MDM. The net role of the dynamic ocean, as an overall dampener or amplifier of anomalous SST variance and persistence, is regionally dependent. Notably, we find that efforts to reduce the complexity of the ocean models in the SOM and MDM configurations result in changes in the magnitude of the thermodynamic forcing of SST variability compared to the FCM. These changes, in part, stem from differences in the seasonally varying mixed layer depth and should be considered when attempting to quantify the relative contribution of certain ocean mechanisms to differences in SST variability between the models.

Bio-inspired target detection method of active sonar based on multi-ping perception

Abstract The performance of the active sonar may degrade severely in real ocean environments, influenced by strong reverberation and multiple clutters. Inspired by the observation that dolphins always utilize a train of clicks when detecting underwater, we proposed an active target detection method by making full use of the multi-ping echo information which encompasses the differences between targets and reverberation/clutters. The detection performance of the click trains with different parameters were analysed first to give an insight on signal choice. Then we achieved apparent reverberation/clutter suppression result by utilizing the correlations and differences between multi-ping echoes. Further, the relative motion status between the target and the sonar platform could be perceived within multiple echoes, which strengthened the detection ability of weak target. We proposed two weighting strategies for multi-ping co-utilization. Either equal weights or unequal weights based on the different envelopes of dolphin echolocation click trains were exerted on multiple echoes, which can achieve different perception results for the relative target motion status. Both simulation and lake trials were conducted to verify the performance of the proposed method. The proposed method effectively suppress reveberaton and noise background by around 2-3 dB and achieve bionic detection of weak targets compared to traditional active sonar signal processing method.

Simulation and parameter determination of the net sorption of phenanthrene by sediment particles

The distribution of polycyclic aromatic hydrocarbons (PAHs) in the ocean is affected by the sorption-desorption process of sediment particles. This process is determined by the concentration of PAHs in seawater, water temperature, and organic matter content of sediment particles. Quantitative relationships between the net sorption rates (=the difference of sorption and desorption rates) and these factors have not been established yet and used in PAH transport models. In this study, phenanthrene was chosen as the representative of PAHs. Three groups of experimental data were collected to address the dependence of the net sorption processes on the initial concentration, water temperature, and organic carbon content representing organic matter content. One-site and two-compartment mass-transfer models were tested to represent the experimental data using various parameters. The results showed that the two-compartment mass-transfer model performed better than the one-site mass-transfer model. The parameters of the two-compartment mass-transfer model include the sorption rate coefficients kafand kas (L g−1 min−1), and the desorption rate coefficients kdf and kds (min−1). The parameters at different temperatures and organic carbon contents were obtained by numerical simulations. Linear relationships were obtained between the parameters and water temperature, as well as organic carbon content. kaf, kas and kdf decreased linearly, while kds increased linearly with temperature. kaf, kas and kdf increased linearly, while kds decreased linearly with organic carbon content. The r2 values between the simulation results based on the relationships and the experimental results reached 0.96–0.99, which supports the application of the model to simulate sorption-desorption processes at different water temperatures and organic carbon contents in a realistic ocean.

Robust speed estimation for a moving harmonic acoustic source with a single stationary sensor.

A noise-insensitive cost function was developed for estimating the speed of harmonic acoustic sources in uniform linear motion. This function weighs and integrates the energy distribution of received tones in the time-frequency plane to enhance the robustness of parameter estimation under low signal-to-noise ratio conditions, where weight values are intentionally combined with the law of observed instantaneous frequency. As the cost function is differentiable, the procedure of parameter estimations also has high computing efficiency. Processing data of SWellEx-96 experiments with real ocean noise confirmed the anti-noise capabilities of this cost function to conventional processing methods.

High-sensitivity blue-energy-shuttle and in-situ electrical behaviors in ocean

Although various triboelectric nanogenerators (TENGs) and hybrid ocean kinetic energy harvesters (OKEHs) have been devised for blue energy harvesting, the lack of studies in the real ocean has hindered their progress. Here, a high-sensitivity modular shuttle-like hybrid nanogenerator (MSHG) is prepared, comprising contact-separation TENG, piezoelectric nanogenerator (PENG), and electromagnetic generator (EMG) components. Its acceleration feature and electrical performance were systematically investigated under various conditions, from laboratory setups to real ocean waves. The MSHG responds sensitively to low-level excitations and exhibits robust energy harvesting capability in the ocean. Statistical analysis shows that its most probable output frequency reaches 0.83 Hz, which is about four times higher than the main frequency of the ocean wave (0.1–0.2 Hz). A 12-integrated MSHG system effectively harvests ocean wave energy to power a water-quality detector, while its most probable acceleration amplitude is approximately 24% of the maximum in a simulated linear motor motion and 57% of the mean value under simulated water wave. The findings provide valuable insights for advancing nanogenerator applications in real oceans.

Investigating the efficacy of autoencoders and other machine learning methods for studying dynamic oceanic processes in long-range acoustic propagation environments

Long-range acoustic propagation is a topic of great interest in applications like acoustic thermometry and underwater navigation. These applications utilize the measured arrival times and structure obtained from the transmitted probe pulses. However, they are highly dependent on the stability and repeatability of the ocean channel. In real oceans, random medium effects like internal waves [1] can induce considerable fluctuations and distortions to the received probe pulses. Our objective here is to investigate the use of machine learning (ML) methods such as autoencoders and other deep learning architectures to see if they can unravel and give insight into the dynamics of the ocean processes generating the fluctuations. In particular, we will investigate geometric concepts from braid, loops, and knot theory that can capture the changing shapes of smoothly deforming features that represent these processes. For the analysis, we will use transmitted frequency maximum length sequence (MLS) signal probe pulses from the 75 Hz Kauai Beacon source received at the International Monitoring Station near Wake Island at a nominal distance of 3500 km. We show that ML analysis can provide some useful insights. J. Xu, “Effects of internal waves on low frequency, long range, acoustic propagation in the deep ocean,” MIT Ph. D. dissertation (2007).

Extraction of comprehensive feature for active target detection using two hand-crafted acoustic features

We propose a model for creating a new feature suitable for classifying targets and non-targets using a small active sonar data. The proposed model extracts a comprehensive feature from two different hand-crafted features using deep-learning (DL). To extract comprehensive feature, we contrived a complementary learning module (CLM), which consists of two stages. In the first stage, a transformer encoder was adopted to reinforce the input features and then complementarily learned through attention mechanism in the following stage. The output features from the CLM are passed through a shallow CNN to classify targets and non-targets. In addition, an uncertainty quantification method is proposed using two CLMs to quickly add new samples acquired in the real ocean dataset. Using the decisions from two CLMs, we calculate the variance and determine which samples from the entire dataset are worthy of prioritized addition. Two real-ocean datasets were used to examine the model generalization and compare the classification results with conventional DL models. The generalization performance of the proposed model was comparable or superior to other DL models and uncertainty quantificaion method improved the generalization performance. [Work supported by Korea Research Institute for defense Technology planning and advancement(KRIT)—Grant funded by the Korea government (DAPA—Defense Acquisition Program Administration) (No. KRIT-CT-23-026, Integrated Underwater Surveillance Research Center for Adapting Future Technologies, 2024)]

Probing the Nonlinear Interactions of Supertidal Internal Waves Using a High-Resolution Regional Ocean Model

Abstract The internal wave (IW) continuum of a regional ocean model is studied in terms of the vertical spectral kinetic energy (KE) fluxes and transfers at high vertical wavenumbers. Previous work has shown that this model permits a partial representation of the IW cascade. In this work, vertical spectral KE flux is decomposed into catalyst, source, and destination vertical modes and frequency bands of nonlinear scattering, a framework that allows for the discernment of different types of nonlinear interactions involving both waves and eddies. Energy transfer within the supertidal IW continuum is found to be strongly dependent on resolution. Specifically, at a horizontal grid spacing of 1/48°, most KE in the supertidal continuum arrives there from lower-frequency modes through a single nonlinear interaction, whereas at 1/384° and with sufficient vertical resolution KE transfers within the supertidal IW continuum are comparable in size to KE transfer from lower-frequency modes. Additionally, comparisons are made with existing theoretical and observational work on energy pathways in the IW continuum. Induced diffusion (ID) is found to be associated with a weak forward frequency transfer within the supertidal IW continuum. ID is also limited to the highest vertical wavenumbers and is more sensitive to resolution relative to spectrally local interactions. At the same time, ID-like processes involving high-vertical-wavenumber near-inertial and tidal waves as well as low-vertical-wavenumber eddy fields are substantial, suggesting that the processes giving rise to a Garrett–Munk-like spectra in the present numerical simulation and perhaps the real ocean may be more varied than in idealized or wave-only frameworks.

Three-dimensional numerical simulations of oblique internal solitary wave-wave interactions in the South China Sea

Satellite images show that the oblique internal solitary wave-wave interactions frequently occur in the South China Sea, especially in the periphery of Dongsha Island. Depending on the amplitudes and angles of initial oblique waves, theoretical works illustrated that the evolution pattern falls into different regimes characterised by the respective four-fold augmentation of wave amplitudes (relative to the initial waves) and occurrence of Mach stem waves in the interaction region. Nevertheless, these results were based on the reduced theories rooted from the primitive Navier-Stokes equations and the disparities induced by these simplifications with the scenarios in realistic ocean are still unclear. To fill this research gap, three-dimensional numerical simulations in the South China Sea are used to evaluate the oblique internal solitary wave-wave interactions. It is found that transformations between mode-1 and mode-2 waves occur near the Dongsha Island when two waves obliquely collide, together with a small portion of energy is converted into higher modes, most of which is dissipated locally due to their unstable vertical structures. This conclusion has been seldom reported in previous studies (if any). These oblique interactions are essentially nonlinear and impacted by the dynamical factors, such as varying depth, background current, etc., exhibiting complicated variations of waveforms and energy, which, further, enhance the mixing at local sites in the mechanism of both shear and convective instabilities indicated by the Richardson number.

Dynamic ocean inverse modeling based on differentiable rendering

Learning and inferring underlying motion patterns of captured 2D scenes and then re-creating dynamic evolution consistent with the real-world natural phenomena have high appeal for graphics and animation. To bridge the technical gap between virtual and real environments, we focus on the inverse modeling and reconstruction of visually consistent and property-verifiable oceans, taking advantage of deep learning and differentiable physics to learn geometry and constitute waves in a self-supervised manner. First, we infer hierarchical geometry using two networks, which are optimized via the differentiable renderer. We extract wave components from the sequence of inferred geometry through a network equipped with a differentiable ocean model. Then, ocean dynamics can be evolved using the reconstructed wave components. Through extensive experiments, we verify that our new method yields satisfactory results for both geometry reconstruction and wave estimation. Moreover, the new framework has the inverse modeling potential to facilitate a host of graphics applications, such as the rapid production of physically accurate scene animation and editing guided by real ocean scenes.

On the steady-state interfacial waves with two-dimensional type-A double exact resonance

Steady-state interfacial waves under two-dimensional (2D) type-A exact triad resonance and other related resonances are researched in a two-layer liquid model with a free surface in contact with air. Five groups (groups 1–5) of convergent series solutions are achieved via the homotopy analysis method. It is found that the phenomenon of double exact resonance could exist in periodic interfacial waves if physical parameters correspond to the intersection of two exact resonance curves. The double exact resonance considered here contains a 2D type-A triad resonance and an other resonance. Under the 2D type-A exact triad resonance, the other resonant triad could obviously enlarge or reduce the wave amplitudes and energy proportions of primary and resonant components. Nevertheless, other resonant quartet, quintet, sextet, and septet all produce no influence on interfacial waves when the 2D type-A exact triad resonance occurs. The above-mentioned results indicate that in the neighborhood of the double exact triad resonance, small perturbations of wave vector of a primary component can cause huge changes on wave profiles of free surface and interface, wave amplitude spectrum, and energy distribution of internal waves in real ocean. In addition, the closer the interfacial waves are to the double exact triad resonance, the more possible energy combinations exist in the wave system, and the greater the number of steady-state interfacial wave solutions. All of this should deepen our understanding of nonlinear resonance interactions in short-crested internal waves.

Dynamic Characterization, Flow Modeling, and Hierarchical Control of an Energy-Harvesting Underwater Kite in Realistic Ocean Conditions

This paper presents a hierarchical control framework for a kite-based marine hydrokinetic (MHK) system, along with a detailed characterization of the dynamic and energetic performance of the system under realistic flow conditions. The underwater kite, which is designed to be deployed off of an offshore floating platform, features a closed-loop controller that executes power-augmenting, cyclic cross-current flight. The robustness of the kite's undersea flight control algorithm is demonstrated in a realistic four-dimensional flow model (which captures both low-and high-frequency spatiotemporal variations in the current) that accounts for turbulence and wave effects, which is coupled with a detailed dynamic model that captures the six-degree-of-freedom kite and floating platform dynamics, in addition to the tether dynamics. Using data obtained by the Coastal Data Information Program (CDIP) 192 Oregon Inlet buoy [1], wave data from the Wave Information Studies Hindcast model [2], and a spectral turbulence model developed at Florida Atlantic University, we demonstrate the robustness of the kite's control system and the sensitivity of both average net power output and peak-to-average power to wave parameters. In common wave conditions, the average and net power output are shown to be highly robust to the peak period and significant wave height. In extreme wave conditions, the peak-to-average power ratio is shown to be highly positively correlated with an effective wave energy density metric, which characterizes the wave energy density presented to the kite system based on a weighted distribution along depth of the kite.