Showing papers in "Ocean Engineering in 2021"

Abstract: Considering heterogeneities and difficulties in the classification of underwater passive targets, this paper proposes the use of Local Wavelet Acoustic Pattern (LWAP) and Multi-Layer Perceptron (MLP) neural networks to design a real-time and accurate underwater targets classifier. To train the MLP classifier, first, the Whale Optimization Algorithm (WOA) is improved and then applied to optimize the parameters of the designed classifier. For this purpose, different mathematical functions are employed for improving the exploitation and inspection capacity of the modified Whale Optimization Algorithm (mWOA). To evaluate the functioning of the proposed optimization algorithm and designed classifier, 23 benchmark test functions are used and an experimental underwater passive dataset is developed, respectively. To assess the accuracy of the classification, the speed of the convergence, and entrapment in local minima, the findings are compared with the results of five newly proposed meta-heuristic algorithms Biogeography-based Optimizer (BBO), Gray Wolf Optimizer (GWO), Salp Swarm Algorithm (SSA), Group Method of Data Handling (GMDH), and Harris Hawks Optimization (HHO), as well as classic WOA. The findings show that the modified optimizer and the designed classifier using mWOA significantly outperform the other benchmark classifiers.

Abstract: The flow-induced vibration (FIV) of two cylindrical structures may cause serious fatigue damage, which is a major concern in engineering fields. Few studies have focused on the FIV hydrodynamic characteristics of two staggered flexible cylinders. In this paper, the hydrodynamic coefficients of two staggered flexible cylinders are calculated using the structural responses acquired from the model tests. The cross-flow (CF) spacing ratios are set as 2, 3, 4 and 6, and the in-line (IL) spacing ratios are set as 4, 6, 8; thus, a total of twelve cases are considered. The fluctuating forces in the CF and IL directions are reconstructed using a finite element model. The forgetting factor least squares method is employed to decompose the CF/IL fluctuating force into lift/varying drag and CF/IL added mass force. In the CF direction, the hydrodynamic coefficients of the two staggered cylinders are significantly different from those of the single cylinder in the mode switch regions. The CF vibrations of the two staggered cylinders enter the higher-order mode earlier, resulting in an increase in the CF added mass coefficients. The downstream cylinder has larger lift coefficients due to the vortex shedding effect of the upstream cylinders. In the IL direction, the upstream cylinder has lower IL added mass coefficients due to the higher-order harmonics in the IL direction. The varying drag coefficients of the two staggered cylinders significantly differ from those of the single cylinder owing to the subharmonic frequency components. A hybrid aspect of wake-induced flutter and vortex-induced vibration is observed in the vibration of the downstream cylinder.

Abstract: The recently developed FSI-SPH model (Sun et al., 2019c), by combining the multi-resolution δ + -SPH scheme and a Total Lagrangian SPH method, is further extended for more complex three-dimensional (3D) Fluid Structure Interaction (FSI) problems. The FSI-SPH model is strengthened with advanced numerical techniques, in which a combination of the Particle Shifting Technique (PST) and the Tensile Instability Control (TIC) is adopted to prevent flow voids induced by the tensile instability. The Adaptive Particle Refinement (APR) is used to refine particles in the boundary layer region and coarsen particles in the far-field to increase local accuracy but reduce overall computational cost. Moreover, the δ + -SPH and Total Lagrangian SPH solvers are coupled through a Modified Sequential Staggered (MSS) algorithm which, on one hand, ensures the numerical accuracy and stability and, on the other hand, improves the efficiency when magnitudes of time steps between the two solvers differ from each other significantly. In the numerical results, challenging 2D and 3D FSI cases are simulated to test the accuracy of the proposed FSI-SPH model. A new FSI benchmark with free-surface is proposed to highlight the advantage of this FSI-SPH model in simulating free-surface viscous flows. In addition, 3D effects in the FSI dam-breaking and sloshing cases are investigated.

Abstract: A SPH (Smoothed Particle Hydrodynamics)-based multi-resolution fully Lagrangian meshfree hydroelastic FSI (Fluid-Structure Interaction) solver is developed for accurate and adaptive reproductions of ocean engineering problems The presented hydroelastic FSI solver comprises of projection-based ISPH (Incompressible SPH) fluid model and SPH structure model, through consideration of continuity/Navier-Stokes equations for fluid phase as well as linear/angular momentum conservation equations for structure phase The FSI solver includes a consistent fluid-structure coupling scheme along with a novel multi-resolution scheme incorporating a common influence length, a modified SPH density definition and a SPH-based formulated SPP (Space Potential Particle) scheme in order to achieve consistent particle-based discretizations, precise satisfaction of fluid-structure interface boundary conditions and accurate volume conservation at the interface The present ISPH fluid model corresponds to a refined version of ISPH incorporating several previously developed refined schemes, and hence the proposed FSI solver is referred to as “Enhanced Multi-resolution ISPH-SPH” Validations are performed qualitatively/quantitatively through reproductions of classical as well as ocean engineering benchmark tests Comparisons are also made with an MPS-based FSI solver as well as an Explicit ISPH-based one A preliminary extension of the proposed solver to three-dimensions is also presented

Abstract: Predicting the likelihood of maritime accidents is hindered by the relative sparsity of collisions on which to develop risk models. Therefore, significant research has investigated the capability of non-accident situations, near misses and encounters between vessels as a surrogate indicator of collision risk. Whilst many studies have developed ship domain concepts, few have considered the practical considerations of implementing this method to characterise navigational risk between waterways and scenarios. In order to address this, within this paper we implement and evaluate the capability and validity of domain analysis to characterise and predict the likelihood of ship collisions. Our results suggest that the strength of the relationship between collisions and encounters is varied both between vessel types and the spatial scale of assessment. In addition, we demonstrate some key practical considerations in utilising domain analysis to predict the change in collision risk, through a hypothetical wind farm. The outcomes of this study provide research direction for practical applications of domain analysis on collision risk assessments.

Abstract: Mechanisms governing the sand plug behavior inside an open-ended pile are examined using the discrete element method. A series of numerical pile penetration tests have been conducted by considering the influence of soil density, pile geometry and installation method. A novel sample generation method, based on the replication of unit cell, is applied to produce a large and homogeneous sample efficiently. According to the soil deformation pattern, a “nose cone”, with the length about one pile diameter, has been observed beneath the small diameter jacked pile at the end of penetration. Plugging effect is shown to be more prevalent for jacked piles than dynamic installed piles. Also, larger penetration, smaller diameter and higher soil density all seem to promote plug formation, while the influence of wall thickness is not that obvious. This conclusion is later verified by the development of Incremental Filling Ratio (IFR) and porosity distribution. Furthermore, remarkable stress concentration has been observed at the lower part of the soil plug. The development of installation resistance indicates that jacking produces the largest resistance while dynamic installation methods ease pile penetration. Further analysis based on particle movements, contact force chain distribution and stress orientation provides a micromechanical perspective of the plug behavior. Finally, the plug resistance mobilization process at different plugging conditions and the formation process for soil plug are illustrated.

Abstract: This paper investigates the adaptive prescribed performance trajectory tracking control problem for underactuated underwater vehicles subjected to unmodeled hydrodynamics, ocean disturbances and input quantization. The controller is synchronized through the command filter-based backstepping design and minimum learning parameter algorithm, thus the adverse effect of “explosion of complexity” and computational complexity inherent in neural network is avoided. To endow tracking errors with prescribed performance guarantees, a mapping function is applied such that the constrained control problem could be transformed to the unconstrained one. By resorting to the hysteresis quantizer, the frequency of data transmission is considerably reduced and the quantization errors are effectively reduced under the proposed control scheme. Through Lyapunov stability analysis, it is verified that the proposed method is capable of ensuring asymptotic stability for tracking errors. Numerical simulation results reveal the advantage and effectiveness of this work.

Abstract: Decision-making in emergency situations is a risky and uncertain process due to the limited information and lack of time. Some key problem parameters, such as the time required to complete important response tasks, must be estimated and are therefore prone to errors. Other parameters, such as the probability of occurrence of a consequential event, will typically change as the response operation progresses. As a result, there should be a dynamic probabilistic risk assessment framework to assess the risk level of decision scenarios and facilitate the decision-making process. In this paper, a methodology for dynamic probabilistic risk assessment of decision making in emergencies for complex marine systems is proposed. In this method, a dynamic event sequence diagram is introduced that helps to quantify events probabilities as a function of time, as well as environmental and operational variables, considering events interdependencies and uncertainties. In addition, the effects of time required 1 and time available 2 for performing a decision in emergency are considered in the risk model. In this methodology, probabilistic models including Bayesian network and Monte Carlo simulation are utilized to quantify the uncertain behavior of the decision-making process in complex marine systems. A computational study is also conducted to evaluate the methodology performance, in terms of effectiveness and efficiency. Computational results show that the proposed approach can obtain optimal solutions for large and practical problem sizes.

Abstract: In this paper we investigate the dynamics of the inertial sea wave energy converter (ISWEC) device using fully-resolved computational fluid dynamics (CFD) simulations. Originally prototyped by the Polytechnic University of Turin, the device consists of a floating, boat-shaped hull that is slack-moored to the sea bed. Internally, a gyroscopic power take-off (PTO) unit converts the wave-induced pitch motion of the hull into electrical energy. The CFD model is based on the incompressible Navier–Stokes equations and utilizes the fictitious domain Brinkman penalization (FD/BP) technique to couple the device physics and water wave dynamics. A numerical wave tank is used to generate both regular waves based on fifth-order Stokes theory and irregular waves based on the JONSWAP spectrum to emulate realistic sea operating conditions. A Froude scaling analysis is performed to enable two- and three-dimensional simulations for a scaled-down (1:20) ISWEC model. It is demonstrated that the scaled-down 2D model is sufficient to accurately simulate the hull’s pitching motion and to predict the power generation capability of the converter. A systematic parameter study of the ISWEC is conducted, and its optimal performance in terms of power generation is determined based on the hull and gyroscope control parameters. It is demonstrated that the device achieves peak performance when the gyroscope specifications are chosen based on the reactive control theory. It is shown that a proportional control of the PTO control torque is required to generate continuous gyroscopic precession effects, without which the device generates no power. In an inertial reference frame, it is demonstrated that the yaw and pitch torques acting on the hull are of the same order of magnitude, informing future design investigations of the ISWEC technology. Further, an energy transfer pathway from the water waves to the hull, the hull to the gyroscope, and the gyroscope to the PTO unit is analytically described and numerically verified. Additional parametric analysis demonstrates that a hull length to wavelength ratio between one-half and one-third yields high conversion efficiency (ratio of power absorbed by the PTO unit to wave power per unit crest width). Finally, device protection during inclement weather conditions is emulated by gradually reducing the gyroscope flywheel speed to zero, and the resulting dynamics are investigated.

Abstract: Data-driven models for ship propulsion are presented while the effect of data pre-processing techniques is extensively examined In this study, a large, automatically collected with high sampling frequency data set is exploited for training models that estimate the required shaft power or main engine fuel consumption of a container ship sailing under arbitrary conditions Emphasis is given to the statistical evaluation and pre-processing of the data and two algorithms are presented for this scope Additionally, state-of-the-art techniques for training and optimizing Feed-Forward Neural Networks (FNNs) are applied The results indicate that with a delicate filtering and preparation stage it is possible to significantly increase the model's accuracy Therefore, increase the prediction ability and awareness regarding the ship's hull and propeller actual condition Furthermore, such models could be employed in studies targeting at the improvement of ship's operational energy efficiency

Abstract: This study aims at portraying the vertical vibration characteristics of an end bearing pile interacting with the saturated soil that is of distinctly inhomogeneous behavior in the radial direction due to the construction disturbance. The pile is considered a one-dimensional bar. The saturated soil is considered by the Biot's two-phase linear theory and its radial inhomogeneity is simulated by the gradual variation of soil properties radially in a linear way. In practical terms, it is partitioned into a number of vertical ring-shaped zones in the radial direction with the soil properties approximately remain the same within the same soil zone. The pile–soil dynamic interaction is then obtained by solving the governing equations of each soil zone and is substituted into the governing equation of the pile to derive the analytical solution for the dynamic impedance at the pile head. The present solution is validated by comparing with other solutions. Meanwhile, it is employed to investigate the effect of the construction disturbance on the vertical vibration of the pile for different pile–soil parameters.

Abstract: The accurate and real-time detection of moving ships has become an essential component in maritime video surveillance, leading to enhanced traffic safety and security. With the rapid development of artificial intelligence, it becomes feasible to develop intelligent techniques to promote ship detection results in maritime applications. In this work, we propose to develop an enhanced convolutional neural network (CNN) to improve ship detection under different weather conditions. To be specific, the learning and representation capacities of our network are promoted by redesigning the sizes of anchor boxes, predicting the localization uncertainties of bounding boxes, introducing the soft non-maximum suppression, and reconstructing a mixed loss function. In addition, a flexible data augmentation strategy with generating synthetically-degraded images is presented to enlarge the volume and diversity of original dataset to train learning-based ship detection methods. This strategy is capable of making our CNN-based detection results more reliable and robust under adverse weather conditions, e.g., rain, haze, and low illumination. Experimental results under different monitoring conditions demonstrate that our method significantly outperforms other competing methods (e.g., SSD, Faster R-CNN, YOLOv2 and YOLOv3) in terms of detection accuracy, robustness and efficiency. The ship detection results under poor imaging conditions have also been implemented to demonstrate the superior performance of our learning method.

Abstract: Based on linear potential flow theory and the matching eigenfunction expansion method, an analytical model is developed to investigate the hydrodynamics of two-dimensional multi-pontoon floating breakwaters that also work as oscillating buoy wave energy converters. The pontoons are constrained to independent heave motion and the linear power take-off damping is used to calculate the absorbed power. The proposed model is verified using the principle of energy conservation. Performance of the system with different numbers of pontoons is studied, and the results show that the wave attenuation performanceper, the energy capture performance, and the effective frequency bandwidth are superior in the multi-pontoon system as compared to the single-pontoon systems with the same volume. Bragg resonance dominates the wave energy extraction performance at certain frequencies, which reduces the hydrodynamic efficiency. Conversely, in the frequency region away from Bragg resonance, the hydrodynamic efficiency is enhanced due to the constructive hydrodynamic interactions of the multi-body system.

Abstract: To effectively improve system autonomy, increase fault-tolerant resilience, solve low payload capacity and short endurance time of unmanned surface vehicles (USVs), there's a trend to deploy multiple USVs as a formation fleet. The formation path planning algorithms are essential to generate optimal trajectories and provide practical collision avoidance maneuvers to efficiently navigate the USV fleet. To ensure the optimality, rationality and path continuity of the formation trajectories, this paper presents a novel deterministic algorithm named multiple sub-target artificial potential field (MTAPF) based on an improved APF. The MTAPF belongs to the local path planning algorithm, which refers to the global optimal path generated by an improved heuristic A* algorithm. and the optimal path is divided by this algorithm into multiple sub-target points to form sub-target point sequence. The MTAPF can greatly reduce the probability that USVs will fall into the local minimum and help USVs to get out of the local minimum by switching target points. As an underactuated system, the USV is restricted by various motion constraints, and the MTAPF is presented to make the generated path compliant with USV's dynamics and orientation restrictions. The proposed algorithm is validated on simulations and proven to work effectively in different environments.

Abstract: Submarine pipelines, as an important tool for oil and gas transportation, have been distributed in offshore oil and gas fields worldwide. Under extreme waves, a large number of submarine pipelines have been damaged in the past decades. In order to investigate the effect of extreme marine environments on the pipelines, the joint effect of solitary waves and background currents on the pipelines is numerically studied in this paper by using a numerical wave tank developed with a free surface tracking approach and the immersed boundary method. The sediment transport module including packed and suspended sediment is incorporated with the flow module. In order to ensure the calculation accuracy of this model, three verification cases related to the wave propagation profile, the hydrodynamic force on the cylinder and the scour hole profile are simulated and the numerical results match the experimental and analytical results well. Given the combined wave exerts on the different pipelines, the environmental variables consider the background current velocity and wave height, and the pipeline arrangement includes the different diameters and the suspended pipeline and tandem pipeline. It is noted that the hydrodynamic characteristics, the forces and the local scour around the pipeline are closely related to the background current and the diameter and layout of the pipeline. It is anticipated that the findings in this paper will enhance our understanding of the damage mechanism of submarine pipeline by waves and may also be useful in future design practices for pipelines.

Abstract: Collision accidents may lead to significant asset damage and human casualties. This paper introduces a direct analysis methodology that makes use of Automatic Identification System (AIS) data to estimate collision probability and generate scenarios for use in ship damage stability assessment. Potential collision scenarios are detected from AIS data by an avoidance behaviour-based collision detection model (ABCD-M) and the probability of collision is estimated in various routes pertaining to a specific area of operation. Damage extents are idealised by the Super – Element (SE) method accounting for the influence of surrounding water in way of contact. Results are presented for a Ro - Pax ship operating from 2018 to 2019 in the Gulf of Finland. It is confirmed that collision probability is extremely diverse among voyages and the damages obtained correlate well with those adopted by the UN IMO Regulatory Instrument SOLAS (2020). It is concluded that the method is by nature sensitive to traffic features in the selected case study area. Yet, it is useful for the evaluation of flooding risk for ships operating in real hydro-meteorological conditions.

Abstract: A computational fluid dynamics (CFD) based Unsteady Reynolds Averaged Navier–Stokes and Volume of Fluid model solver is adopted to estimate ship motion responses in bi-directional cross waves. The characteristics of cross waves are analyzed theoretically and by CFD verification at first. Then ship nonlinear motion responses and green water on deck induced by cross waves are systematically analyzed by adopting a S175 containership model sailing in different cross wave conditions such as various wave lengths, wave heading angles and forward speeds. Finally, ship safe navigational strategy is explored and corresponding operational guidance is proposed to ensure the safety of ships when encountering cross waves.

Abstract: Microbiologically influenced corrosion (MIC) is a complex phenomenon that occurs when a microbial community is involved in the degradation of an asset (e.g. pipelines). It is widely recognized as a significant cause of hazardous hydrocarbon release and subsequently, fires, explosions, and economic and environmental impacts. This paper presents a new MIC management methodology. The proposed methodology assists in accurately monitoring MIC activity and accordingly develop strategies to manage it. The MIC monitoring and management activities are achieved using Continuous Bayesian Network (CBN) technique with Hierarchical Bayesian Analysis (HBA). The integration of HBA and CBN helps overcome the Bayesian network's discrete value limitations (BN) and source-to-source uncertainty for each node in the network. The methodology can provide the precise value of parameters, such as failure probability and MIC occurrence rate which are verified using observed data. The application of the methodology is demonstrated on a subsea pipeline. The study provides a better understanding of the influencing factors of MIC rate and failure probability. This assists in developing effective MIC management strategies.

Abstract: This paper proposes an efficient potential and viscous flow decomposition method for wave-structure interaction simulation with single-phase wave models and two-phase Computational Fluid Dynamics (CFD) solvers. The potential part - represents the incident waves - is solved with spectral wave models; the viscous part - represents the complementary perturbation on the incident waves - is solved with the CFD solver. The decomposition strategy is called Spectral Wave Explicit Navier-Stokes Equations (SWENSE), originally proposed for single-phase CFD solvers ( Ferrant et al., 2003). Firstly, this paper presents a new two-phase SWENS Equations with interface capturing technique. To achieve this single-phase and two-phase decomposition, the incident fields are extended in the air with a density-weighted pressure. Secondly, an accurate and efficient interpolation method is proposed to transfer High Order Spectral (HOS) wave model's result on CFD mesh, which reduces drastically the divergence error of the interpolated velocity. Implemented within OpenFOAM, these methods are tested by three verification, validation, and application cases, considering incident wave propagation, high-order loads on a vertical cylinder in regular waves, and a Catenary Anchor Leg Mooring buoy in both regular and irregular waves. Speed-ups between 1.7 and 4.2 are achieved. The wave models and the interpolation method are released open-source to the public.

Abstract: To achieve reliable mining results for massive vessel trajectories, one of the most important challenges is how to efficiently compute the similarities between different vessel trajectories. The computation of vessel trajectory similarity has recently attracted increasing attention in the maritime data mining research community. However, traditional shape- and warping-based methods often suffer from several drawbacks such as high computational cost and sensitivity to unwanted artifacts and non-uniform sampling rates, etc. To eliminate these drawbacks, we propose an unsupervised learning method which automatically extracts low-dimensional features through a convolutional auto-encoder (CAE). In particular, we first generate the informative trajectory images by remapping the raw vessel trajectories into two-dimensional matrices while maintaining the spatio-temporal properties. Based on the massive vessel trajectories collected, the CAE can learn the low-dimensional representations of informative trajectory images in an unsupervised manner. The trajectory similarity is finally equivalent to efficiently computing the similarities between the learned low-dimensional features, which strongly correlate with the raw vessel trajectories. Comprehensive experiments on realistic data sets have demonstrated that the proposed method largely outperforms traditional trajectory similarity computation methods in terms of efficiency and effectiveness. The high-quality trajectory clustering performance could also be guaranteed according to the CAE-based trajectory similarity computation results.

Abstract: Numerical simulation of fluid structure interaction (FSI) problems is one of the most challenging topics in computational fluid dynamics. In this paper, coupling edge-based smoothed finite element method (ES-FEM) and smoothed particle hydrodynamics (SPH) method (ES-FEM-SPH) is proposed for solving FSI problems, where the edge-based smoothed finite element method is used to model the movement and deformation of structures, and the smoothed particle hydrodynamics is used to model the fluid flow. In ES-FEM, the gradient smoothing technique is applied over the smoothing domain and it can effectively overcome the ‘‘overly-stiff’’ effect in conventional FEM model. Some correction algorithms including density correction, kernel gradient correction and particle shift technique are integrated into the SPH method to improve computational stability and accuracy. A virtual particle coupling scheme is used to implement the coupling of ES-FEM and SPH with complex geometry interface. As ES-FEM is more accurate than conventional FEM, and it is expected that this ES-FEM-SPH coupling approach should be superior than existing FEM-SPH coupling approaches. A number of test examples with FSI are investigated with the presented ES-FEM-SPH, and compared with results from other approaches including FEM-SPH. From the obtained numerical results, we can conclude that the ES-FEM-SPH coupling approach is effective to simulate FSI problems.

Abstract: Reliable predictions of oceanic waves during storms have always been foremost for offshore design and operation, coastal hazards, and navigation safety. Indeed, many accidents that occurred during storms were ascribed to the impact with unforeseen large waves. In this context, the purpose of this study is to improve the present state extreme wave estimate from spectral wave models. We describe an implementation for the WAM model, and we investigate the use of WAM and WAVEWATCH III fed with common routines designed to evaluate the short-term/range maximum wave statistics. An extensive assessment of models' results in the Adriatic and North Sea is performed using time and space-time wave measurements, and through an intercomparison between WAM and WAVEWATCH III applied with three different input/dissipation source term parametrizations (ST3/4/6). Further, models’ capabilities are investigated, and extreme waves characterized, in the Mediterranean Sea, aiming also at disentangling the wave spectrum bulk parameters that may point to favorable conditions for the generation of high waves. Based on the comparisons between model results and measurements, we conclude that for the model characterization of extremes, the accuracy of the significant wave height is pivotal; differences between models of other spectral parameters seem to have a minor effect.

Abstract: Vessel and wave hydrodynamics are fundamental for vessel motion prediction. Improving hydrodynamic model accuracy without compromising computational efficiency has always been of high interest for safe and cost-effective marine operations. With continuous development of sensor technology and computational capacity, an improved digital twin concept for vessel motion prediction can be realized based on an onboard online adaptive hydrodynamic model. This article proposes and demonstrates a practical approach for tuning of important vessel hydrodynamic model parameters based on simulated onboard sensor data of vessel motion response. The algorithm relies fundamentally on spectral analysis, probabilistic modelling and the discrete Bayesian updating formula. All case studies show promising and reasonable tuning results. Sensitivities of the approach with respect to its key parameters were also studied. Sensor noise has been considered. The algorithm is found to be computationally efficient, robust and stable when tuning the values of hydrodynamic parameters and updating their uncertainties, within reasonable sensor noise levels.

Abstract: COLREGs-based collision risk awareness model is urgently needed in real-time operating conditions. However, this is a complicated problem under various encounter situations, some of which are very complex. In order to quantify the collision risk in real operating conditions, a novel risk-informed collision risk awareness approach is proposed for real-time operating conditions. Firstly, the ship's actions are identified based on the Automatic Identification System (AIS) data. Secondly, the uncertainty of ship action patterns is analyzed by regarding the target ships as velocity obstacles. Then, the collision risk model is utilized to assess the collision risk level based on the uncertainty in the non-linear velocity obstacles algorithm considering responsibility. Finally, some case studies are carried out based on the proposed model. In the model, the dynamic and uncertainty features of the ship action dynamics in real operating conditions are considered, which could benefit on reducing ship collision accidents and improving the development of technologies on intelligent collision avoidance decision makings.

Abstract: Ocean renewable energy is a promising inexhaustible source of renewable energy, with an estimated harnessing potential of approximately 337 GW worldwide, which could re-shape the power generation mix. As with other sources of renewables, however, wave energy has an intermittent and irregular nature, which is a major concern for power system stability. Consequently, in order to integrate wave energy into power grids, it must be forecasted. This paper proposes using optimised deep learning neural networks to forecast the wave energy flux, and other wave parameters. In particular, we use moth-flame optimisation as the central decision-making unit to configure the deep neural network structure and the proper input data selection. Besides, the moth-flame optimisation algorithm was modified to improve its search space mechanisms. The forecasting skills are assessed using 13 datasets from locations across the Pacific and Atlantic coasts, and the Gulf of Mexico. The proposed optimised deep neural network performs well at all the sites, especially over short-term horizons, where it outperforms statistical and physics-based approaches.

Abstract: In practice, dynamic positioning (DP) vessels are subjected to complex disturbances as well as the magnitude and changing rate constraints of the thrusts and moments. This study applied a dynamic surface controller based on an uncertainty and disturbance estimator (UDE) to a DP vessel with complex disturbances and input constraints. The UDE was designed to estimate and handle the complex disturbances. An auxiliary dynamic system (ADS) and smooth switching function were employed to compensate for the input constraints and avoid the singularity phenomenon caused by the ADS, respectively. The combination of the UDE method and dynamic surface control (DSC) technology significantly simplified the design process for the control law and increased the practicability for DP vessels. The stability of the proposed control law was proved using the Lyapunov theory. The effectiveness of the control law and possibility of actually applying it to a DP vessel were verified using simulation experiments.

Abstract: The cavity dynamics and trajectories of twin spheres vertically entering water side-by-side are investigated experimentally for different time intervals and several lateral distances at initial velocities from 14.1 m/s to 15.2 m/s, with the diameter (D) based Froude number varying from 37.1 to 40.0. A high-speed photograph system and an image processing method are employed to obtain the features of cavity flow and the position of twin spheres. We firstly investigate the cavity shapes and sphere trajectories with lateral distance being 1.5D between the spheres. Results show that the contact lines of two cavities are obliquely pinned on the twin spheres in synchronous water entry, and a molar-shaped cavity is formed by the overlap of the two cavities. Subsequently, the motion characteristics of the second water-entry sphere (Sphere II) are studied as the increase of time interval between the twin spheres. It is found that the influence on Sphere II increases as the decrease of the time interval at lateral distance of a = 1.5D, except a certain condition, i.e. Δt = 2.5 ms. As we change the lateral distance in synchronous water entry, the disturbance introduced by adjacent sphere is weakened with the increase of the distance. The results demonstrate that both the cavity dynamics and trajectories of Sphere II are similar to that of single water entry when the lateral distance increases to 5.5D. As for the combined effects of lateral distance and time interval on twin water entry, it is found that Sphere II is attracted horizontally to Sphere I at moderate time intervals with the lateral distance of a = 1.5D, while the interaction acted on Sphere II in cases of a = 2.5D and 3.5D changes from repulsion to attraction gradually as the increase of time interval from 3.5 ms to 8.4 ms and then to 13.1 ms.

Abstract: The harbor resonance triggered by double solitary waves (DSWs) with different wave parameters (including various wave heights and relative separation distances) is simulated based on the fully nonlinear Boussinesq model, FUNWAVE-TVD. A long and narrow harbor with different topographies is adopted. In the current study, effects of incident wave height, relative separation distance and bottom profile on hydrodynamic characteristics related to the transient oscillations are mainly investigated. The hydrodynamic characteristics considered include the evolution of the maximum free-surface elevation, the maximum runup, the wave energy distribution and the total wave energy inside the harbor. Results show that Green's law can accurately estimate the evolution of the maximum free-surface elevation in most part of the harbor area. The impacts of the topography on the maximum runup exhibit a strong dependence on the incident wave height. The smaller mean water depth inside the harbor, the larger relative separation distance, and the higher incident wave height tend to result in greater uniformity of the wave energy distribution. The normalized total wave energy is always shown to decrease gradually with the incident wave height, and to increase remarkably at first and then decrease slightly with the increase of the mean water depth.

Abstract: Routine cleaning of the corroded ship hulls in dry dock maintenance guarantees the smooth operation of the shipping industry. Deploying the autonomous system to remove the corrosion by water-blasting is a feasible approach to ease the burden in manual operation and to reduce water, time, and energy consumption. In this paper, the water-blasting framework is proposed for a novel robot platform named Hornbill with the adhesion mechanism by permanent magnetic, self-localization by sensor fusion to navigate smoothly on a vertical surface. Hence, we propose a complete waypoint path planning (CWPP) to re-blast the self-synthesizing deep convolutional neural network (DCNN) based corrosion heatmap by initial-blasting. The optimal CWPP problem, including the shortest travel distance and shortest travel time to save water, power while ensuring visiting all predefined waypoints by benchmarking output, is modeled as the classic Travel Salesman Problem (TSP). Further, the Pareto-optimal trajectory for given TSP has been driven by the reinforcement learning (RL) technique with a proposed reward function based on the robot's operation during blasting. From the experimental results at the shipyard site, the proposed RL-based CWPP generates the Pareto-optimal trajectory that enables the water-blasting robot to spend about 10% of energy and 9% of water less than the second-best evolutionary-based optimization method in various workspaces.

Abstract: This paper investigates the finite-time distributed formation control for unmanned surface vessels (USVs) exposed to external disturbances, model uncertainties and input saturation constraints. By combing the sliding mode control method and adaptive algorithms, two control architectures are developed for USVs’ formation control problem. Radial Basis Function Neural Networks (RBFNNs) is adopted for approximating the unavailable system dynamics, where the minimum learning parameter (MLP) algorithm is utilized to alleviate the excessive occupation of the computational resource. By feat of an auxiliary system, an adaptive mechanism is devised such that the input saturation problem could be figured out. It follows from the theoretical analysis that finite-time convergence is achievable under the proposed two controllers. Finally. numerical simulations are exhibited to illustrate the effectiveness of the proposed formation control schemes.