Three different hull forms, monohull, catamaran, and SWATH (small waterplane area twin hull), of similar displacement and principal dimensions are designed for the systematic comparisons of their seakeeping performances in sea state 3 and 4, which is considered as the typical operational limit of CTVs (crew transfer vessels). The RAOs (response amplitude operators) of the three vessels for various wave headings were calculated from potential theory and BEM (boundary element method) in the frequency domain. In parallel, the time-domain simulations including viscous drag effects were also conducted for their motions in sea state 3 and 4 by using an independent in-house program, CHARM3D, which compared well against the frequency-domain results. When comparing seakeeping performance in sea state 3 and 4, the SWATH vessel outperforms both the catamaran and the monohull. The SWATH's 6DOF motion amplitudes are about half of those of catamaran and monohull since its heave-roll-pitch natural frequencies are much lower than the peak frequencies of incident waves in sea state 3 and 4. Therefore, we found that a SWATH can be operated up to a higher sea state 4 while a monohull and a catamaran can be used up to sea state 3. Our simulations also examined the three CTVs positioned about 10 m behind a monopile wind turbine structure of 10 m-diameter, by using our in-house two-body CHARM3D hydrodynamic interaction simulation programs. The two-body simulation results show minor shield effects in motions when operating behind the monopile. Again, SWATH shows the best seakeeping performance compared to other vessels.
Liquid sloshing in partially filled tanks induces time-varying loads and center-of-gravity (CG) shifts that can degrade stability and control. We present an experiment-driven framework that integrates laboratory measurements, computational fluid dynamics (CFD), and machine learning (ML) to accurately quantify and predict CG dynamics under sloshing excitation. The framework (i) reconstructs CG trajectories from synchronized load-cell and pressure measurements, (ii) validates a volume-of-fluid (VOF) OpenFOAM model against experiments using CG-centric metrics-peak-to-peak (P2P) amplitude, root-mean-square (RMS), and dominant frequency-with confidence intervals, and (iii) trains data-driven surrogate models that generalize within this jointly validated domain. To ensure transparent benchmarking, CFD validation is performed at water depths of 2, 4, and 6 cm (i.e., D/L approximate to {0.033, 0.067, 0.100}). Surrogate modeling (ML) is supported by a broader dataset covering depths from 3.0 to 7.2 cm, enabling interpolation across intermediate fill levels and testing generalization. Direct experiment-CFD overlays confirm agreement within 5-10% in RMS and peak-to-peak metrics. The ML surrogates, particularly the LSTM sequence model (two stacked layers, horizon H = 1000), achieve mean absolute errors around 1-2.5 mm across unseen fill levels, while simpler models (linear regression (LR), random forest (RF), and gradient boosting (GB)) remain competitive only in low-variability regimes. Results demonstrate that CG trajectory prediction and frequency content can be captured with high fidelity across fill levels, with ML surrogates providing substantial speedups for design-time trade studies. The surrogate's limits relative to CFD are clarified, and representative overlays (experiment vs. CFD vs. surrogate) provide direct visual and quantitative comparison, enhancing clarity, transparency, and reproducibility.
Two-planar tubular DKT-joints are widely used in offshore jacket structures, where accurate assessment of stress concentration factors (SCFs) is vital for evaluating fatigue performance. However, SCFs in DKT-joints with internal ring stiffeners have not been thoroughly studied, and no specific design equations currently exist for determining SCFs under axial loading which is typically the dominant load in these joints. In practice, engineers often rely on SCF data from uniplanar KT-joints to estimate values for multi-planar joints. This approach, however, overlooks the influence of out-of-plane braces, potentially resulting in significant inaccuracies. This study addresses this gap by analyzing SCFs on the chord side of ring-stiffened two-planar tubular DKT-joints under axial brace loading. The investigation is based on 118 finite element (FE) models, validated through both experimental data and existing numerical results. A comprehensive parametric FE study was conducted, followed by nonlinear regression analyses to derive four new equations that accurately predict SCFs. These proposed formulas offer a reliable tool for fatigue design applications.
This study experimentally evaluated the serviceability of multi-unit floating structures designed for marine city applications and verified the effect of wave-dissipating modules on wave motion reduction. Serviceability indicators were defined as vertical acceleration and inclination (pitch and roll), reflecting both the stable operation of topside facilities and human activity and discomfort (motion sickness). The experimental results showed that the installation of wave-dissipating modules reduced vertical RMS accelerations by approximately 3439% and pitch/roll inclinations by 21-42%. These improvements were consistently observed not only under 1-year return period wave conditions but also under extreme 100-year return period waves, thereby confirming the reliability of the proposed design. This study demonstrates that wave-dissipating modules are an effective design strategy to enhance the habitability and serviceability of floating infrastructures for marine cities. Furthermore, it highlights the need to extend conventional serviceability evaluation frameworks, which have been focused on industrial facilities, to ergonomics-based criteria that reflect the dual requirements of technical stability and humanoriented serviceability in marine city environments. These findings are expected to make an important contribution to the development of next-generation offshore infrastructures, such as marine cities and offshore renewable energy hubs, where both technical stability and human-oriented serviceability must be simultaneously ensured.
This research focuses on predicting the speed of ocean currents in the Sunda Strait by employing a Long Short-Term Memory (LSTM) model based on historical data. The approach includes data preprocessing, normalization of features using MinMaxScaler, segmentation of the data into training and testing sets, and the development of layered LSTM model architecture. The dataset comprises longitude, latitude, current velocity, and time information from 2022 to 2024. The findings indicate that the LSTM model can predict ocean current speeds with a Root Mean Squared Error (RMSE) of 13.66 cm/s, a mean absolute error (MAE) of 9.06 cm/s, and a determination coefficient (R2) of 0.87. The demonstration illustrated the typical design of ocean current speed fluctuations; however, forecasting unusual variations remains challenging. In summary, the LSTM model represents a practical approach for predicting ocean currents based on historical data, aiming to enhance prediction accuracy. This model will support navigation efforts and marine resource management in the Sunda Strait region.
The geometry of the underlip in onshore oscillating water column (OWC) systems is a key determinant of their hydrodynamic performance, directly influencing the wave-structure interactions and energy conversion efficiency. Recent advances in experimental hydrodynamics have highlighted the potential of geometric optimization; however, the specific influence of underlip configurations remains underexplored in the context of high-fidelity computational modeling. This study addresses this gap by performing a systematic evaluation of multiple underlip geometries using computational fluid dynamics (CFD) simulations. The analysis revealed that subtle geometric modifications could yield substantial performance gains. Notably, the circular underlip configuration achieved the highest improvement, enhancing the efficiency by 9.1% under a takeoff damping variation of 0.0079. This improvement was attributed to its capacity to suppress turbulent kinetic energy generation during wave impact, thereby reducing energy dissipation. The results present a novel, cost-effective design optimization pathway that requires minimal structural modification, contributing to the growing body of research on hydrodynamic enhancement strategies for OWC-based wave energy converters.
The hydrodynamic variations around floating structures in the naval ship applications are crucial to study its stability and hull optimization. The choice of the hull shape is capital to reduce the dispenses associated with energy. We address one-way fluid-structure interaction for a rigid (fixed) hull under a fluid dynamics model. Structural dynamics are not solved. The developed model is based on the coupling between Reynolds Averaged Navier-Stokes (RANS) equations and a k-epsilon epsilon turbulence model. This model extends naturally several models available in the literature including classical RANS models (steady and unsteady) and several RANS based models that neglect the turbulence phenomena (including transport and diffusion). The coupled RANS-based model is implemented numerically using finite element methods. We have chosen a two-dimensional ship hull 2D model to show how modelling can address turbulent flows around fixed structure. The numerical results obtained are encouraging and can allow us to study their optimization for the preliminary phase with a certain precision.
A one-way Fluid and Structure interaction method is introduced based on a lumped parameters method and a Reynolds-averaged Navier-Stokes solver with automatic viscous mesh generation. The lumped parameters method is numerically solved by fourth-order Runge-Kutta method. Unidirectional coupling by hydrodynamic interpolation and transformation from flow field to cable dynamics. A dimensional analysis is applied in this high Fluid and Structure Interaction (FSI) system. The Reynolds effects and Strouhal effects are both fully discussed. A two scale ratios model is applied in scale effect analysis. We found a similarity of hydrodynamic distribution and vortex shedding in scaled cable wake under only given two-ratios. The upper bound Reynolds effect is also discussed.
In the present study, the extended standard complex variable method (ESCVM) was proposed as a robust method for calculating the analysis of design sensitivities, including the first and second order. The standard complex variables method (SCVM) uses only the imaginary step for sensitivity analysis. In contrast, the presented method applies both the imaginary and the real part to enhance the effectiveness of the procedure. To illustrate this, the ESCVM is employed for the transited laminar incompressible flow. The Navier-Stokes equations are solved using finite element analysis and the developed SCVM was then applied to them. It has been shown that the first order (FO) sensitivity analysis is less susceptible to variations in the step size for both the standard and extended SCVM. However, it is evident that, unlike the SCVM, the extended SCVM was less dependent on the step size in estimating the second-order sensitivity (SO). This ability can be seen as an improvement in the efficiency and robustness of the extended standard method for complex variables.
Simultaneous localization and mapping (SLAM) is a critical capability for any autonomous underwater vehicle (AUV) in various underwater applications, including infrastructure inspection and seabed exploration. However, achieving robust and accurate state estimation in such environments remains a significant hurdle. This is primarily attributable to the inherent scarcity of geometric features in the subsea environment itself and the limited field of view (FoV) of imaging sonar used for feature acquisition. These factors collectively elevate the probability of iterative closest point (ICP) degeneracy, a frequent challenge in most SLAM solutions. To overcome this limitation, this study proposes a method that actively adjusts the imaging sonar's viewpoint to achieve more reliable ICP results and improve SLAM performance. First, we introduce a 2D tensor voting-based geometric descriptor to quantify the geometric information of features extracted from the sonar, enabling real-time assessment of degeneracy risk. Second, in viewpoints where degeneracy is anticipated, the sonar viewpoint is actively adjusted to acquire a more geometrically rich perspective. Third, omnidirectional features obtained during the viewpoint adjustment process are integrated as new keyframes into the SLAM graph, providing additional constraints. Our experimental validation comprises both a field feasibility test and simulations. The field feasibility test, conducted in a real underwater structured environment, confirmed the inherent scarcity of geometrically rich features in actual data acquisition. Subsequently, simulation experiments mimicking three underwater structured environments, including harbor and offshore oil rig structures, were performed. The results showed that the proposed method improved both the absolute trajectory error (ATE) and the ICP matching success rate compared to conventional fixed-sonar-heading SLAM even when following an identical trajectory in both methods.
Maritime Natural Caves (MNCs) are real and natural representations of shoreline Oscillating Water Column (OWC) devices. Recently, one particular MNC located in Cidade Velha on Santiago Island, Cabo Verde, has been the focus of several studies aimed at analyzing its behavior and energy performance under different wave climate conditions. This study investigates the operation of this MNC, focusing on the impact of airflow damping caused by its power take-off mechanism, represented here by orifices (ORs) with different cross-section areas and Wells turbines with various rotor blade stagger angles (B) on its energy extraction and production capacity. Our study showed that the power available from the MNC is linked to the flow damping characterized by the area contraction coefficient and the linear damping coefficient for turbines. The optimum flow contraction coefficient was found to be CC = 0.269, which maximizes both the average and peak power available from the cave, reaching 439.5 W and 4237.9 W, respectively. When using turbines, the average power available ranged from 38.3 W to 80.5 W, which was considerably lower than the range observed with orifices (132.2 W - 454.1 W). This suggests that the damping capacity of the turbines could be improved. The turbine with the highest damping coefficient (B=15) produced the highest values of both available and converted power. However, it showed limitations in effectively converting the energy made availabThe turbines with moderatele by the MNC. The turbines with moderate damping, between 2.52 cmH2O m3/s (turbine with B=0) and 2.84 cmH2O m3/s (B=10), demonstrated better energy conversion performance with average efficiency of 23.6% and 20.7%, respectively. The remaining turbines exhibited lower average efficiency: 16.6% (B=15) and 13.3% (B=5). However, all turbines showed losses of efficiency when the MNC operated with high airflow rates. The turbine with B=0 had a contraction coefficient of 0.545, significantly higher than the optimal found for the orifices. The ideal contraction coefficient could be achieved by increasing both the turbine blades and hub by 60%.
Through-the-thickness stress distribution in a tubular member has a profound effect on the fatigue behavior of tubular joints commonly found in steel offshore structures. Such stress distribution can be characterized by the degree of bending (DoB). Although tubular T-joints with concrete-filled chords are commonly used in offshore tubular structures and the concrete fill can have a significant effect on the DoB values at the brace-to-chord intersection, no investigation has been reported on the DoB in tubular T-joints with concrete-filled chords due to the complexity of the problem and high cost involved. In the present research, data extracted from 162 stress analyses conducted on 81 finite element (FE) models subjected to brace tension and compression, verified based on available experimental data and parametric equations, was used to study the effects of geometrical parameters on the DoB values in tubular T-joints with concrete-filled chords. Parametric FE study was followed by a set of nonlinear regression analyses to develop four new DoB parametric equations for the fatigue analysis and design of axially loaded tubular T-joints with concrete-filled chords.
Wind turbines often exhibit component failures before completion of their typical 20-year design life. Unexpected part failures increase the associated costs of their Operations & Maintenance (O&M). In turn, this raises the associated Levelized Cost of Electricity (LCOE), making this method of power generation less competitive compared to traditional methods. One of the components of particular concern is the slew or "pitch" bearing connecting the root of the blades to the rotor hub. The Drivetrain Reliability Collaborative (DRC) of the National Renewable Energy Lab (NREL) has begun investigations into pitch bearing reliability. One outcome of this is a collection campaign on the 1.5MW Wind Turbine at the NREL Flatirons Campus to observe variations in pitch bearing strains during real operation. This entailed outfitting the turbine with additional instrumentation such as strain gauges in the rotor hub. The present study intends to extend the applicability of the DRC1.5 field tests by relating the strain signals to standard operational output. Machine Learning (ML) techniques include supervised learning by Artificial Neural Networks (ANN) and Long-Short-Term Memory (LSTM), as well as Principal Component Analysis (PCA). The same DRC test data sets were applied to ANN and LSTM and their results are compared. Discussions of results describe which generalize best for the purpose of sensor reduction, and which of the operational signals are most indicative of bearing strain. The results showed that both ANN and LSTM predicted future (or nonfunctional) sensor signals well with slightly higher accuracy by LSTM. Post processing of time series predictions can then track the progression of fatigue damage without additional sensors. Examining the prediction results details the model performance and highlights the relevance for wind turbine condition monitoring. Incorporation of learning techniques is presented as a systematic approach that can be replicated to simplify and optimize real monitoring strategies.
The rapid growth of offshore wind energy has driven the need for advanced foundation designs to support larger turbines in challenging marine environments. This study evaluates the performance of two key shallow foundation types for offshore wind turbines-gravity-based foundations (GBFs) and monopod suction buckets (MSBs)-using finite element analysis (FEA) in ABAQUS. Conducted at a site in the Dorood Oil Field in the Persian Gulf, the analysis compares soil stress, foundation settlement, lateral displacement, and rotation under gravitational and environmental loads. Eight GBF configurations with varying height-to-diameter ratios and ten MSB configurations with different skirt length to diameter ratios were examined. Results show that MSB foundations generally exhibit lower settlement and comparable lateral stability compared to GBFs, particularly for larger configurations, due to effective load transfer to deeper soil layers. However, GBFs demonstrate lower rotation angles at higher h/D ratios. Optimal configurations, GBF-1 and MSB-1, were identified as balanced designs offering reliable performance. These findings provide valuable insights for optimizing foundation design in offshore wind turbine projects, emphasizing the critical role of foundation geometry and soil-structure interaction.
The influence of the second and third order wave loads on the TLP FOWT responses are investigated in this study. The second order wave loads are calculated by commercial wave diffraction/radiation analysis software tool. The third order wave loads calculation methods are developed in this study based on the well-known FNV formulation. The third order wave force is applied on the platform in dynamic simulation models as external force. It is observed that the third order wave force is cubic proportional to the wave heights. The sum frequency third order wave force has periods about 1/3 of the first order wave periods. With the same wave heights, the third order wave forces on the surface piercing structure are higher in shallower waters. The 2nd order wave loads have higher influences on the FOWT responses than the 3rd order wave loads in both fatigue and extreme load cases. The 3rd order wave force influence is negligible in fatigue load cases. The high order wave loads have more impact on the tower and mooring system responses than platform motion. On a TLP FOWT, the tower and mooring system usually feature high frequency resonance susceptible to excitation from sum frequency 2nd and 3rd order wave loads.
Buoys are a crucial structure used offshore, and the data acquired from them is essential for marine navigation, offshore engineering, coastal management, weather forecasting and wave energy research. To optimize wave energy extraction and guarantee the dependability of ocean-based structures, accurate heave displacement forecasting is essential. In order to enhance the estimation of heave displacement, a unique hybrid model that combines a Long Short-Term Memory (LSTM) network with the Frequency Enhanced Decomposition Transformer (FEDformer) is implemented. The proposed FEDformer - LSTM hybrid model competently captures long-range dependencies and non-linear temporal patterns in wave data by employing the frequency-domain decomposition powers of FEDformer and the temporal learning advantages of LSTM. Experimental data are retrieved from the buoy data of the National Institute of Ocean Technology (NIOT), which includes wave height, wind speed, and other data from key maritime areas. The hybrid model beats state-of-the-art forcasting algorithms and independent deep-learning techniques in terms of correlation metrics, Mean Absolute Error (MAE) and Root Meam Square Error (RMSE), affording to proportional tests carried out using real-world buoy datasets. The findings indicate that the FEDformer-LSTM model is more appropriate prediction model for the proposed application.
This study presents a comprehensive data-driven approach for the design and optimization of multi-chamber oscillating water column (OWC) wave energy converters by integrating high-fidelity computational fluid dynamics (CFD) simulations with machine learning (ML) techniques. The CFD model was rigorously validated against experimental data from literature results, with good agreement observed in both hydrodynamic efficiency and power output. Further, a large input data has been generated with distinct simulation cases, spanning single-, double-, and triple-chamber chamber configurations under various wave conditions with kh ranging from 2.0 s to 5.5 s, were conducted. The CFD-generated dataset was employed to train several ML models-polynomial regression, decision trees, random forest, XGBoost, support vector regression, and multilayer perceptron. XGBoost demonstrated better performance compared to the other machine learning models evaluated. Furthermore, to identify the optimal design configuration, Latin Hypercube Sampling was employed to randomly generate 1,000 distinct OWC configurations, which were then evaluated using the XGBoost model. The top ten configurations were identified, with the highest predicted power output of 36.40 W obtained from the dual-chamber OWC configuration. These findings confirm the potential of ML-driven models to significantly reduce computational cost and accelerate the design of efficient wave energy systems.
This paper provides a novel numerical approach in order to simulate ocean wave propagation, integrating sigma-transformation with the finite difference schemes. The governing equations are derived from viscous flow theory, specifically the incompressible Navier-Stokes equations under the assumption of negligible viscosity (Euler's equations). The continuity equation represents the conservation of mass and is a fundamental part of fluid dynamics, applicable to both potential flow theory and viscous flow theory, and momentum equations represent the conservation of momentum in the horizontal and vertical directions, respectively. The method enhances accuracy and stability in modeling wave dynamics in deep and transitional waters while effectively handling complex geometries and boundary conditions. Numerical experiments indicate high precision and the capability to capture nonlinear wave behavior, particularly in comparisons with linear and second-order Stokes theory. Stability analyses confirm that the framework maintains reliable results across varying time steps with minimal error growth. This research provides a powerful tool for ocean wave simulation, holding significant implications for marine engineering, environmental studies, and coastal management.
For safe navigation of ships, it is essential to accurately detect and continuously track surrounding obstacles. The horizon, serving as the boundary between the sky and the sea, plays a crucial role in enabling ships to maintain precise routes and effectively assess the positions of obstacles. Reliable horizon detection is, therefore, highly significant. Conventional horizon detection methods, such as the Hough transform and edge-based approaches, have shown good performance in relatively simple environments. However, their accuracy significantly decreases in realistic maritime environments, which involve complex factors such as terrain, obstacles, and reflections. To address these limitations, this study proposes a novel horizon detection method that fine-tunes the SAM (Segment Anything Model), a deep learning model specifically designed for maritime images. An efficient adapter-based fine-tuning technique was implemented on the SAM's mask decoder, enabling the model to effectively learn the distinct visual and structural characteristics of maritime environments. Experimental evaluations demonstrated that the method combining the SAM fine-tuning with the vertical edge response approach achieved superior performance, significantly reducing height error and slope error by an average of 75.58% and 70.17%, respectively, even in highly complex environments. These findings highlight the superior accuracy and robustness of the proposed method, indicating its substantial potential for practical applications in autonomous navigation systems and enhanced maritime safety.
In general, for an autonomous underwater vehicle (AUVs), its shape is one of an important parameter influencing its performance. AUVs can be propelled using conventional propellers or by undulating (flapping), its body like aquatic animals. Most of the existing AUVs that propels by undulating its body are multi-segmented and rectangular. However, research studies have often focussed on NACA hydrofoils, despite the prevalence of rectangular designs in AUVs. This paper aims to fill the gap in the current research by conducting a comparative study of the thrust force generation and efficiency of rectangular fin and hydrofoil. Besides, a merged body shape (MBS) has been proposed, combining the advantages of hydrofoil and fin. A comprehensive analysis has been made by comparing the performance of an undulating NACA 0012 foil, a rectangular fin with the proposed MBS at St = 0.2-1, for non-dimensional wavelengths (lambda*= 0.8 and 1.0) at Re = 1000. Increase in thrust forces are observed with increase in St and lambda*. The efficiency at lambda*=0.8 is higher than that at lambda*=1.0, indicating optimal wavelength for high efficiency. The MBS generates a mean thrust coefficient comparable to the hydrofoil, offering a balance of thrust and modular adaptability.
