Cyber-resilience is an increasing concern for autonomous navigation of marine vessels. This paper scrutinizes cyber-resilience properties of marine navigation through a prism with three edges: multiple sensor information fusion, diagnosis of not-normal behaviours, and change detection. It proposes a two-stage estimator for diagnosis and mitigation of sensor signals used for coastal navigation. Developing a Likelihood Field approach, the first stage extracts shoreline features from radar and matches them to the electronic navigation chart. The second stage associates buoy and beacon features from the radar with chart information. Using real data logged at sea tests combined with simulated spoofing, the paper verifies the ability to timely diagnose and isolate an attempt to compromise position measurements. A new approach is suggested for high level processing of received data to evaluate their consistency, which is agnostic to the underlying technology of the individual sensory input. A combined generalized likelihood ratio test using both parametric Gaussian modelling and Kernel Density Estimation is suggested and compared with a detector using only either of two. The paper shows how the detection of deviations from nominal behaviour is possible when the navigation sensor is under attack or defects occur.
Results from Blind Test Series 1, part of the Collaborative Computational Project in Wave Structure Interaction (CCP-WSI), are presented. Participants, with a range of numerical methods, blindly simulate the interaction between a fixed structure and focused waves ranging in steepness and direction. Numerical results are compared against corresponding physical data. The predictive capability of each method is assessed based on pressure and run-up measurements. In general, all methods perform well in the cases considered, however, there is notable variation in the results (even between similar methods). Recommendations are made for appropriate considerations and analysis in future comparative studies.
The design of wave energy converters should rely on numerical models that are able to estimate accurately the dynamics and loads in extreme wave conditions. A high-fidelity CFD model of a 1:30 scale point-absorber is developed and validated on experimental data. This work constitutes beyond the state-of-the-art validation study as the system is subjected to 50-year return period waves. Additionally, a new methodology that addresses the well-known challenge in CFD codes of mesh deformation is successfully applied and validated. The CFD model is evaluated in different conditions: wave-only, free decay, and wave–structure interaction. The results show that the extreme waves and the experimental setup of the wave energy converter are simulated within an accuracy of 2%. The developed high-fidelity model is able to capture the motion of the system and the force in the mooring line under extreme waves with satisfactory accuracy. The deviation between the numerical and corresponding experimental RAOs is lower than 7% for waves with smaller steepness. In higher waves, the deviation increases up to 10% due to the inevitable wave reflections and complex dynamics. The pitch motion presents a larger deviation, however, the pitch is of secondary importance for a point-absorber wave energy converter.
ECOPRODIGI (2017-2020) is an Interreg Baltic Sea Region flagship project, which links research organisations, enterprises, associations and business support organisations. Altogether, 21 partners jointly investigate the most critical eco-inefficiencies in maritime processes in the Baltic Sea Region as well as develop and pilot digital solutions for improving the eco-efficiency by focusing on three specific cases: 1) digital performance monitoring of vessels, 2) cargo stowage optimisation at ports and 3) process optimisation at shipyards. Furthermore, looking towards the future, the project partners, on one hand, create a digitalisation roadmap and training modules for future decision makers in the maritime industry but also reach out to policymakers to engage them in discussion regarding how they can support the digital change. This report provides an overview of the project and main findings achieved to date, describes the main eco-inefficiencies identified and presents the potential of digital technologies and new concepts for improving them. Also, as the current digital transformation relates to the way how changes are managed in organisations, this report presents the main challenges and requirements identified in the process of moving towards more digitalised business operations. Finally, the last section looks at the maritime sector from a broader perspective and provides some ideas about the most likely future developments. The main findings of the project so far indicate that major improvements in eco-efficiency can be carried out in the maritime industry. They can be summarised as follows: 1) In the first case, ‘digital performance monitoring’, the project partners estimate, for instance, that fuel consumption and emissions can potentially be reduced by 2-20% based on data and analysis from distinct ship segments, routes and their baseline situations. The reductions are possible to achieve by taking such actions as capitalising on the latest digital technologies, utilising and analysing real-time operational data and vessel performance, anticipating operating conditions and maintenance of the ship and its components, changing working methods and improving practices as well as placing a focus on the training of personnel. 2) In the second case, ‘cargo stowage optimisation’ the project partners identified a set of eco-efficiency bottlenecks in the cargo stowage processes at ports that can be subject to improvement. The use of advanced digital technologies can contribute to more efficient utilisation of vessels and terminal operations. The port stays can be reduced, and, thereby, vessels can sail more slowly and reduce fuel consumption and emissions. Moreover, when stability calculations improve due to further digitalisation of cargo unit data, the ship can be loaded more optimally and the amount of ballast water can potentially be decreased without compromising safety, which again reduces fuel consumption on the sea leg. It is estimated that fuel consumption and emissions can potentially be reduced by 2-10% per route and ship and that additional benefits can be gained on the landside due to future digital decision support tools applied for the end-to-end stowage process. In addition, improved cargo unit pick up time estimates can be provided to customers waiting for the cargo to be handled at port, whereby the service improves. 3) In the third case, ‘process optimisation at shipyards’, improved situational awareness and process management, including the use of new technologies, such as 3D and solutions for managing the complex supply chain, have potential for improving the shipyard processes aimed at increased eco-efficiency. For example, in block building phase 3D technology reduces lead-time and potentially saves hundreds of man-hours in rework due to the fact that more efficient processes and proactive actions are enabled.
This PhD theis focuses on identifying the opportunities and challenges that on-board maintenance and practical operation of vessels poses in the development of autonomous ships. Inspired by the rapid development of autonomous vehicles considerable effort and interest is now invested in the development of autonomous ships. So far however, most of the research has focused on the legal aspect of unmanned vessels and on developing a system enabling a vessel to operate within the maritime collision regulation without human interaction. Specifically, the theisi looks into three research questions: (1) How is autonomous technology going to affect the workload required for operating and maintaining modern cargo vessels? (2) How is autonomous technology going to affect the operational patterns of the vessels? And (3) How is autonomous technology going to affect the reliability and utilization rate of the vessels?
The study is planned in cooperation between Svendborg International Maritime Academy (SIMAC) and University of Southern Denmark.
Increasing developments in the offshore energy sector have led to demand for robotics use in inspection, maintenance, and repair maintenance tasks, particularly for the service life extension of structures. These robots experience slippage due to varying surface conditions caused by environmental factors and marine growth, leading to inconsistent traction forces and potential mission failures in single-drive systems. This paper explores control strategies and mechanical configurations both in simulation and on the physical industrial robot to mitigate slippage in offshore robotic operations, improving reliability and reducing costs. This study examines mechanical and control modifications such as multi-wheel drive (MWD), PID velocity control, and a feedback-linearized slip control system with an individual wheel disturbance observer to detect surface variations. The results indicate that a 3 WD setup with slip control handles the widest range of conditions but suffers from high control effort due to chattering effects. The simulations show potential for slip control; practically, challenges arise from low sampling rates compared to traction changes. In real-world conditions, a PID-controlled MWD system, combined with increased normal force, achieves better traction and stability. The findings highlight the need for further investigation into the mechanical design and sensor feedback, with the refinement of slip control strategies and observer design for the offshore environment.
Savonius hydrokinetic turbines (SHTs), categorized as emerging cyclic-type wave energy converters (WECs), have demonstrated notable potential in achieving elevated energy conversion efficiency and consistent power output. This performance is particularly observed when operating under the initial phase-locked strategy (IPLS), marking a significant advancement in the realm of wave energy harvesting. However, a thorough exploration of the influences stemming from wave conditions and turbine design remains an area that warrants further investigation for advancing the performance of SHT-WECs under the proper operational strategy. This study undertakes an exhaustive analysis of geometric parameters, encompassing turbine diameter, blade number, and thickness. An experiment-validated numerical model based on the unsteady two-phase Reynolds-averaged Navier-Stokes equations is adopted in the research. Comprehensive investigations include analyzes of flow fields around the turbine, pressure distributions on blade surfaces, and dynamic torque variations. These analyzes serve to elucidate the variation rules of hydrodynamic characteristics and their influential mechanisms. The results highlight the notable impact of the proposed "relative-short wavelength impact" on the performance of SHT-WECs operating under IPLS conditions. Notably, no significant impact is observed when the relative wavelength exceeds 17. Optimal performance is achieved with the thinnest and two-bladed turbine configuration. Moreover, optimizing the turbine diameter significantly enhances SHT-WEC conversion efficiency, with the attained maximum value reaching approximately 18.6%. This study offers a concise guideline for designing turbine diameters in alignment with specific wave conditions.
Abstract In the quest for a numerical method for surface waves and wave-induced effects applicable when linear or weakly nonlinear methods are insufficient, a three-dimensional numerical wave tank assuming fully-nonlinear potential-flow theory is proposed. When viscous-flow effects, breaking waves or other violent flow-phenomena are not of primary importance, potential-flow methods may have similar capability in capturing the involved physics as Navier-Stokes solvers while being potentially more accurate in handling wave-propagation mechanism and more computationally efficient. If made sufficiently accurate, efficient and numerically robust, fully-nonlinear potential flow models can therefore represent a powerful tool in the study of ocean waves and their interaction with marine structures, which is the main motivation behind the present work. The governing Laplace equation for the velocity potential is solved using the harmonic polynomial cell method, which is a field method giving high-order accuracy provided that the cells used to describe the water domain have no stretching or distortion. This can only be achieved in a grid with cubic cells, which leads to poor numerical efficiency unless measures are introduced to refine the grid locally. Here, to improve the efficiency using strictly cubic cells, an adaptive grid refinement technique is introduced. It is shown that this has the ability to improve the computational speed with a factor of up to 20 without sacrificing accuracy. Numerical results are shown to be in good agreement with highly accurate nonlinear reference solutions for regular and irregular waves of various steepness up to the limit of theoretical wave breaking. For long-crested irregular waves, significant discrepancies with a second-order theory for the crest-height distribution are identified, while the second-order theory appears to provide a better description of the crest height for the single short-crested irregular sea state simulated. Having demonstrated that the proposed numerical method accurately models nonlinear wave phenomena up to the limit of wave breaking, future work should seek to implement wave-body interaction capabilities. The adaptive grid refinement technique, which refines the grid dynamically depending on the position of boundaries of interest, is developed with this application in mind. Except from providing a robust way of dealing with wave-body intersection points, extending the method to account for wave-body interactions should therefore involve limited difficulty.
In near coast navigation, buoys and beacons convey essential information about dangers. At night-time, selected buoys send out individual blink-sequences that are marked in sea charts. International regulations require that navigation officer on watch makes visual confirmation of objects and their type in order to navigate safely. With rapid developments of highly automated vessels, this duty needs be carried out by algorithms that detect and locate objects without human intervention. At night-time, this requires algorithms that decode blink sequences and are able to classify from this information. The paper investigates this problem and suggests an algorithm that solves the problem. Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU) are developed for classification. A dedicated architecture is suggested that includes both temporal and color decoding to obtain unique precision. We demonstrate how networks are trained on synthetically generated data, and the paper shows, on real-world data, how the suggested approach yields 100.0% accurate results on 44 real-world recordings while being robust to inaccuracy in actual blink sequences. Comparison with baseline signal processing and with a recent state-of-the-art 3D CNN model shows that the new blink-sequence classifier outperforms alternative algorithms. A showcase of the results of this work is available in this video: https://youtu.be/KEi8qNnKV2w.
Offshore pipelines and structures require regular marine growth removal and inspection to ensure structural integrity. These operations are typically carried out by Remotely Operated Vehicles (ROVs) and demand reliable and accurate feedback signals for operating the ROVs efficiently under harsh offshore conditions. This study investigates and quantifies how sensor delays impact the expected control performance without the need for defining the control parameters. Input-output (IO) controllability analysis of the open-loop system is applied to find the lower bound of the H-infinity peaks of the unspecified optimal closed-loop systems. The performance analyses have shown that near-structure operations, such as pipeline inspection or cleaning, in which small error tolerances are required, have a small threshold for the time delays. The IO controllability analysis indicates that off-structure navigation allow substantial larger time delays. Especially heading is vulnerable to time delay; however, fast-responding sensors usually measure this motion. Lastly, a sensor comparison is presented where available sensors are evaluated for each ROV motion’s respective sensor-induced time delays. It is concluded that even though off-structure navigation have larger time delay tolerance the corresponding sensors also introduce substantially larger time delays.