A comprehensive method which determines the most efficient propeller blade shapes for a given axisymmetric hull to travel at a desired speed, is presented. A nonlinear optimization method is used to design the blade, the shape of which is defined by a 3-D B-spline polygon, with the coordinates of the B-spline control points being the parameters to be optimized for maximum propeller efficiency, for given effective wake and propeller thrust. The performance of the propeller within the optimization scheme is assessed by a vortex-lattice method (VLM). To account fully for the hull/propeller interaction, the effective wake to the propeller and the hull resistance are determined by analyzing the designed propeller geometry by the VLM, coupled with a Reynolds-Averaged Navier-Stokes (RANS) solver. The optimization method re-designs the optimum blade with the updated effective wake and propeller thrust (taken to be equal to the updated hull resistance), and the procedure continues until convergence of the propeller performance. The current approach does not require knowledge of the wake fraction or the thrust deduction factor, both of which must be estimated a priori in traditional propeller design. The method is applied for a given hull to travel at a desired speed, and the optimum blades are designed for various combinations of propeller diameter and RPM, in the case of open and ducted propellers with provided duct shapes. The effects of the propeller diameter and RPM on the designed propeller thrust, torque, propeller efficiency, and required power are presented and compared with each other in the case of open and ducted propellers. The present approach is shown to provide guidance on the design of propulsors for underwater vehicles, and is applicable to the design of propulsors for surface ships.

It is important to increase the productivity of every shipyard. Visualizing the actual work status during any industrial activity is essential. Work observation as one of the methods of industrial engineering has been applied in various fields in shipyards in Japan to increase productivity. However, current work observation requires both time and labor, and in some cases, shipyards hesitate to implement work observation. The aim of this study is to develop a methodology that uses deep neural networks to reduce the disadvantages of current work observation approaches while identifying work tasks and the accuracy of this observation.

The ability to extract quantitative flow information from photographic images of the velocity field using Particle Image Velocimetry (PIV) is a powerful alternative to the more traditional invasive or integrated method techniques. The usage of PIV allows the complete characterization of the flow field, and not just at discrete points. Additionally, with PIV, it is possible to predict the hydrodynamic characteristics of a lifting body without measuring the forces and moments acting upon it. In this paper, the hydrodynamic performance of a NACA 0018 airfoil was determined by analyzing the flow kinematics from a 2D-2C (two-dimensional, two-component) PIV data set. The motivation for this work was to provide a canonical study to show that laser optical measurement techniques such as PIV, can be an attractive alternative to dynamic force testing. The hydrodynamic performance evaluated using PIV data was compared to the computational program XFOIL to assess the validity of the results. The analytical drag force prediction was carried out using the Von Kármán Momentum Integral approach for a flat plate and the Squire-Young boundary layer method as an improved method, whereas the analytical lift force prediction calculation was based on the Kutta-Joukowski theorem. The results show reasonable agreement with the numerical prediction tool XFOIL and they follow the expected trends across all operating conditions. These findings suggest that this methodology might be expanded to conduct hydrodynamic analyses on more complex geometries such as hydrofoils, turbines, propulsors, fin stabilizers, rudders, and other control surfaces using flow kinematics data from PIV.

The U.S. 2021 National Defense Authorization Act (NDAA) requires one main propulsion system and one electrical generation and distribution system of the Navy’s Large Unmanned Surface Vessel to be qualified by a Senior Technical Authority. To address this, related Navy offices and the American Bureau of Shipping (ABS) developed a process for a Navy-specific qualification for USV program equipment and systems to support verification of both design and reliability. This paper presented includes detailed discussions of the process increments (Product Design Assessment, Technology Readiness, Reliability Qualification) and a review of the full process using an example.

Low- and non-ice-class ship-ice interactions are modelled with a shared-energy approach, which typically models the internal mechanics with nonlinear finite element methods. For applications like the preliminary design phase and quick operational assessments of the ship’s structural capabilities, a finite element shared-energy approach can be time consuming and information intensive, therefore, an analytical share-energy algorithm is proposed. The proposed algorithm applies the upper bound energy methodology by equating the external collision energy, determined with the Popov collision model (Popov, et al., 1967), to the sum of the internal ice and structural response energies. The distribution of the internal energy, between the ice and the structure, is determined by iterating through possible shared contact forces until the sum of the internal response energies equals the external energy introduced into the system. The ice-crushing energy is modelled with Daley’s (1999) energy based ice collision force models, and the internal structural strain energy is modelled through a combination of classical beam theory and design of experiments methodology. The proposed model is benchmarked against a finite element ice wedge-ship grillage structure interaction.

After more than one hundred years the Titanic is still probably the most remembered ship in the world. This paper briefly discusses the history of the Titanic from why the White Star Line decided to build the Olympic class ships through the recently signed treaty protecting the ship. It is shown that many of the design features of the ship were far ahead of the rest of the industry but that some compromises were against the naval architect’s desires. A number of myths concerning the ship are dispelled. The circumstances leading up to the collision with the iceberg and the sinking of the ship are examined followed by an analysis of the sinking; the discovery of the ship; and finally after almost thirty-five years, a treaty to protect the ship.

The paper describes the implementation of state-of-the-art “Industry 4.0” methods and tools, a holistic ship design optimization and modular production methods, as well as advanced battery technologies to enable a fully electrical, fast zero-emission waterborne urban transport. The design of a fast catamaran passenger ferry demonstrator planned for operation as a waterborne shuttle in the Stavanger/Norway area and of a replicator for operation at Thames River/London are elaborated, including infrastructural issues for their operation. The presented research is in the frame of the H2020 funded project “TrAM – Transport: Advanced and Modular” (www.tramproject.eu)

Emerging heterogeneous computing, computing at the edge, machine learning and AI at the edge technology drives approaches and techniques for processing and analysing onboard instrument data in near real-time. The author has used edge computing and neural networks combined with high performance heterogeneous computing platforms to accelerate AI workloads. Heterogeneous computing hardware used is readily available, low cost, delivers impressive AI performance and can run multiple neural networks in parallel. Collecting, processing and machine learning from onboard instruments data in near real-time is not a trivial problem due to data volumes, complexities of data filtering, data storage and continual learning. Little research has been done on continual machine learning which aims at a higher level of machine intelligence through providing the artificial agents with the ability to learn from a non-stationary and never-ending stream of data. The author has applied the concept of continual learning to building a system that continually learns from actual boat performance and refines predictions previously done using static VPP data. The neural networks used are initially trained using the output from traditional VPP software and continue to learn from actual data collected under real sailing conditions. The author will present the system design, AI, and edge computing techniques used and the approaches he has researched for incremental training to realise continual learning.

This paper analyzes weather data recorded by typical oceanographic buoys using data analytics and regression techniques. Time series data over a period of more than four decades (1976 – 2020) are reviewed and profiled. A set of key variables including seasonality, wind speed, wind direction, wave period, wave direction, etc., are screened from the buoy measurements to build a predictive model based on multiple linear regression for significant wave height prediction. A sensitivity analysis is then conducted for the available weather window corresponding to specified threshold operational limits of the significant wave height. Key insights are presented along with suggestions for future work to assist marine operators in planning and derisking offshore operations. Utilizing the algorithms and workflows presented in this paper, a user can increase confidence in weather window prediction, and develop safer, efficient offshore operation plans.

In the current work, a new error evaluation methodology is introduced based on error analysis in ship production with reverse engineering data. The aim is to determine the errors and prevent or reduce the occurrence in other projects. First step is to compose a database of the errors; then, group the similar errors and calculate the Error Priority Number (EPN) by the evaluation of the predetermined criteria. The radar diagrams, which are suitable for representing a number of parameters having the same variables, were used to present the error groups in a simple way. The error groups were created on the diagram with the scores taken from the specific criteria. With the aid of the radar diagram, valuable information is given by presenting similarities and dissimilarities of these errors with other error groups. After examining the radar diagrams and evaluating the results, the cause and effect diagrams were prepared for these error groups from the field experts. Thus, the methodology should be customized for the shipyard to ensure maximum efficiency.

For prismatic planing vessels, attitude parameters of deadrise, heel, and trim angle affect hydrodynamic maneuver forces on the hull as shown in experimental data. These effects can be exploited to achieve maneuvering objectives. Demonstrated through numerical model-based simulations, asymmetrical mass distribution on low deadrise hulls affects heel and induces yaw moments that turn the vessel without the use of traditional aft steering mechanisms. On high deadrise hulls, maneuvering by use of a combined rudder and asymmetrical mass results in ~10% drag savings and improved stability in comparison with rudder alone.

Offshore wind energy is developing rapidly in the United States, particularly off the East coast, which has an extensive continental shelf and where the water depths are such that wind turbines founded directly on the seabed are the most attractive. Of the various foundation configurations that are feasible, it is the monopile that is expected to be most widely used. Another feature of the US East coast is that it is hurricane-prone region, which poses some unique challenges to designers. This paper summarizes research work undertaken to investigate how the role of modern optimization techniques can contribute to the design of such installation and does not establish any realistic project related implications. The project addresses the design of a 15 MW turbine, which represents the upper limit capacity of currently available wind turbines.

Since 2011, the Caribbean has been invaded by large banks of “sargassum,” which increase every year and have become an environmental threat and a health problem to those living in the extensive coastal areas affected. We identified different systems around the world dedicated to collecting floating marine debris. Most solutions are not suited to the magnitude and nature of the pollution caused by the sargassum. The limitations we observed are either the loading capacity or limitations in the disposal of the collected sargassum debris. A conceptual solution is proposed in this paper for further review, namely, the “CSC Integrated System”.

This paper presents the concept and design of a tanker powered by, and carrying, liquid hydrogen (LH2). Beyond the realization of a complete reduction in carbon emissions, the concept is closely related to the authors’ proposal for a rethinking of ship operation with a substitution of Just-in-Time (JIT) by Warehouse-in-Transit (WIT). Ways to fund a LH2 tanker is also reviewed and suggested.

The importance of the blue economy is growing. A key element of this is the transition from fossil-based energy to greener alternatives such as offshore wind, marine renewables and other sources of energy. The ocean has been a source of food for millennia but newer, more innovative methods, of offshore cultivation are being developed and deployed. The engineering experience gained in offshore oil and gas industry is finding application in these examples of engineering for the blue economy. The trends are outlined together with the challenges faced by the engineering community.

This paper focuses on the effect of the sea conditions and ship motions on ship operations and combatant activities. Readers interested in an exhaustive discussion of Bismarck, her creation and these epic battles in never before seen detail are encouraged to consult Garzke, 2019. The sea conditions were reported in the ship’s logs and were used to drive modern seakeeping tools to evaluate the probable ship motions. From the ship motions we have calculated the effect that these would have on the ability to train and elevate the guns for range.

Ship designers use experience and standards to guide their solutions to technical problems. Automation and optimization algorithms, despite their limited exposure in the maritime industry, could reduce this effort and help make trade-offs between new technologies. Marine electrical systems are procedurally designed with repetitive calculations, while marine power plants are designed by best practices and time-intensive trade-off studies. We developed a marine engineering design tool that aids with electrical system and power plant design, combining automation and optimization to minimize engineering time. This tool could prove valuable for the concept phase of ship design, allowing engineers to draft and evaluate options quickly and easily. We demonstrated that automation and optimization are applicable to complex design problems in the marine industry and accessible at the undergraduate level.

We addressed the problem of estimating man-hours to fabricate a curved hull using cold and thermal forming methods and analyzed the current forming methods and identified parameters affecting the forming time. Subsequently, we obtained mathematical formulae that relate geometric parameters (curvedness and area), with a forming parameter (forming time), through experiments and simulations to estimate the forming time when a curved plate is provided as an input. We tested the formulae using examples and analyzed the results to verify their validity. Results show that the derived relations can be used for man-hour prediction of curved hull plates in shipbuilding.

This paper provides the results of model tests in ice to evaluate the performance of the USCG Mackinaw Icebreaker which equipped with two podded propulsors and compares with the data obtained from the full-scale ice trials. The objective of this collaborative model test program between the NRC and USCG was to understand the capability and limitation of the model tests with podded vessels in ice. As a result, the model tests showed a good agreement with attainable speeds at selected power levels but an overestimation of the ice resistance by an average of 7 % (from -10 % to 25%). Further discussion of podded icebreaker performance including turning circle tests in ice is provided and future work is proposed. This paper also provides a discussion of two different flexural strength test methods, which are simple beam and cantilever beam tests.

With increasingly inexpensive computational resources, big data and machine learning are more available and approachable than ever before. Many industries are moving towards including big data analytics in their current processes and having success using machine learning techniques to improve existing systems and methods. The drive within the naval architecture community over the last couple of decades towards set-based design and design space exploration has resulted in increasingly large and more readily obtainable sets of ship design data. This research focuses on coupling the state-of-the-art in machine learning techniques with the increasingly available ship design data in order to improve the hull form design process.