Marine ecosystem restoration increasingly relies on artificial reefs (ARs) as critical tools for enhancing biodiversity and sustaining fishery resources. While ARs generate ecologically beneficial hydrodynamic features through upwelling and wake regions, the resulting near-seabed flow patterns can induce sediment scouring that compromises structural integrity and ecological functionality. Here, a systematic investigation of triangular ARs is presented. The hydrodynamic and ecological performance of these ARs (characterized by upwelling, wake region, and sediment scouring) is governed by three interdependent structural parameters: base angle (alpha), height (h), and length (l). Through three-dimensional computational fluid dynamics (CFD) simulations performed using openfoam, combined with analysis of existing experimental scour data, we quantify the relationships between these structural parameters and three critical performance indices: upwelling index (I-u), wake index (I-w), and scour index (I-s). Generalized Linear Model (GLM) analysis reveals that alpha exerts dominant control over both I-u and I-s, while h demonstrates limited influence on these indices. By developing a comprehensive performance index and employing Kriging interpolation with Bayesian optimization, we identify an optimal triangular AR configuration (alpha = 30.3 deg, h = 9.7 cm, and l = 33.4 cm) that maximizes ecological benefits while minimizing scour effects. Our findings establish a quantitative framework for AR design optimization, advancing the development of sustainable marine infrastructure.
The L-shaped caisson has been extensively utilized in marine infrastructure projects, particularly for deep-water ports and artificial islands. Numerical studies were conducted in this article to investigate the deformation and stability of the L-shaped caisson quay wall on sandy soil under live load. The accuracy of the numerical model was validated by the laboratory model tests in the literature. Parametric studies were conducted to examine the effects of caisson dimensions, live loads, and backfill soil properties on the stability of the L-shaped caisson quay walls. It is found that the heel length of the L-shaped caisson, the loading distance and width of the loading plate, and the effective friction angles of the soil heavily influence the stability and deformation of the L-shaped caisson quay wall. The horizontal displacements decreased by 49% with increasing heel length and by 92% with increasing loading distance. Four failure modes can be categorized based on the shapes and numbers of failure surfaces generated in the sand backfill. A method for determining failure modes was developed based on a statistical analysis of 130 numerical simulation cases. The predictive accuracy of the proposed classification criteria was verified to be 94% for the failure Mode I, 77% for Mode II, and 82% for Mode III.
Structural health monitoring of offshore jacket platforms is crucial to ensure the safety of offshore oil and gas development. At this stage, the judgment of platform structural safety state based on monitoring data mainly focuses on deterministic prediction, which often neglects the uncertainty and trend of safety state changes. So, a state detection model for offshore jacket platforms based on signal trend feature extraction is proposed in this article. First, the variational modal decomposition, along with Harris hawk optimization, was combined in this model, which was used to decompose the initial data into an intrinsic mode function (IMF) with clearer trends. Subsequently, the Holt-Winters algorithm is utilized to extract trend information from the historical data to predict the possible future changes of the IMF. Further, the Holt-Winters projection results under two different trend parameters are input into the regularized extreme learning machine to obtain the prediction intervals for the corresponding moments. Finally, a bi-objective optimization model with identification accuracy and interval width as the dual objectives is constructed to determine the reasonable interval of the platform's state change during operation, in order to monitor its safety state. The analysis results show that the proposed method achieves excellent recognition accuracy while the interval width is greatly reduced, which significantly improves the credibility of the model and can provide theoretical support for the state detection of offshore jacket platforms.
The present study proposes an approach to investigate the short-term distribution and variability of fish cage responses under irregular wave conditions. The finite element method-based software package aquasim is used to simulate the hydroelastic response of a gravity-type fish cage. A set of irregular wave series is generated by the Pierson-Moskowitz wave spectrum, and the response cycle samples are extracted using a level up-crossing technique. Four candidate probabilistic distribution models were examined and statistically tested for their goodness-of-fit to the response samples, and the variability of the maximum response under given stationary sea states was quantitatively analyzed. The target responses determined by two types of equivalent wave design methods were also compared and evaluated against the short-term maximum response values under different design safety levels. The results indicate that the generalized extreme value distribution is suitable for fitting most mooring force response samples, while the Weibull distribution is better for describing the stress response and vertical displacement response of the floating collar in most cases. Compared to the design method based on regular waves, the maximum response of fish cages under irregular waves exhibits significant uncertainty, highlighting the need to consider short-term variability. These findings provide helpful references for the structural strength and safety design of fish cages.
Through-wall pitting is the most common failure mode of concern for high-pressure pipelines as used in the oil and gas industry, both offshore and onshore, potentially allowing loss of containment and environmental pollution. Pipe burst under pressure is also of concern, particularly in high-safety class pipelines. Semiempirical models for predicting pipeline burst capacity vary in their fidelity. Mostly, this has been estimated by benchmarking burst capacity prediction models of undefined conservatism against burst pressure derived from finite element analysis (FEA) of steel pipes with wall defects, such as those caused by corrosion. The most recent of these comparative assessments is reviewed herein, and areas of concern are noted. This is followed by a statistical analysis for performance assessment of several burst capacity models, by comparing their predictions against FEA-generated data, for which both the modeling and the input data appear to have been correctly applied. The results of the analyses allow a more accurate ranking of burst capacity models, considering both predicted mean values and estimates of variability, and provide a basis for making logically consistent comparisons. They also form a basis for the application of safety factors to provide measures of the relative probability of pressure pipe bursts.
Square piles are widely utilized in coastal engineering due to their economic efficiency and robustness in resisting large forces in the coastal environment. However, the removal of sediment particles due to the approaching flow around such structures, known as scour, raises concerns about the stability and safety of the structure. Therefore, this study investigates scour around square piles placed at 45 deg and 90 deg angles in wave-current flows. A newly developed sediment transport module within the open-source REEF3D framework is developed, incorporating a three-phase semicoupled approach with level-set method (LSM) for realistic representation of sediment bed and free surface interfaces. The developed model is first validated against experimental results of circular and square pile scour in different flow conditions, such as steady current, wave-only, and wave-current flows. Furthermore, the effect of the combined wave-current parameter (U-cw) and Keulegan-Carpenter (KC) number on the normalized equilibrium scour depth (S/D-w) is explored. This study provides new insights into how square pile orientation modifies bed topography and equilibrium scour depth in wave-current flows. Numerical results demonstrate that a higher S/D-w value was observed for larger U-cw and KC numbers for both piles. It is revealed that in wave-only and combined wave-current flows with low KC numbers (KC < 10), square piles oriented at 45 deg experience greater scour depths than those oriented at 90 deg. However, at a higher KC number (KC = 18), square piles oriented at 90 deg exhibit greater scour depths compared to those at 45 deg.
Accurate in situ wave measurement is crucial for the safe and efficient operation of offshore floating platforms. However, the presence of a platform significantly perturbs the local wave field through complex wave-structure interactions, including wave diffraction and radiation, making direct measurement of the undisturbed incident waves a significant challenge. The relationship between the platform's hydrodynamic responses (air-gap and 6-DOF motion responses) and the incident wave field constitutes a complex, nonlinear inverse problem. Traditional linear methods often struggle with the strong nonlinearities inherent in this relationship, especially under severe sea states. This article proposes an improved methodology for decoupling undisturbed incident waves from near-field measurements based on a dual-frequency convolutional neural network (CNN). By separating the hydrodynamic responses into wave-frequency and high-frequency components and training the network with distinct datasets (irregular waves and white-noise waves) for each, our approach significantly enhances the model's generalization capabilities and accuracy. The proposed dual-frequency CNN effectively reconstructs the incident wave time series, with the standard deviation of the wave-frequency components achieving accuracies within 3% of the ground truth. Furthermore, the model demonstrates robustness against measurement noise, highlighting its potential for practical deployment on operational floating platforms for near-field wave sensing.
Offshore fish farms have been developed for providing large quantities of improved-quality aquaculture products. Critical components such as nets/tendons of the fish cages’ net systems in offshore farms may become damaged due to severe environmental conditions. Fish escape through the damaged nets with dire economic/biological consequences. Thus, early detection of these damages is important. Currently, structural health monitoring (SHM) in the net systems is costly, time-consuming, and sporadic as it is conducted via divers and remote operating vehicles. SHM in the tendons can also be achieved by checking the force signals acquired via load sensors, with only failures such as broken tendons successfully detected and not incipient damages such as degradation. The present case study investigates the detection of a single damaged vertical tendon in a cage’s net system via an automated vibration-based SHM method. The method’s novelty lies in integrating vector autoregressive models identified based on simulated displacement data from two spatial measurement points on the fish cage under changing wave and current conditions, thus allowing the accurate detection of degraded tendons and enabling a remote, continuous, cost-effective monitoring with a constant stream of integrity data. Test cases for the healthy and damaged cage are examined, with degradation (fatigue damage) considered along the whole tendon and at specific points in the tendon. The degradation is simulated by stiffness reduction, and the method successfully detects all 184 test cases for the damaged cage and 34 of 36 test cases for the healthy cage.
Bragg scattering due to multiple asymmetric trenches in a two-layer fluid is explained in a framework of linear two-dimensional theory. Both fluids have finite depths. The upper fluid is confined by the free surface at the top, while the lower fluid is bounded below by an impermeable trench bottom. The fluid region is divided into subregions associated with the trench position. The solution of the boundary-value problem is analyzed by the matched eigenfunction method of water wave potential. The phase velocity and group velocity for both wave modes are derived from dispersion relations and analyzed graphically. By solving the boundary-value problem, the upper surface elevation, interface elevation, reflection, and transmission coefficients in surface and internal modes are obtained numerically. These results indicate that as the width and depth of the trenches increase, wave transmission diminishes, and the free surface elevation reduces as the incident wave passes over the trenches. The present model has significant importance to protect harbors and offshore structures by mitigating wave height displacement and transmission coefficients.
Hybrid energy systems are investigated as a viable alternative to conventional energy systems typically consisting of multiple diesel engines, on ice-class vessels. Vessels bearing ice-class notation experience significant disparities in load profiles between typical voyages and those involving occasional icy conditions. Ice-class rules mandate considerably higher installed power than necessary for regular service, which can reduce the average operating efficiency of a conventional energy system. This article demonstrates the performance of two hybrid energy system configurations on a 50-m research vessel operating mainly in the Baltic Sea with ice-class 1A. Ship resistance is calculated in open water and ice and characteristic time-based load profiles are generated for a selected route over three voyage conditions. The peak propulsion loads, even in mild winter conditions with ice, are observed to be over four times as high as open water operation in summer. Various hybrid energy system configurations, including diesel gensets, fuel cells, and batteries, are analyzed over three voyage conditions. Results indicate that a fuel cell-based energy system can yield up to 24.2% energy savings. Upto 13.7% energy savings can be achieved by modularizing the onboard genset installation, while battery hybridization enables optimal energy system operation over a wider range of voyages. Finally, it is shown that ice-class-capable energy system can perform as efficiently as a comparable downsized energy system when the genset installation is modularized and even enable further fuel savings in the presence of fuel cells.
This article presents a probabilistic Monte Carlo simulation workflow that integrates sea ice dynamics, seastate conditions, and vessel operability to estimate weather windows for Arctic offshore drilling. The analysis also demonstrates how estimates of the weather window can be used to develop probabilistic cost and schedule estimates for drilling activities. Using 12 years of sea ice observations and 29 years of wave data for a Chukchi Sea drilling site, a first-order, time-inhomogeneous Markov chain is specified to estimate week-to-week site availability under open-water/sea-ice-free conditions (<10% ice concentration). Drilling rig operability is estimated based on joint wave direction-height distributions, drilling vessel flex-joint angle limits, and operator response (rig rotation) when operating limits are exceeded. A Monte Carlo simulation yields a probability distribution of the duration of the July-November operating season. When integrated with drilling activity cost and schedule information, the framework provides a quantitative foundation to develop risk-based cost and schedule estimates to support project planning and decision-making at various levels, from portfolio management and asset evaluation to detailed execution planning.
In deepwater riserless drilling, the effects of the drillship's motion and ocean currents on the drill string create complex dynamics that remain poorly understood. Previous studies revealed self-excited vibration caused by the friction of the heave compensator and forced vibration in the torsional direction due to heave. Thus, in deepwater drilling, new phenomena are likely to emerge in the drill string's dynamic behavior. This study focused on the lateral dynamics of the drill string in deepwater drilling. This study reviewed previous studies to determine the conditions for backward whirls, conducted numerical and small-scale model experiments, and investigated the effect of ocean currents. Consequently, the pipe clearance and friction had a significant effect, and by reflecting this in the small-scale model experimental conditions, a backward whirl was reproduced. Then, a coupling between the backward whirl and bending due to the Magnus effect was observed by towing the experimental device. Next, the results of numerical simulations and small-scale model experiments were compared, and some agreement was observed. In addition, numerical simulations showed coupled phenomena, such as an increase in the radius of the backward whirl due to the Magnus effect. Finally, the lateral dynamics of riserless drilling at a water depth of 7000 m were examined using numerical simulation. As a result, under this study's calculation conditions, the influence of lateral dynamics was small in deepwater drilling at 7000 m. This study will contribute to understanding the dynamics of the drill string in riserless drilling.
Floating wind turbines are core equipment for developing deep-sea wind energy resources. Accurately predicting their six degrees-of-freedom (6DOF) motion response is crucial for load fluctuation control and proactive structural health monitoring. This study proposes an Optuna-based hyperparameter optimization framework for the NLinear model (OP-NLinear) to perform multi-step prediction of the motion response for a 15 MW deep-sea floating wind turbine. Twelve typical operating conditions are selected to construct a 6DOF motion response dataset. The NLinear model is employed to extract trends and patterns from the motion response time series, while Optuna enhanced its generalization capability by optimizing model hyperparameters. Results demonstrate that when predicting the 6DOF motion response over the next 15 time-steps (15 s), OP-NLinear outperforms benchmark models such as OP-Bi-LSTM, OP-DLinear, and OP-XGBoost across all operating conditions, achieving an average coefficient of determination (R-2) of 0.922. Even when predicting motion responses over 20 time-steps, the average R-2 value remained at 0.895. Furthermore, the model exhibits strong noise immunity, demonstrating "intelligent robustness" against 20 dB colored noise. The proposed OP-NLinear method provides a reliable and efficient solution for predicting the 6DOF motion response of deep-sea floating wind turbines, laying the foundation for developing related active control strategies.
In this study, the influence of the hydrodynamic damping models on the floater dynamics of a semisubmersible floating offshore wind turbine (FOWT) was investigated numerically and experimentally. Semisubmersible FOWT substructures typically consist of pontoons and vertical columns. These submerged components exhibit dynamic behavior under the combined influence of waves and currents. The drag force induced by the structure's shape and the damping force caused by fluid viscosity interact in a complex manner, influencing the floater dynamics. During the early design stage, the platform's response characteristics are typically evaluated through integrated load analysis. To better approximate the dynamic behavior observed in the actual structure, it is important to construct a suitable damping model that can represent the real damping characteristics of the system. In this study, a damping model was constructed by integrating results from computational fluid dynamics (CFD) simulations and 1/36-scale model experiments. The linear and quadratic damping coefficients were determined through the results of free decay experiments conducted in still water. The nonlinear Morison drag coefficient was obtained from CFD uniform flow simulation. The characteristics of three models-linear damping, linear-plus-quadratic damping, and Morison drag-were compared, and a method for combining them was proposed. The validity of the combined damping model was confirmed by applying it to the floating substructure.
Accurate modeling of ship roll dynamics under varying wave excitations remains challenging due to nonlinear system behavior and measurement noise. This study presents a physics-informed neural network (PINN) for high-fidelity identification of roll motion parameters with physical interpretability. By leveraging automatic differentiation, the proposed PINN avoids the numerical instability typically encountered with discrete differentiation. Training data are generated using the Fermat polynomial method (FPM). Extensive simulations under varying noise levels (2-5%) reveal PINN's superior robustness compared to support vector regression (SVR), achieving 45-48% lower root mean square error (RMSE) in roll trajectory prediction with consistently narrower uncertainty bands across both regular and stochastic wave excitations. To enhance learning efficiency for periodic motion, a novel Snake activation function is incorporated within the PINN architecture. Performance is validated using frozen cargo experimental measurements from full-scale sea trials, with 70% of the data used for training and 30% reserved for independent validation, demonstrating 47% lower RMSE compared to SVR. The results highlight the proposed PINN's capability to accurately and robustly predict ship roll dynamics under diverse excitation conditions.
This research article examines the intracycle performance of the S-shaped vertical-axis autorotation current turbine (VAACT). The investigation is performed by simulations using a two-dimensional computational fluid dynamics (CFD) model. The verification and validation (V&V) processes are as recommended practices from the International Towing Tank Conference (ITTC) 2023. The verification process includes checking the domain, grid, and time-step, leading to a convergent result with sufficient grid elements. The validation process uses comparison with the model results performed at the current channel of the Laboratory of Waves and Currents (LOC-COPPE/UFRJ). The test facility presents 22 m in length, 1.4 m in width, and a maximum flow velocity of about 0.50 m/s at 0.5 m water depth. In the present case, the investigation focuses on VAACT, a highly efficient S-shaped profile. In the CFD environment, the power take-off (PTO) system is modeled by adding an external torque due to the PTO system and the damping coefficient (Kt) into a user-defined function (UDF). The validation is performed at Reynolds numbers ranging from 70,000 to 135,000. The time series analysis indicates that lift (crossline) and drag (inline) forces are crucial to the turbine's performance. The intracycle analysis shows that although the instantaneous efficiency is in phase with the drag force, the lift drives the device when the drag achieves smaller magnitudes close to 0- to 30-deg encounter angle. Additionally, the torque coefficient is in phase with the lift force, which implies that it can govern hydrodynamic performance. The analysis also reveals that the returning blade adds a resisting torque on the turbine, which impacts its performance, as represented by high-pressure points on this blade. The torque coefficient drops by 38% from 45 deg to 90 deg because of this resisting effect.
Analysis of the operability under environmental uncertainty is important for on-board decision-making and will impact the safety and economy of complex marine operations. To this end, this study examines the operability of a crane barge during the vibratory extraction of a monopile foundation. A numerical model has been developed to couple the dynamic behavior of the monopile with that of the crane barge during vibratory monopile extraction. The model incorporates the centrifugal force generated by the vibro-hammer and the reduced soil resistance caused by cyclic loading, implemented via the external dynamic link library. This model is used in the present study to conduct operability analysis. The dynamic response of the coupled system is analyzed by investigating the effects of modeling the vibro-hammer mass as either a distributed mass or a lumped mass. An extreme value analysis is conducted to evaluate the operability of the crane barge and establish the allowable sea states for safe operation. The findings reveal that the modeled crane barge exhibits low operability in the examined zone, primarily due to its limited freeboard, large weight of the monopile and vibro-hammer, and the harsh environmental conditions of the considered site.
The contribution is concerned with the influence of bristle-blasting on texture, chemical status, and corrosion of aluminum (AA6082) substrates in synthetic and real seawater. The performance of an offshore epoxy coating, applied onto the substrates, is also investigated. Bristle-blasting generated substrates with a low level of contamination and with a rather regular surface texture. It complied with requirements for offshore use in terms of substrate roughness and coating adhesion. The filiform corrosion tests delivered useful results. The cyclic corrosion test duration (3000 h), however, was too short, and the degradation was negligible. The coating performance was also evaluated during two years in real marine outdoor exposure in the splash zone and the tidal zone in a North Sea location (Helgoland); the results were compared with the laboratory test results. For the first time, acceleration factors were calculated for the filiform laboratory test. The test generated very high acceleration factors, which depended on the location of the specimens during outdoor site testing.
Offshore platform pipeline leakage detection faces severe challenges from complex marine environments, where intense environmental noise interference and complex signal characteristics make traditional methods difficult to achieve accurate leakage valve localization. To address this technical challenge, this study proposes an offshore platform pipeline leakage valve localization method based on dynamic time warping distance-based complete ensemble empirical mode decomposition with adaptive noise (DCEEMDAN) and adaptive temporal-spatial fusion network (ATSFN). First, by introducing dynamic time warping distance similarity measurement into the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) framework and combining probability density function feature extraction, adaptive denoising of acoustic emission signals in marine environments is achieved. Second, a temporal-spatial feature extraction architecture with a parallel multiscale convolutional neural network (CNN) and a hierarchical GRU is designed, realizing deep fusion of CNN spatial features and GRU temporal features through a cross-attention mechanism. Finally, an end-to-end intelligent monitoring system is constructed, achieving high-precision localization of 10 valve positions through dual-stage verification combining laboratory experiments and offshore platform field measurements. Experimental results show that DCEEMDAN outperforms traditional EMD series algorithms, achieving a signal-to-noise ratio (SNR) of 19.69 dB with 16.6% improvement over CEEMDAN. ATSFN achieves average localization accuracies of 94.38% and 95.75% under 4 MPa and 5 MPa conditions, respectively, representing improvements of 10.9% and 10.77% over best baseline models. Under extreme noise conditions, the model maintains localization accuracy above 82.3%, demonstrating excellent noise robustness. This research provides an effective technical solution for offshore platform pipeline leakage detection.
This study proposes an integrated approach for predicting the crack driving force in bimetallic welded pipes by combining the finite element method (FEM) and machine learning. The bimetallic pipes are connected by welding, often leading to flaws and misalignment, which present significant challenges to their integrity. In this work, a study was conducted on the factors affecting the crack tip opening displacement (CTOD) of geometrically mismatched composite pipes with surface cracks. A numerical framework for canoe-shaped surface cracks was developed. After verifying the accuracy of the method, a reasonable parameter space was designed, and axial tensile finite element simulations were performed under high-strain conditions. The linear coefficients and c2 of the linear segment of the CTOD-epsilon g curve were used as output variables. Different machine learning models were employed for training, and the impact of each feature on crack response was analyzed based on permutation importance. The multilayer perceptron (MLP) model showed the highest prediction accuracy, and the crack depth ratio was consistently identified as the most influential feature. By combining the finite element analysis with machine learning models, the crack driving force for canoe-shaped surface cracks in oil and gas pipelines entering the plastic deformation stage can be predicted more accurately and quickly. This approach strikes a balance between accuracy and overly conservative tactics, providing reliable technical support for pipeline design and safety assessments.
