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Photometric stereo is a widely used optical 3-D reconstruction method, which has advantages for high-resolution and well-detailed 3-D reconstruction even in weak texture regions. However, in underwater photometric stereo, the light refraction and attenuation using the camera and point light sources usually result in significant errors in shape recovery. In this article, we present a novel solution to address these challenges for improving the performance of underwater photometric stereo by combining it with underwater laser triangulation. First, we employ underwater laser triangulation as references to correct the global surface-shape distortion caused by nonuniform close-range illumination. Second, we propose to use an underwater camera refractive model to eliminate nonlinear refraction distortion. Third, we build a device implementing the proposed method for 3-D seabed reconstruction in on-site sea trials. Experimental results demonstrate that the proposed approach is able to produce accurate 3-D reconstruction results in the underwater environment.

MARPOL 73/78, Chapter IV is considered to reveal the emission control engineering on ships. The probability model of the fuel oil consumption is established based on the machine learning technique. The proposed methods are applied into this research in order to establish the probability model of fuel oil consumption. The combination of Monte Carlo (MC) simulation method with Artificial Neural Networks (ANNs) is an optimal solution to deal the fuel consumption of marine main diesel engine. The sample data has been established based on the Monte Carlo simulation method. The model of fuel oil consumption is designed by Artificial Neural Network method. The proposed prediction model of fuel oil consumption is based on a back-propagation training algorithm of ANNs method. The research results of proposed model have been verified with the actual operation data that have been collected from a certain bulk carrier of VINIC shipping transportation company in Vietnam. The collected data is the actual operation parameters from the noon-log report of voyage during two years of the ship. The probability model of fuel oil consumption for main diesel engine is very useful in the field of ships energy efficiency management with higher accurancy than the other previous models.

The extreme operational environmental conditions and aging conditions of subsea structures pose a risk to their structural integrity and is critical to their safety. Non-destructive testing is essential to identify defects developing within the structure, allowing repair in a timely manner to mitigate against failures that cause damage to the environment and pose a hazard to human operators. However, in order to be cost effective, inspections must be carried out without taking the risers out of service. This poses significant safety risks if undertaken manually. This paper presents the development of an automated inspection system for flexible risers that are used to connect wellheads on the seafloor to the offshore production and storage facility. Due to the complex structure of risers, radiography is considered as the best technique to inspect multiple layers of the risers. However, radiography inspection in turn requires a robotic system for in-situ inspection with higher payload capacity, precise movement of source and detector which is able to withstand an extreme operational environment. The deployment of a radiography inspection system has been achieved by developing a customized subsea robotic system called RiserSure that can precisely provide the scanning motion of a gamma ray source and digital detector moving in alignment. The prototype has been tested on a flexible riser during shallow water sea trials with the system placed around a riser by a remotely operated vehicle. The results from the trials show that the internal inner and outer tensile armour layer and defects in the riser can be successfully imaged in real operational conditions.

Underwater acoustic target recognition based on ship-radiated noise is difficult owing to the complex marine environment and the interference by multiple targets. As an important technology for target recognition, deep-learning has high accuracy but poor interpretability. In this study, an attention-based neural network (ABNN) is proposed for target recognition in the pressure spectrogram with multi-source interference using an attention module to inspect the inner workings of the neural network. From data obtained during a September 2020 sea trial, the ABNN exhibited a gradual focus on the frequency-domain feature of the target ship and suppressed environmental noises and marine vessel interference, which led to high accuracy in the target detection and recognition.

Underwater autonomous navigation technology is a key link for AUV to complete various underwater missions successfully. In this paper, we have proposed a position correction model based on hybrid gated RNN, which does not need to establish a motion model like typical navigation algorithms, to avoid modeling errors in the navigation process. According to the low update rate of GPS and the slip data caused by the coverage of GPS antenna during the process of floating and diving, it does not meet the actual requirements of navigation and control. We proposed to use improved EKF and iSAM as the output of the training dataset to filter out GPS slips, then the position error is corrected at the same time. First, the normalized input sequence is fed to the two-layer hybrid gated RNN with BiLSTM output mode as sequence and LSTM output mode as last, and then the processed data is fed to the two fully connected layers with a dropout layer added in the middle. After that, the output of the position correction model can be obtained through the regression layer. To confirm the validity of the proposed model, we conducted sea trials to compare the proposed model with EKF and iSAM. The experimental results prove that the proposed position correction model performs better than EKF and iSAM in all aspects, and the correction model trained with iSAM is more effective than that trained with EKF.

A novel system was developed to deploy settlement panels to monitor biofouling growth in situ and evaluate antifouling coatings at depths representative of operational conditions of full-scale marine renewable energy devices. Biofouling loading, species diversity, and succession were assessed at depths ranging from 25-40 m at four tests sites in Orkney (UK) featuring extreme wave and tidal current exposure to more sheltered conditions. Evaluations were carried out over a period of 8 months with intermediate retrieval of samples after 3 months. Early pioneer fouling communities, comprised of colonial hydroids, were succeeded by tube-forming amphipods across sites while solitary tunicates dominated in greater shelter. The highest biofouling loading was observed on high-density polyethylene (HDPE) panels (6.17 kg m-2) compared with coated steel (3.34 kg m-2) panels after 8 months. Distinct assemblages were present at exposed vs sheltered sites. Better understanding of fouling and antifouling strategies may provide guidance to more effectively manage biofouling impacts in this sector.

One of the challenges of Internet of Underwater Things (IoUT) is the design of nodes for collecting information from underwater, with features of low cost, long-term, long range, voyage routing, and real-time communications. Wave glider, has shown great potential acting as IoUT nodes through its persistent, long range travelling, and flexibility underwater. In this paper, we propose an architecture of Internet of Underwater Things, involving multiple wave gliders as nodes for acoustical observation. We present target localisation method via acoustical observation of nodes with multiple wave gliders deployed underwater, by which precision of bearing estimation of each node is required to achieve high precision of localisation. With the data collected, we apply a compensation method of bearing estimate when the hydrophone array is rotating during the observation. The feasibility of acoustical observation of wave gliders has been validated through both simulation and sea trial data, which is of great potential to be nodes for constructing IoUT.

A joint multiple-input multiple-output (MIMO) processing framework is proposed to exploit spatial diversity and moving cues for the enhancement of target detection in a shallow ocean environment. Orthogonal signals are transmitted to illuminate different aspects of a target and beamforming operation is carried out over the received data for estimating target bearing. The target range is achieved by a replica correlation integrator where the beamformer outputs are matched with transmitted signals. After meanshift clustering algorithm is carried out over the bearing-range spectrums to generate the clutter centers, the potential trajectories of targets can be tracked by an improved Bernoulli filter with inputs of these centers. The at-sea experimental results have shown the effectiveness of the joint processing framework in MIMO detection of moving targets.

Small-scale ocean observations play an important role in oceanographic research. The station-keeping method based on the underwater mobile platform has been proposed as a means of small-scale ocean observation with the advantages of low cost and high flexibility. However, few studies have carried out complete modeling of the small-scale ocean observation process of mobile platforms and proposed a corresponding station-keeping method based on this. In this article, a hybrid underwater profiler (HUP) was tested under two working conditions for its ability of maintaining position during designated area persistent monitoring (DAPM) within a specific area of the sea over a long period. The DAPM problem is modeled and the cost function that evaluates the performance of DAPM is defined. If the underwater profiler cannot measure data about underwater currents, it can maintain its position by setting constant gliding and yaw angles based on the depth-averaged current measured each time it surfaces. If it has instruments to observe ocean currents at each depth during a dive, the profiler can maintain its position using a strategy based on a quasi-Newton method. Simulation results show that the gliding strategies effectively assist the HUP in monitoring a designated area. Additionally, sea trials were performed without instrumentation to measure ocean currents and the effectiveness of the simpler gliding strategy was clearly demonstrated.

The study of extending navigation range of autonomous underwater vehicles (AUVs) is popular for ocean exploration. A common method is to use the underwater docking station to recover and recharge AUVs. This paper uses the simple but effective inductive power transmission (IPT) system to realize underwater wireless charging for an AUV docking system. A design procedure is provided to select the optimal underwater operating frequency of the IPT system in consideration of eddy-current loss in seawater. To solve the power failure problem of the control power system, an inrush current limiter (ICL) is proposed on the basis of the dynamic model of the IPT system. The test results in the laboratory experiment and sea trial both verify the advantages of the frequency design method and the effectiveness of the ICL.

Since rain alters the histogram pattern of radar images, rain-contaminated radar data can be identified. In this article, a support vector machine (SVM)-based method for rain detection using X-band marine radar images is presented. First, the normalized histogram bin values for each image are extracted and combined into feature vector. Then, SVMs are employed to classify between rain-free and rain-contaminated images. Radar images and simultaneous rain rate data collected from a sea trial in North Atlantic Ocean are utilized for model training and testing. Comparison with the zero pixel percentage (ZPP) threshold method shows that the SVM-based method obtains higher detection accuracy, with 98.4% for the Decca radar data and 99.7% for the Furuno radar. It is also found that as the total number of bins does not significantly affect detection accuracy, the proposed method can be applied to different radar systems directly with a suitable number of bins. In addition, compared to the ZPP threshold method, the SVM-based method proves to be more robust even with limited training samples.

The colonization of underwater environments by exotic seaweeds is causing major ecological problems around the world. This project, referred to AMALIA, aims to transform this current ocean threat into an opportunity by adding value to the macroalgae present off the northwest of the Iberian Peninsula. To do so and to observe the presence of seaweeds in situ, an ocean modular submersible platform was developed. This platform was designed to be capable of detecting and surveying surges of invasive seaweeds while withstanding sea conditions. Conceptual designs followed by a screening process were performed, taking into consideration criteria such as operational range and modularity. An open-frame lander was considered and further developed using buckling criteria. In parallel, a state-of-the-art monitoring system was created using spectral imaging, allowing for the future creation of a macroalgae identification system. In addition, sensorial systems for characterizing growth conditions were introduced. Laboratory trials were executed to assess the capability of the system, and sea trials are currently being performed. Numerical simulations and laboratory trials indicate that the structure is fully capable of being deployed for shallow-water environments with a state-of-the-art invasive seaweed monitoring system while maintaining a high degree of modularity.

This paper proposes a method that synthesizes realistic sonar images using a Generative Adversarial Network (GAN). A ray-tracing-based sonar simulator first calculates semantic information of a viewed scene, and the GAN-based style transfer algorithm then generates realistic sonar images from the simulated images. We evaluated the method by measuring the similarity between the generated realistic images and real sonar images for several objects. We applied the proposed method to deep learning-based object detection, which is necessary to automate underwater tasks such as shipwreck investigation, mine removal, and landmark-based navigation. The detection results showed that the proposed method could generate images realistic enough to be used as training images of target objects. The proposed method can synthesize realistic training images of various angles and circumstances without sea trials, making the object detection straightforward and robust. The proposed method of generating realistic sonar images can be applied to other sonar-image-based algorithms as well as to object detection.

In 2018, a German- Dutch project consortium under the scientific direction of the Emden/Leer University of Applied Sciences retrofitted and commissioned the latest rotor development of the Eco-Flettner type on the test ship Fehn Pollux of the Leer-based shipping company Fehn Ship Management. Main design features are both upper and lower endplates with large diameter for improved aerodynamic performance and modular manufacturing. The retrofitting concept is groundbreaking in terms of easy transferability to other ships. Upscaling to a significant share of the world merchant fleet could make a substantial and realizable contribution to climate protection in the short term and potentially reduced transport costs in the long term. The most frequently asked question in connection with modern sail drives is about the performance potential and the associated fuel savings. Transparent performance data is required to enable an economic prognosis for the use of Flettner rotors on ships. The Faculty of Maritime Sciences at Emden/ Leer University of Applied Sciences has developed an automatic control and monitoring system for Flettner rotors that also records extensive operating and environmental data. The data shows that all previous assumptions and model calculations are basically correct. With regard to the performance potential, the first series of measurements show even higher rotor forces compared with model calculations. This is a further benefit for Flettner rotor efficiency and could help the technology achieve a breakthrough as a building block for low-emission shipping.

An integrated wave-tidal current power turbine is affected by both wave and tidal current forces, and its energy efficiency is closely related to the velocity and direction of the two forces. To improve the probability of the horizontal axis turbine reaching maximum energy efficiency under real-time changing sea conditions, we performed the following investigations in this study. Based on the actual application scenario of Lianyungang port, a time series prediction model of tidal current (velocity and flow direction) and wave (mean wave direction, mean wave period, and significant wave height) data for the past year was established. The changes in waves and tidal currents within 24 h after the cutoff point of the existing data were predicted. The integrated wave-tidal current mechanism was studied, and the superposition of wave energy and tidal current energy was transformed into the equivalent velocity vector of wave-tidal current integration. The conversion coefficient between waves and equivalent flows was determined by a numerical wave flume simulation. According to the historical wave and tidal current data, the equivalent velocity range of the integrated action of waves and tidal currents in Lianyungang was determined. The influence of different blade motions on the energy harvesting efficiency of the turbine under the corresponding flow conditions was studied using the Computational Fluid Dynamics (CFD) method to determine the blade motion law of the turbine. The blade motion law of the prototype was verified in a sea trial experiment. The experimental results were basically consistent with the simulation results for the blade motion law designed according to the wave and tidal current prediction law. This design scheme can provide a reference for engineering design for the development and utilization of new marine energy.

Source ranging based on ship-radiated noise is a crucial task in many practical applications. Deep neural networks (DNNs) have shown outstanding performance but poor interpretability on source ranging, leading to the heavily hidden risks of blind trust in the AI black box. In this study, an attention-based convolutional neural network (ABCNN) is proposed for the ship ranging in an attempt to visualize the features of concern in neural networks. Acoustic data of four ships were collected during a sea trial conducted in January 2021 to validate the ship ranging performance of ABCNN. Results showed high accuracy in ship ranging using synthetic data and part of the experimental data as a training set for the proposed method. The attention mechanism visualized a concentration on the inherent features of ships and the waveguide effect of underwater acoustic channels.

Shape from shading(SFS) algorithm, based on Lambert diffuse reflection model, is often utilized for three-dimensional(3D) reconstruction from two-dimensional(2D) forward-looking sonar image. However, the Lambert model has been demonstrated to be an inaccurate approximation to the diffuse component, so traditional SFS is not reliable to reconstruct the 3D model. This paper applies a modified SFS algorithm in optics, based more accuracy Oren and Nayar diffuse reflection model, to the 3D reconstruction from 2D sonar images. After verification of the simulation data and sea trial data, the 3D model, reconstructed by the algorithm in this paper, is closer to the actual size of objects and recovers the details of objects better. Thus the modified SFS algorithm, in this paper, is more accurate than the traditional SFS in 3D reconstruction from 2D forward-looking sonar images.

Artificial upwelling (AU), as one of the geoengineering tools, has received worldwide attention because of its potential ability to actualize ocean fertilization in a sustainable way. The severe challenges of AU are the design and fabrication of a technologically robust device with structural longevity that can maintain the function in the variable and complex hydrodynamics of the upper ocean. In this work, a sea trial of an air-lift concept AU system driven by self-powered energy was carried out in the East China Sea (ECS; 30°8′14″N, 122°44′59″E) to assess the logistics of at-sea deployment and the durability of the equipment under extremely complex hydrodynamic conditions from 3 to 7 September 2014. Seawater below the thermocline layer was measured to be uplifted from approximately 30 m to the euphotic layer with a volumetric upwelling rate of 155.43 m3 h−1 and total inputs of 2.8 mol h−1 NO3−, 0.15 mol h−1 PO43−, and 4.41 mol h−1 SiO43−. A plume formed by cold, saline deep ocean water (DOW) was tracked by a drifting buoy system with a mixing ratio of 37%–51% DOW at the depth of 18–22 m, which conforms to the simulation results. During the AU’s application, disturbance in the vertical hydrological structure could be observed. However, diatom (Skeletonema costatum) blooming from somewhere in the outer ECS floated to the sea trial region on the second day after the AU’s application, which makes it hard to strip off the biochemical effects of AU from the effects of S. costatum bloom.

Twin hull high-speed catamarans encounter a wide range of sea wave loads. This paper studies the full-scale prediction of global loads on a high-speed catamaran using linear regression analysis based on finite element results. Load cases based on Det Norske Veritas rules are applied to a finite element model to derive load–strain transformation. Strain responses are evaluated at 16 different locations on the catamaran finite element model corresponding to the strain gauges positioned on the HSV-2 Swift 98m Incat catamaran during sea trials. A transformation matrix is generated using the concept of ordinary least squares, to convert from strain responses to the equivalent DNV global load cases. This is applied to determine global loads during several sea trial runs in different heading angles and speeds of 10, 20 and 35 knots. These loads then are compared to show each global load severity at specific speed or heading angle.

Three-dimensional (3-D) power prediction method from model test has been widely accepted and used for many years. Form factor k is the most important characteristics of 3-D method and very crucial to the accurate power prediction. However, it’s rather difficult to get the accurate value of k for those ships with pronounced bulbous or large transom from model test. In this paper, a new method to predict power is proposed, using k from CFD while other data from model test (or experimental fluid dynamics (EFD)). Delivered power prediction using the combination of CFD/EFD method agrees well with sea-trial results.

Tidal current energy is a promising renewable energy resource. Full scale tidal current turbines (TCT) have been deployed all over the world. To verify the design methods of large TCTs with high reliability and efficiency, a 600-kW tidal current turbine was designed and tested in this study. This turbine consisted of a two-blade rotor, a low speed ratio gearbox and a medium speed generator. The turbine was grid-connected through a full-rated converter, and its speed was controlled to capture the maximum power. First, to reach high turbine performance, system design details and maximum power point tracking (MPPT) control strategy were considered. Then, the 600-kW turbine was manufactured, and an onshore test was carried out to test power transmission and control performance. The power performance of the blades and control system were further tested in the sea trial carried out in Zhoushan. Good agreement was obtained between theoretical analyses and test results. Finally, to ascertain causes for observed power fluctuations, the power spectral density (PSD) was analyzed; the results indicated that power fluctuations may be due to stream shear.

In the development of ocean energy technologies, full-scale sea trials have proven technically challenging and expensive. As a contribution to the development of flexible, reliable, and affordable measurement systems for such sea trials, this paper presents and evaluates a portable data acquisition system. The system offers a cheap and flexible option for when and where signal infrastructure at site is not available. It is battery powered and consists of a sensor system and a logger unit. The sensor system is placed inside the object of study, which for this application is a wave power generator standing on the seafloor. The logger unit, which contains a logger with integrated data storage and a battery, is placed outside the object of study in a submersible and retrievable vessel. Sensor output is carried with 4–20-mA current signals between the sensor system and the logger, which makes the system directly compatible with a wide range of industrial sensors. For the specific setup implemented in this paper, the sensor system was used to measure voltage and current inside the generator. Beyond this, the system is designed to be adaptable for a wider range of sensors, with 16 individual signals and a sampling frequency up to 1 kHz. The custom logger is programmable and offers many options to apply different logging schedules and to limit the sensor system power supply accordingly. Evaluations show that the implemented system offers 5–22 weeks of 1-kHz and 16-b monitoring of 16 signals, depending on a chosen logging schedule. Suggestions are also given for how to raise the operation time up to 50 weeks. The system has been tested offshore for the collection of power production data from two wave energy converters, with good results.

This study analyzes and summarizes seven main characteristics of the marine data sampled by multiple underwater gliders. These characteristics such as the big data volume and data sparseness make it extremely difficult to do some meaningful applications like early warning of marine environment. In order to make full use of the sea trial data, this paper gives the definition of two types of marine data cube which can integrate the big marine data sampled by multiple underwater gliders along saw-tooth paths, and proposes a data fitting algorithm based on time extraction and space compression (DFTS) to construct the temperature and conductivity data cubes. This research also presents an early warning algorithm based on data cube (EWDC) to realize the early warning of a new sampled data file. Experiments results show that the proposed methods are reasonable and effective. Our work is the first study to do some realistic applications on the data sampled by multiple underwater vehicles, and it provides a research framework for processing and analyzing the big marine data oriented to the applications of underwater gliders.

Prediction of the dynamic behavior of autonomous underwater vehicles (AUVs) is the basis for developing the control system and optimizing the hydrodynamic shape of AUVs. A fast and efficient prediction method of dynamic behavior of AUVs was developed with the secondary programming of a commercial computational fluid dynamics (CFD) code of FLUENT. In the proposed CFD method, mass properties of the AUV and thrust of the propeller were coupled to the 6-degree-of-freedom (DOF) dynamic equations of AUV in the form of user defined functions. A subregional unstructured mixed meshing strategy combining static meshes and dynamic meshes was proposed to improve the computational efficiency and to realize the 6-DOF movement of AUV. The proposed CFD prediction method can predict not only the dynamic behavior of the AUV, but also its surrounding flow field. Dynamic behavior of a seafloor mapping AUV in straight running and turn running were predicted using the developed CFD strategy. Comparison between the prediction results from the CFD method and those from the sea trials proved the proposed method to be accurate and effective. Some measures were put forward to improve motion performance of the vehicle according to the prediction results.

The target of analyse was to determine the exposure levels to the staff on the vessel and ensure adequate measures are in place to minimize the exposure when necessary. Noise levels through the vessel will be compared to the noise code detailed in section 2 of this report. The measurement data results obtained at this survey will be analysed against the codes, as shown below. In addition, the measurement result table has the readings from 2007 during sea trials at the shipyard, Brodogradilište d.o.o. – Split. The analyse is to measure the exposure levels, through the accommodation and machinery spaces to determine the risk to staff working in these areas as well as making recommendations that could reduce the exposure levels.

A comparison between sea trial measurements and full–scale CFD results is presented for two self–propelled ships. Two ships considered in the present study are: a general cargo carrier at Froude number Fn=0.182 and a car carrier at Fn=0.254. For the general cargo carrier, the propeller rotation rate is fixed and the achieved speed and trim are compared to sea trials, while for the car carrier, the propeller rotation rate is adjusted to achieve the 80% MCR. In addition, three grids are used for each ship in order to assess the grid refinement sensitivity. All simulations are performed using the Naval Hydro pack based on foam-extend, a community driven fork of the OpenFOAM software. The results demonstrate the possibility of using high–fidelity numerical methods to directly calculate ship scale flow characteristics, including the effects of free surface, non–linearity, turbulence and the interaction between propeller, hull and the flow field. Keywords: Full scale ship propulsion, Actuator disc, Sea trial validation, Grid sensitivity studies, OpenFOAM

In this short paper, we report some preliminary results on the seabed terrain reconstruction using the two-dimensional (2D) forward-looking sonar. In the underwater pipeline burying project, when the burying machine excavating the trench using the jetting device, the stirred turbid water sharply shorten the visual distance of optical camera. Researchers from Shenyang Institute of Automation, Chinese Academy of Sciences (SIA, CAS) and Korea Institute of Robot and Convergence (KIRO) work together to reconstruct the 3D environment using the acoustic camera. The proposed reconstruction procedure mainly includes two parts: 3D point-cloud extraction for each sonar frame and point-cloud registration. With the former step, the elevation information, which is irreversely lost in the acoustic imaging process, is recovered by the shape-from-shading technology under the Lambert surface assumption. With the latter, a registration method based on symmetrical Kullback-Leibler divergence has been proposed to stitch the point clouds from each sonar image into a panoramic terrain map. The initial experiment results with the sea-trial datasets demonstrate that the proposed method is feasible for the practical underwater engineering.

The prediction of underwater acoustic transmission loss in the sea plays a key role in generating situational awareness in complex naval battles and assisting underwater operations. However, the traditional classical underwater acoustic transmission loss models do not consider the regional hydrological elements, and the performance of underwater acoustic transmission loss prediction under complex environmental conditions in a wide range of sea areas is limited. In order to solve this problem, we propose a deep learning-based underwater acoustic transmission loss prediction method. First, we studied the application domains of typical underwater acoustic transmission loss models (ray model, normal model, fast field program model, parabolic equation model), analyzed the constraint rules of its characteristic parameters, and constructed a dataset according to the rules. Then, according to the characteristics of the dataset, we built a DBN (deep belief net) neural network model and used DBN to train and learn the dataset. Through the DBN method, the adaptation and calculation of the underwater acoustic transmission loss model under different regional hydrological elements were carried out in a simulation environment. Finally, the new method was verified with the measured transmission loss data of acoustic sea trials in a certain sea area. The results show that the RMSE error between the underwater acoustic transmission loss calculated by the new method and the measured data was less than 6.5 dB, the accuracy was higher than that of the traditional method, and the prediction speed was faster, the result was more accurate, and had a wide range of adaptability in complex seas.

ROV trencher is a kind of ROV which trenches the sea floor using a specifically designed tool and buries the subsea cables and pipelines. According to the soil conditions, this trenching method can have two different types, one is mechanical cutting and the other one is water jetting. In this paper, we present a water jet tool design method for a 2500 m depth-rated ROV trencher. A series of CFD simulations and laboratory tests with one nozzle, and a ground test using 1:6 scale jetting arm model were carried out to derive and demonstrate the jetting tool design parameters. In October 2018, the constructed ROV trencher was put into the sea trial in the East Sea of Korea to evaluate its final performances. In addition, in December 2019, the trencher was applied in a construction site to bury subsea water pipelines near the Yogji Island in the Korea. Through these two field tests and operation, the trencher was demonstrated for both its operational capability and trenching performance. The main contribution of this paper is that it presents the entire design procedures of water jet tools, including CFD simulations, laboratory tests, field test with 1:6 scaled jetting tool, and the final prototype tool design. These consecutive procedures are carried out in order for us to set up sort of relationship between jetting angle, trench depth, trench speed, and jetting power, from which we can predict and evaluate the trenching performance of the prototype jetting tool.

The Gate Rudder is a recently introduced twin rudder system whose major advantages stem from its energy-saving properties. The two foil-shaped blades of the rudder, placed aside of the propeller, act as a partial duct in the wake of the hull producing additional thrust. However, since the rudder is a primary safety device on any vessel, investigating the manoeuvrability performance of the Gate Rudder is a critical aspect that needs careful and detailed consideration. Owing to its peculiar working principle and location, the standard manoeuvrability prediction methods cannot produce reliable results since they are applied to rudders acting behind the propellers. This paper presents the first comprehensive investigation into the manoeuvrability performance of a Gate Rudder system, which includes the development of a modified MMG model, towing tank experiments and full-scale measurements. The modified MMG model was conceived to predict the manoeuvring motions of a ship with the Gate Rudder system. A generalised prediction method is defined based on this modified MMG model and detailed CFD analysis of the flow pattern around the Gate Rudder for two commercial hull-forms. The simulation model of the Gate Rudder is validated by means of towing tank tests and full-scale manoeuvring trials. The sea trials were conducted onboard two sister container vessels, the first fitted with the first-ever Gate Rudder system and the second with a high-performance flap rudder. This also allowed to compare the two different steering systems.

Autonomous navigation quay to quay is a goal for various surface vessel trades, from inland ferries to river transport and offshore services. Ability to navigate safely within a harbour or other confined waters is an essential step-stone towards this goal. This paper aims at creating a map of open water area that is available for safe navigation, given dynamic and static obstacles. Employing electro-optical sensors, the paper suggests open water detection using a classification convolutional neural network on context sensitive sub-partitioning of an image in a pyramid of smaller areas, combining the classifications in to a map of subareas containing open water. A salient feature of this approach is the ease of annotation and ease of creating a large amount of annotated images that is needed for machine learning. Following classification of sub-areas, camera images are transformed to bird’s view by projective geometry methods to enable planning of feasible paths for navigation. This new approach is validated on data from sea trials in Danish waters.

This paper addresses the challenges and advances made in the hydrodynamic design and design evaluation of ducted propellers. The application of new ducted propeller analysis tools is shown for the practical case of the design of controllable pitch propellers for hopper dredgers. Results of model tests and sea trials are compared with results of a state-of-the-art boundary element method (BEM) code that is used for the simulation of the unsteady cavitating flow around ducted propellers. The results of the BEM simulations are compared to more time consuming and comprehensive URANS sliding interface calculation results in which the flow including the unsteady flow around the rotating propeller is modelled in the RANS domain. Comparisons are made of pressure distributions and pressure side cavitation margins that are important in the propeller blade design. The details of the flow into the duct are discussed as well as its implications on the propeller blade design. The employed BEM method is proposed as the best practical next step in ducted propeller design given its reasonable calculation times. Based on the obtained results a new research initiative has been started to obtain better sea trial data from ducted propellers to improve and validate the ducted BEM method as well as the SI URANS method.

Ocean vehicles (such as AUVs, Gliders, and ROVs) are rapidly developing. They have become mainstream collectors of standard oceanographic data (conductivity, temperature, pressure, dissolved oxygen, fluorescence and backscatter) and are increasingly used as trucks to carry a wide variety of hydrographic and bio-geochemical sensors. Due to the complexity of the marine environment, there are significant uncertainties in the use of ocean vehicles. As an essential part of ocean vehicles' performance verification, sea trials have problems such as complex sea conditions, painful process experiments, and high costs. To save human resources, financial resources, and time, a large number of simulation tests must be carried out before sea trials. This paper uses the strategy of combining fluid mechanics software and physics engine to solve the complex problem of simulation experiment. Virtual reality technology and unity3d engine are used to assist in constructing a virtual ocean environment in simulating the working state and process of ocean vehicles in different conditions, reducing the difficulty and cost of experiments, and improving the working efficiency of ocean vehicles. The accuracy of the virtual simulation experiment platform is verified by comparing sea trials' measured data with the simulation results.

In this paper, a novel combination method based on the AdaBoost algorithm and the general regression neural network (GRNN) is proposed to predict the depth-averaged current (DAC) for the next profile using the underwater gliders (UGs). Firstly, considering changes of the seawater density and the hull deformation, the velocity calculation model of UGs and the calculation method of DAC are given. Secondly, two DAC forecasting models are established with historical data using the support vector machine (SVM) and the back-propagation neural network (BPNN) methods, respectively. Then, the forecasting models are combined nonlinearly by adopting the GRNN. Finally, outputs from the GRNN are combined using the AdaBoost algorithm, which further taken for the DAC forecasting. A sea trial was conducted using Petrel-II glider in the South China Sea, which verified the accuracy of the three forecasting models. Results demonstrate that the proposed combination method has a better performance in DAC forecasting than the other two single forecasting models.

Quantification of the escapement rate of unwanted fish through a selective device is usually based on catch comparison. This study proposes a new, time efficient method to automatically compare two selective devices by automated counting of fish escapements through each selective device based on video sequences. First, sea trials were conducted to record video sequences of fish escaping a white and black square mesh panel. Then, all of the underwater sequences were automatically analysed by a computer vision software for automated object detection and tracking. Finally, the algorithm was assessed using 150 min of video sequences analysed by humans. We observed that the variability in escapements rate between all the observers on reference video sequences could reach 5%. As the difference in escapements rate between the algorithm and the observers was lower than the variability between observers, the automated approach was validated. The software detected a significant difference in fish escapement rate according to the net colour in the camera field of view: 60% of all fish escaped through the white panel. Our results suggest that net colour influences the escape rates of fish. The colour of the selective device should therefore be investigated further with the aim of increasing their efficiency. Further development of the software could be done to identify species and size of the fish and assess the effectiveness of a selective device by species and size.

The task of visual classification, done until not long ago by specialists through direct observation, has recently benefited from advancements in the field of computer vision, specifically due to statistical optimization algorithms, such as deep neural networks. In spite of their many advantages, these algorithms require a considerable amount of training data to produce meaningful results. Another downside is that neural networks are usually computationally demanding algorithms, with millions (if not tens of millions) of parameters, which restricts their deployment on low-power embedded field equipment. In this paper, we address the classification of multiple species of pelagic fish by using small convolutional networks to process images as well as videos frames. We show that such networks, even with little more than 12,000 parameters and trained on small datasets, provide relatively high accuracy (almost 42% for six fish species) in the classification task. Moreover, if the fish images come from videos, we deploy a simple object tracking algorithm to augment the data, increasing the accuracy to almost 49\% for six fish species. The small size of our convolutional networks enables their deployment on relatively limited devices. TRUE pub

An autonomous underwater vehicle (AUV) relies on an underwater camera and sonar for perception the surrounding environment including underwater image processing, object detecting and tracking. For underwater optical environment perception, the limited visual range of the camera severely limits the acceptance of detection information; in addition, underwater light is assimilated and scattered, which seriously deteriorate the underwater imaging, especially decreases the image contrast. To address these issues, a joint framework based on CNN is proposed to improve underwater image quality and extract more matching feature points. For accurate registration between images, an underwater mosaicking technology is also involved, and a fusion algorithm is implemented to mitigate artificial mosaicking traces. Experimental results show that our presented framework can not only keep underwater image detail information, but also exact more matching feature points for registration and mosaicking. For underwater acoustic image environment perception, mechanically scanned imaging sonars (MSIS) are usually equipped on AUVs for avoiding obstacles and multiple-targets tracking. In this chapter, two multiple underwater object tracking methods are presented. The proposed methods are using the cloud-like model data association. Some sea trials are implemented to validate the effectiveness of the presented algorithms. Experiment results demonstrate that the presented cloud-like model data association method has the characteristics of more accurate clustering. Multiple targets were finally clustered for AUV stable tracking.

A new pair of optical wireless modems has been realized, which exploit visible light communication to transmit Ethernet signals through water. The modem prototypes were finally tested in sea waters at La Spezia harbor; they successfully transmitted 10 Mbit/s 10Base-T signals over up to 10 m, notwithstanding the high turbidity and the strong sunlight. Final tests included the integration with SUNRISE testbed and the use with a moving robot, remotely operated. Commercial components were used to realize the modems; thus, we expect that the key design concepts can be used as a starting point for practical deployment of this technology.

A gradual change of the pitch angle was observed for a glider in a sea trial in August 2014. It took place when the glider moved in both upward and downward glides between the sea surface and a depth of 1200 m and there was neither any internal mass movement nor buoyancy engine regulation in the steady glide. In this paper, this phenomenon is validated via a nonlinear dynamic model, which is restricted to the vertical plane. The model is based on Kirchhoff's equations of motion for a submerged rigid body and takes the seawater pressure, temperature, and density, and the deformation of the pressure hull of the glider into consideration. While the variations of aforementioned four parameters influence many physical parameters, including added mass, buoyancy, center of buoyancy (CB), hydrodynamic forces, and torques, their effects on glide velocity appear to be the primary cause for the gradual change of pitch angle observed. To minimize the gradual change of pitch angle, two effective approaches are proposed and validated, namely increasing the lift/drag ratio and decreasing the hydrodynamic torque coefficients.

To improve the observation capability of the ocean, the combination of the static cabled ocean observatory network and the dynamic autonomous underwater vehicle has attracted more and more attention. In this paper, a non-contact docking system for the autonomous underwater vehicle is developed to combine the advantages of the cabled ocean observatory network and the autonomous underwater vehicle. The system includes both acoustic and optical navigation, underwater wireless communication, non-contact power transfer, and monitoring and controlling of the docking system. This docking system was verified by sea trials at depths of 50 and 105 m. The autonomous underwater vehicle successfully docked 11 times, during which non-contact power transfer and wireless communication were completed. The charging power reached 682 W, with a total efficiency of 78.5%, and the efficiency of the power transfer unit was 92%. The rate of wireless data transfer reached 3.1 MB/s. For the first time, this docking system has realized complete homing and docking navigation, wireless communication and charging in a water depth of more than 100 m, thus establishing a good foundation for the combination of the cabled ocean observatory network and autonomous underwater vehicles.

Full scale measurement, model testing and a hybrid CFD method were used to characterize the Underwater Radiated Noise (URN) of a ship at design speed. The CFD method consists of a multiphase Delayed Detached Eddy Simulation and the Ffowcs-Williams Hawkings acoustic analogy. The paper discusses the correlation of the noise spectra with the observed cavitation behavior and compares the measured full scale data with those predicted by the model testing and the CFD method. The comparison shows that the sheet cavity and Tip Vortex Cavitation (TVC) predicted by the model testing are in reasonably good agreement with the full scale observations. The pressure pulses are somewhat higher than the full-scale data. Overall, the predicted URN has a good correlation with the noise spectra obtained from the sea trial. The CFD method shows the potential to resolve turbulence eddy structures in the wake. It captures the dynamic development of sheet cavitation and the collapse and rebound of TVC as observed in the model test and the sea trial, but under-predicts the extent of TVC. The pressure pulses and tonal noise are in close agreement with the respective measured data for the first five orders of blade passing frequency. The method underestimates the broadband noise level in the frequency range 50–112 Hz where the TVC is expected to have an important contribution. The maximum under-prediction in this range is about 28 dB at 72 Hz. At frequencies above 200 Hz, the broadband noise becomes more and more under-predicted with increasing frequency.

This paper is focused on improving the self-awareness of autonomous underwater vehicles (AUVs) operating in unknown environments. A runtime estimation framework is introduced to derive energy usage and navigation performance metrics in the presence of external disturbances, such as slowly varying sea currents. These are calculated by a state-of-the-art nonlinear regression algorithm (LWPR) using measurements commonly available on-board modern AUVs without relying on external sensors or a priori knowledge about the environment. The proposed framework is validated on two vehicles, an IVER3 AUV and a Nessie VII AUV, in the context of real sea trials with no modification required for the AUVs or their missions. Derived metrics are used to estimate the feasibility of underwater missions employing the concept of probability of mission completion (PoMC). If environmental effects modify the vehicle's effectiveness, a mission plan update is performed. This is based on an energy-aware route optimization algorithm that is also introduced in the paper. This algorithm, known as energy-aware orienteering problem (EA-OP), shows a practical usage for the runtime metrics. It allows an AUV to optimize its navigation and to maximize its mission's outcome according to measured performances. Simulation results are also presented for inspection scenarios. These show average improvements of 5%–20% for the mission's outcome when using the proposed strategy in the presence of environmental disturbances.

Due to the convenience of wireless measurement, wireless acceleration sensors have been widely employed to monitor the structural health of mechanical systems. Although wireless sensors for general purpose have been available in the market, a wireless measurement system still needs to be customized for the purpose of its testing condition (measurement distance and time, communication interference and blockage, frequency range, number of sensors installed, etc.). For ships, vibration measurement in a sea trial is required to evaluate the habitability of crews in the accommodation area and study the vibration characteristics of hull structures and shipboard equipment. However, there have been few attempts to utilize wireless sensors in ship vibration measurements due to concerns about a loss of measured signals caused by failures of wireless network. This article presents the wireless measurement system developed to measure ship vibration in a sea trial. System requirements are first defined, and the wireless measurement system is designed and manufactured. Comparative studies between the wireless and wired measurement systems are conducted to confirm applicability of the wireless measurement system in a sea trial. The results in a frequency domain show that both frequency and magnitude match well with each other. Finally, it is concluded that the developed wireless system is reliable as much as the conventional wired system.

To achieve a continuous and accurate navigation solution that avoids interruption, GPS is integrated with dead reckoning techniques such as inertial navigation and speed log. A common approach to obtain a low-cost navigation solution is to utilize a multisensor system consisting of MEMS-based inertial measurement unit (IMU), and the whole system is integrated with GPS. However, these sensors provide significant inherent errors because of their complex error characteristics. Particle filter (PF) is considered as a nonlinear filtering technique to accommodate for arbitrary inertial sensor characteristics and motion dynamics. An adaptive fission PF (AFPF) method is proposed to overcome the shortcoming of sample impoverishment problem and improve particle quality. Moreover, further improvement of the MEMS-based IMU/GPS integration navigation system performance during GPS unavailability is achieved by integrating measurement updates from speed log data. The performance of the proposed GPS/IMU/LOG integration using AFPF is evaluated by sea trial, and results indicate that the AFPF solution outperform three different solutions.

In the paper a modular manoeuvring model of the passenger ferry “Landegode” is built, validated and studied. Global sensitivity analysis based on the variance decomposition is performed to assess the sensitivity of the individual model coefficients on the simulation outcomes. It is found that uncertainty in both hull hydrodynamic coefficients and the steering and interaction coefficients can result in significant uncertainty in the simulation results. The most influential coefficients are defined for the standard IMO manoeuvres. The possibility of identification of “true” values of the coefficients from full scale trials is studied. Such analysis would allow improving empirical predictions of the coefficients. It is found that different combinations of the model coefficients result in similar time-series. This indicates the presence of correlation between the coefficients. Thus, although the identified coefficients can be used for simulations of the ship manoeuvring, it is impossible to identify the single “true” value for each coefficient from these sea trials.

At new ships’ design stages, an important requirement is to grant the structure safety to give to the shipowner and the crew a greater tranquility during ships’ operation. In this scenario, it is relevant to make vibration analysis to identify possible resonance conditions between the structure and the main excitation sources. To trustly predict the ship’s natural frequencies, it is necessary to use a methodology which involves the model to confidently represent the structure and its interaction with the fluid medium. A 3D model that takes into account all the elements with relevance in the calculation of the vertical natural frequencies was developed. To achieve a simpler modeling and short computational effort, a 1D model based on Timoshenko’s beam and thin-walled beam shear flow theories was also developed. Additionaly, the added mass effects were considered using several methods. The methodology was verified with experimental results obtained for a Panamax type ship from which vibration data was acquired during a sea trial. The results showed that both models confidently represent the structure in the vertical vibration analyses, but the computational effort in 1D model is much smaller than that required by the 3D model.