Unmanned surface vehicles (USVs) are increasingly appealing for gathering metocean data, including directional sea spectra. This paper presents new developments towards estimating the response amplitude operators (RAOs) of surface vehicles equipped with inertial sensors. The novel approach undertakes the data-driven estimation of vehicle models of the wave-induced heave, roll, and pitch motion dynamics, as required to perform subsequent seakeeping computations. Specifically, a genetic algorithm executes the calibration of available closed-form RAOs for a simplified geometry. The algorithm makes a population of model-fitting parameters evolve towards minimising discrepancies between the predicted and measured response spectra in stationary operational conditions. Trust in the model is eventually increased by screening and merging the best-fitting solutions. Resulting response predictions using high-resolution spectral wave data for the AutoNaut USV demonstrate satisfactory accuracy and robustness in heave and pitch but a worse fidelity in roll, thereby motivating follow-up studies to improve the estimation of roll RAOs.
This study addresses a critical gap in environmental assessments by focusing on petrochemical port operations, an area traditionally overlooked in life cycle assessments (LCAs) of material supply chains. This study investigates various methods of loading for 22 petrochemical products i.e., gas, liquid, container, tanker, and bulk loading; at the biggest petrochemical port in the world situated in the Persian Gulf with a loading capacity of 35 MMt/yr. Twelve scenarios were developed to enhance environmental efficiency based on hotspots defined in LCAs of port loading operations of petrochemicals in their present state. Scenarios 1 through 5 consider electricity savings of 2%–10%, scenarios 6 through 10 consider renewable photovoltaic energy mix of 10%–50%, and scenarios 11 and 12 consider no flaring and rejection of ash waste from ships.
To prioritize these scenarios based on environmental efficiency gains, a comprehensive LCA-GRM hybrid model has been introduced. This integrated model combines life cycle assessment and gray relational modeling, providing a robust framework for evaluating and ranking the scenarios. The Best Worst Method (BWM) is implemented for weighing multiple environmental criteria, contributing to informed decision-making.
The findings underscore the substantial impact of electricity consumption and gas flaring in petrochemical port operations, prompting the identification of the 'no flaring' scenario (S11) as the most preferred option. Implementing this scenario could lead to significant reductions in climate change impacts (22.14%), ozone formation and human health impacts (16.73%), and photochemical oxidant formation (15.98%).
The study's significance lies in emphasizing the environmental implications of port operations and urging policymakers to integrate port impacts into broader supply chain assessments. We advocate for targeted strategies to enhance electricity efficiency and reduce gas flaring in petrochemical ports, aligning with global sustainability goals. The Comprehensive LCA-GRM hybrid approach offers valuable insights for decision-makers involved in the global transportation of goods through ports.
Gathering real-world high-quality data from underwater environments is cost-intensive, as is labeling this data for machine learning. Given this, synthetic data represents a possible solution that delivers ground-truth training data. Nevertheless, rendering and modeling of underwater environments are challenging due to several factors, including attenuation, scattering, and turbidity. The focus of this study is on the creation of a simulated underwater environment constructed for the purposes of simulating marine growth on offshore structures. The main requirement is the creation of renderings of sufficient quality and quantity with respect to the representation of marine-species distribution and intra-class variation, and sufficiently accurate recreation of lighting and turbidity (Jerlov water type) conditions underwater. Underwater rendering has been implemented using Blender, with marine growth from 2D/3D scanned and hand-modelled entities combined with a CAD model of an actual offshore installation. The proposed approach provides for the generation of synthetic images usable for training computer vision models in marine-growth inspection applications as well as other related underwater applications. This has been demonstrated in a case study, wherein the utility of the rendered dataset has been briefly demonstrated in a neural network marine-growth segmentation task. The produced renderings are available as a dataset of 1038 scene renders, using varying poses and randomized representative marine growth; each render includes RGB images, ground-truth segmentation masks, water-free RGB images, and depth information. In future work, the expansion with additional species and objects in other oceanic and coastal environments is envisioned.
Offshore wind energy production has seen a significant expansion in the past decade and has become one of the most important maritime activities. However, the implications of offshore wind farm expansion for maritime security have, so far, received sparse attention in the literature. In this article we conduct one of the first thorough analyses of the security of offshore wind farms and related installations, such as underwater electricity cables, energy islands, and hydrogen plants.
Objective: To study enablers of bullying and harassment in the work environment on board ships.
Method: A qualitative sub-study embedded within a large population-based cross-sectional survey of the prevalence of bullying and harassment on ships in the Danish merchant fleet. The sub-study is based on in-depth semi-structured interviews and responses to open-ended questions in the survey questionnaire. In total, 32 participants were interviewed face-to-face or online. The data was imported into NVivo and coded thematically.
Results: Four themes emerged as enablers specific to the work environment on a ship: The first theme, Isolation, covers how the crew -as a group, is physically isolated from the world outside the vessel, but also that new crew members can be challenged when trying to become established inside the groups on board. The second theme, Hierarchy, encompasses how challenging the existing hierarchy based on rank is not tolerated and that the hierarchy is often maintained through bullying and harassment. The third theme, Crewing, includes how diversity related to e.g., culture, language, gender, or age/experience may create another form of hierarchy with the same negative consequences as the hierarchy based on formal rank. The fourth theme, Leadership, covers how leadership can enable both a good and an unacceptable work environment related to bullying and harassment. Conclusion: Bullying and harassment pose serious challenges to the work environment on ships. Four themes were identified as enablers of bullying and harassment. Some are a premise, but others may be improved through continued awareness and focused educational initiatives.
This paper introduces an interactive method which aims to map out perspectives on the state of ecosystem-based approaches (EbA) in marine spatial planning (MSP). MSP meets a need for organizing the allocation of space to maritime uses. MSP should also control and limit the pressures induced by these activities on marine ecosystems. To evaluate how EbA is actually applied, this research proposes a participatory method to assess the effectiveness of current EbA practices within the MSP process using perceptual maps. The mapping focuses on two dimensions: relevance and implementation of a set of 13 key elements of EbA. The method was tested on a sample of marine planners from ongoing MSP processes in northern European sea basins. The study shows that perceptual maps provide four main benefits: participatory, visualization capacity, qualitative and quantitative applicability, and easy to use. This tool undoubtedly has the potential to illustrate the state of integration of EbA in MSP and highlight the priority issues to develop in future plans, and consequently to provide keys to revise marine plans in a way that better takes into account EbA principles.
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.
The present paper explores how industrial symbiosis emergence unfolds within port industrial areas through a multiple case study that focuses on actors, platforms for collaboration, and content of stakeholder processes. Drivers for industrial symbiosis emergence are identified, and future lines of research are proposed.
Port clusters are expected to play a significant role in the transition towards a circular economy, both at the level of facilitating regional and global transport within circular production chains, as well as hosting circular activities in port areas. There is strong evidence that significant investments in the circular economy (CE) are being made in port areas, albeit without much knowledge on their impacts. To ensure an efficient use of port resources in view of this transition, these impacts should be adequately monitored. Research on circular economy indicators for ports is still in an exploratory stage, characterized by an absence of in-depth research on the development of port-related circular economy indicators. This paper focuses on the development of a comprehensive set of relevant and feasible CE indicators, which aim to support port managing bodies (PMBs) as well as port stakeholders to monitor the CE transition taking place. Through multimethod qualitative research, including content analysis, focus groups, a gap analysis and a qualitative survey, an actionable list of CE 12 indicators for ports was developed. Seven of which are highly feasible and five of which have medium feasibility in terms of stakeholder relevance and ease of implementation. Findings related to (1) the overall limited CE ambition levels of PMBs and (2) the difference in the values of some indicators for different port typologies are also discussed. The value of this study for practitioners lies in providing them with an actionable set of KPIs which can support their efforts and communication related to their CE transition.