Determining the key characteristics of a ship during the concept and preliminary design phases is a critical and intricate process. In this study, we propose an alternative to traditional empirical methods by introducing a model to estimate the main particulars of diesel-powered Z-Drive harbor tugboats. This prediction is performed to determine the main particulars of tugboats: length, beam, draft, and power concerning the required service speed and bollard pull values, employing Bayesian network and non-linear regression methods. We utilized a dataset comprising 476 samples from 68 distinct diesel-powered Z-Drive harbor tugboat series to construct this model. The case study results demonstrate that the established model accurately predicts the main parameters of a tugboat with the obtained average of mean absolute percentage error values; 6.574% for the Bayesian network and 5.795%, 9.955% for non-linear regression methods. This model, therefore, proves to be a practical and valuable tool for ship designers in determining the main particulars of ships during the concept design stage by reducing revision return possibilities in further stages of ship design.
Determination of coverage and thickness of marine growth is a useful tool for determining structural loads and drags on marine structures and ships. In this work, we present an algorithmic program based on sonar and optical camera measurements, that estimates both the coverage and thickness of marine-fouling on off-shore structures. The marine-fouling composition is estimated using a Deep-Neural Network, trained using supervised methods, which can distinguish between hard/soft fouling species and the background water and structural components. The marine-fouling thickness is estimated using an HF Forward Looking Sonar, which is applied as a sensitive ultrasonic thickness gauge, when combined with a thickness measurement algorithm. Combined the measurements provide a localized estimate of the marine-fouling coverage and loadings across the structural surfaces, which can be used for automatic inspection evaluation and mission planning.
Monitoring methods, such as seabed bottom-towed cameras, sediment grabs, and benthic sledges, have limitations in spatial coverage, cause seabed disturbance, are restricted to soft-bottom substrates, and offer low flexibility for marine seabed monitoring. In this study, we investigate the potential of a non-invasive and simple underwater remotely operated vehicle (ROV) to enhance marine seabed monitoring. A tethered ROV equipped with a GoPro camera was deployed in three areas of Skagerrak at depths from 15-34 m to assess accuracy in species identification and substrate classification identified from still frames. The quality of still frames varied between areas due to turbidity, motion blur, and marine snow, which reduced the number of high-quality frames by approximately 20%. Classification of substrates and taxa identification were possible in the remaining still frames. Two different substrates were detected: sand and stone reef. Stone reefs had a lower occurrence compared to sand. A total of 10 taxa were detected in the two substrate types. The highest abundance was observed in the stone reef substrate compared to the sand substrate. Identification at the species level was limited due to the quality of the still frames, which affected the detectability of morphological traits. This study demonstrates that a widely accessible ROV can be used for marine monitoring. The ROV can be used in different substrates, and still frames provide valuable information on species composition, which can enhance the replicability of monitoring programs.
MoodyMarine is a weakly nonlinear potential flow model for wave-body and mooring simulations with a graphical user interface. In this work we present the extension of the model to deal with constrained multi-body dynamics. By combining different translation and rotation constraints most joints can be modelled. As the constraints are imposed through springs and dampers in the explicit time-stepping algorithm, a slight manual tuning is required to make sure the bodies are constrained properly. Nevertheless, this tuning is shown not to influence the final results. In the paper we compare to existing test cases in literature as well as against experimental data. In all test cases there is a good agreement between the target solutions and MoodyMarine.
Simulating the free decay motion and wave radiation from a heaving semi-submerged sphere poses significant computational challenges due to its three-dimensional complexity. By leveraging axisymmetry, we reduce the problem to a two-dimensional simulation, significantly decreasing computational demands while maintaining accuracy. In this paper, we exploit axisymmetry to perform a large ensemble of Computational Fluid Dynamics (CFDs) simulations, aiming to evaluate and maximize both accuracy and efficiency, using the Reynolds Averaged Navier–Stokes (RANS) solver interFOAM, in the opensource finite volume CFD software OpenFOAM. Validated against highly accurate experimental data, extensive parametric studies are conducted, previously limited by computational constraints, which facilitate the refinement of simulation setups. More than 50 iterations of the same heaving sphere simulation are performed, informing efficient trade-offs between computational cost and accuracy across various simulation parameters and mesh configurations. Ultimately, by employing axisymmetry, this research contributes to the development of more accurate and efficient numerical modeling in ocean engineering.
Harbour vessel emissions are growing concerns in the maritime industry regarding environmental sustainability. Accurate emissions prediction can stand in monitoring and addressing the issue. This study proposes a machine-learning approach using Artificial Neural Network (ANN) for predicting harbour vessel emissions. The approach shows superiority over the bottom-up method introduced by the 4th IMO GHG Study regarding prediction accuracy. Actual emissions data from onboard measurements are used for training ANN models and as references for evaluating the methods. Compared to the bottom-up method, the improvement in error reduction can be up to 30% for predicting nitrogen oxides and 54% for carbon monoxide when only using ship-related factors as input variables. By adding selected meteorological factors in the experiments, the prediction accuracy enhancement can achieve up to 48% for nitrogen oxides and 62% for carbon monoxide. The proposed ANN approach could assist relevant stakeholders in improving emissions prediction and operations optimisation.
As the world collectively looks to technology to salvage what is left of our world to sustain a habitat that can accommodate our way of life, users are increasingly exposed to technological solutions, rarely developed with an offset in their practice. This also holds for the maritime sector in Denmark, where the way of developing technology is limited to the applicability of technological artifacts and can reduce the potential efficiency gains that technologies can introduce. This paper applies qualitative research to show that there is a disconnect between, on the one hand, funders, technology developers, and decision-makers and, on the other hand, technology users and practitioners in the Danish maritime sector. It argued that if technology is to replace or assist any human practice and solve for example the climate crises, then knowledge of users’ practices must be key to developing the technological solutions.
We present a hybrid linear potential flow - machine learning (LPF-ML) model for simulating weakly nonlinear wave-body interaction problems. In this paper we focus on using hierarchical modeling for generating training data to be used with recurrent neural networks (RNNs) in order to derive nonlinear correction forces. Three different approaches are investigated: (i) a baseline method where data from a Reynolds averaged Navier Stokes (RANS) model is directly linked to data from an LPF model to generate nonlinear corrections; (ii) an approach in which we start from high-fidelity RANS simulations and build the nonlinear corrections by stepping down in the fidelity hierarchy; and (iii) a method starting from low-fidelity, successively moving up the fidelity staircase. The three approaches are evaluated for the simple test case of a heaving sphere. The results show that the baseline model performs best, as expected for this simple test case. Stepping up in the fidelity hierarchy very easily introduces errors that propagate through the hierarchical modeling via the correction forces. The baseline method was found to accurately predict the motion of the heaving sphere. The hierarchical approaches struggled with the task, with the approach that steps down in fidelity performing somewhat better of the two.
Numerical models used in the design of floating bodies routinely rely on linear hydrodynamics. Extensions for hydrodynamic nonlinearities can be approximated using eg Morison type drag and nonlinear Froude-Krylov forces. This paper aims to improve the approximation of nonlinear forces acting on floating bodies by using machine learning (ML). Many ML models are general function approximators and therefore suitable for representing such nonlinear correction terms. A hierarchical modeling approach is used to build mappings between higher-fidelity simulations and the linear method. The ML corrections are built up for FNPF, Euler and RANS simulations. Results for decay tests of a sphere in model scale using recurrent neural networks (RNN) are presented. The RNN algorithm is shown to satisfactorily predict the correction terms if the most nonlinear case is used as training data. No difference in the performance of the RNN model is seen for the different hydrodynamic models.
Physical wave basin tests with a focus on uncertainty estimation have been conducted on a fixed sphere subjected to wave loads at Aalborg University as part of the effort of the OES Wave Energy Converters Modeling Verification and Validation (formerly, OES Task 10) working group to increase credibility of numerical modeling of WECs.
The present note defines an idealized test case formulated to accurately represent the physical tests in a simple way. The test case consists of a fixed, rigid sphere half submerged in water subjected to regular waves of three different levels of linearity. The objective of the present note is to allow for numerical tests of the idealized test case.