Search Results (602)

The complex topology of shipboard DC microgrids and the strong coupling between positive and negative poles during faults pose significant challenges for faulted-pole identification, especially under high-resistance conditions. To address these issues, this paper proposes a novel faulted-pole identification method based on S-Transformation and convolutional neural networks (CNNs). Single-ended voltage and current measurements from the generator side are used to generate time–frequency spectrograms via S-Transformation, which are then processed by a CNN trained to classify the faulted pole. This approach avoids reliance on complex threshold settings. Simulation results on a representative shipboard DC microgrid demonstrate that the proposed method achieves high accuracy, fast response, and strong robustness, even under high-resistance fault scenarios. The method significantly enhances the selectivity and reliability of fault protection, offering a promising solution for advanced marine DC power systems. Compared to conventional fault-diagnosis techniques, the proposed model achieves notable improvements in classification accuracy and computational efficiency for line-fault detection. Full article

The accurate prediction of significant wave height presents a complex yet vital challenge in the fields of ocean engineering. This capability is essential for disaster prevention, fostering sustainable development and deepening our understanding of various scientific phenomena. We explore the development of a comprehensive predictive methodology for wave height prediction by integrating novel Takagi–Sugeno–Kang fuzzy models within a multiresolution analysis framework. The multiresolution analysis emerges via wavelets, since they are prominent models characterised by their inherent multiresolution nature. The maximal overlap discrete wavelet transform is utilised to generate the detail and resolution components of the time series, resulting from this multiresolution analysis. The novelty of the proposed model lies on its hybrid training approach, which combines least squares with AdaBound, a gradient-based algorithm derived from the deep learning literature. Significant wave height prediction is studied as a time series problem, hence, the appropriate inputs to the model are selected by developing a surrogate-based wrapped algorithm. The developed wrapper-based algorithm, employs Bayesian optimisation to deliver a fast and accurate method for feature selection. In addition, we introduce a projection step, to further refine the approximation capabilities of the resulting predictive system. The proposed methodology is applied to a real-world time series pertaining to spectral wave height and obtained from the Poseidon operational oceanography system at the Institute of Oceanography, part of the Hellenic Center for Marine Research. Numerical studies showcase a high degree of approximation performance. The predictive scheme with the projection step yields a coefficient of determination of $0.9991$, indicating a high level of accuracy. Furthermore, it outperforms the second-best comparative model by approximately $49\%$ in terms of root mean squared error. Comparative evaluations against powerful artificial intelligence models, using regression metrics and hypothesis test, underscore the effectiveness of the proposed methodology. Full article

Wireless power transfer (WPT) systems for autonomous underwater vehicles (AUVs) are gaining traction in marine exploration due to their operational convenience, safety, and flexibility. Nevertheless, disturbances from ocean currents and marine organisms frequently induce rotational, axial, and air-gap misalignments, significantly degrading the output power stability. To mitigate this issue, this paper proposes a novel reconfigurable WPT system utilizing coaxial antipodal dual DD (CAD-DD) coils, which strategically switches between a detuned S-LCC topology and a detuned S-S topology at a fixed operating frequency. By characterizing the output power versus the coupling coefficient (P-k) profiles under both reconfiguration modes, a parameter design methodology is developed to ensure stable power delivery across wide coupling variations. Experimental validation using a 1.2 kW AUV charging prototype demonstrates remarkable tolerance to misalignment: ±30° rotation, ±120 mm axial displacement, and 20–50 mm air-gap variation. Within this range, the output power fluctuation is confined to within 5%, while the system efficiency exceeds 85% consistently, peaking at 91.56%. Full article

As the generation of Municipal Solid Waste (MSW) has exponentially increased, this poses a challenge for waste managers, such as municipalities, to effectively control waste streams. If waste streams are not managed correctly, they negatively contribute to climate change, marine plastic pollution and human health effects. Therefore, waste streams need to be identified, categorised and valorised to ensure that the most effective waste management strategy is employed. Research suggests that a more efficient process of identifying and categorising waste at the source can achieve this. Therefore, the aim of the paper is to identify the state of research of AI-powered drones in identifying and categorising waste. This paper will conduct a systematic review and meta-analysis on the application of drone technology integrated with image sensing technology and deep learning methods for waste management. Different systems are explored, and a quantitative meta-analysis of their performance metrics (such as the F1 score) is conducted to determine the best integration of technology. Therefore, the research proposes designing and developing a hybrid deep learning model with integrated architecture (YOLO-Transformer model) that can capture Multispectral imagery data from drones for waste stream identification, categorisation and potential valorisation for waste managers in small-scale environments. Full article

Non-autoclave-cured wet layup composites are used extensively in applications ranging from civil and marine infrastructure to offshore components and in transmission power systems. In many of these applications the composites can be exposed to elevated temperatures for extended periods of time. While residual tensile characteristics have been used traditionally to assess the integrity of the composite after a thermal event/exposure, it is emphasized that fiber-dominated characteristics such as longitudinal tensile strength are not affected as much as those associated with shear. This paper reports on the investigation of shear related characteristics through off-axis and short-beam shear testing after exposure to temperatures between 66 °C and 260 °C for periods of time up to 72 h. It is shown that the use of shear test results in conjunction with tensile tests enables better assessment of the competing effects of postcure, which results in an increase in performance, and thermal degradation, which causes drops in performance. Off-axis-to-tensile strength and short-beam shear strength-to-tensile strength ratios are used to determine zones of influence and mechanisms. It is shown that temperatures up to 149 °C can lead to advantageous postcure related increases in performance whereas temperatures above 232 °C can lead to significant deterioration at time periods as low as 4 h. The use of shear tests is shown to provide data critical to performance integrity showing trends otherwise obscured by just the use of longitudinal tensile tests. A phenomenological model developed based on effects of the competing mechanisms and grouping based on phenomenon dominance and temperature regimes is shown to model data well providing a useful context for deign thresholds and determination of remaining structural integrity. Full article

Ocean energy is an abundant, eco-friendly, and renewable energy resource that is useful for powering sensor networks connected to the maritime Internet of Things (MIoT). These sensor networks can be used to measure different marine environmental parameters that affect ocean infrastructure integrity and harm marine ecosystems. This ocean energy can be harnessed through hybrid nanogenerators that combine triboelectric nanogenerators, electromagnetic generators, piezoelectric nanogenerators, and pyroelectric generators. These nanogenerators have advantages such as high-power density, robust design, easy operating principle, and cost-effective fabrication. However, the performance of these nanogenerators can be affected by the wear of their main components, reduction of wave frequency and amplitude, extreme corrosion, and sea storms. To address these challenges, future research on hybrid nanogenerators must improve their mechanical strength, including materials and packages with anti-corrosion coatings. Herein, we present recent advances in the performance of different hybrid nanogenerators to harvest ocean energy, including various transduction mechanisms. Furthermore, this review reports potential applications of hybrid nanogenerators to power devices in marine infrastructure or serve as self-powered MIoT monitoring sensor networks. This review discusses key challenges that must be addressed to achieve the commercial success of these nanogenerators, regarding design strategies with advanced simulation models or digital twins. Also, these strategies must incorporate new materials that improve the performance, reliability, and integration of future nanogenerator array systems. Thus, optimized hybrid nanogenerators can represent a promising technology for ocean energy harvesting with application in the maritime industry. Full article

Permanent magnetic couplings provide critical advantages for deep-sea systems through static-sealed, contactless power transmission. However, conventional metallic isolation sleeves incur significant eddy current losses, limiting efficiency and high-speed operation. Limited torque capacities fail to meet the operational demands of harsh marine environments. This study presents a novel permanent magnet coupling featuring a ceramic isolation sleeve engineered for deep-sea cryogenic ammonia submersible pumps. The ceramic sleeve eliminates eddy current losses and provides exceptional corrosion resistance in acidic/alkaline environments. To withstand 3.5 MPa hydrostatic pressure, a 6-mm-thick sleeve necessitates a 10 mm operational air gap, challenging magnetic circuit efficiency. To address this limitation, an improved 3D magnetic equivalent circuit (MEC) model was developed that explicitly accounts for flux leakage and axial end-effects, enabling the accurate characterization of large air gap fields. Leveraging this model, a Taguchi method-based optimization framework was implemented by balancing key parameters to maximize the torque density. This co-design strategy achieved a 21% increase in torque density, enabling higher torque transfer per unit volume. Experimental validation demonstrated a maximum torque of 920 Nm, with stable performance under simulated deep-sea conditions. This design establishes a new paradigm for high-power leak-free transmission in corrosive, high-pressure marine environments, advancing applications from deep-sea propulsion to offshore energy systems. Full article

Autonomous Unmanned Surface Vehicle (USV) operations in complex ocean engineering scenarios necessitate robust navigation, guidance, and control technologies. These systems require reliable sensor-based object detection and efficient, safe, and energy-aware path planning. To address these multifaceted challenges, this paper proposes a novel synergistic AI framework. The framework integrates (1) a novel adaptation of the Swin-Transformer to generate a dense, semantic risk map from raw visual data, enabling the system to interpret ambiguous marine conditions like sun glare and choppy water, enabling real-time environmental understanding crucial for guidance; (2) a Transformer-enhanced A-star (T-ASTAR) algorithm with spatio-temporal attentional guidance to generate globally near-optimal and energy-aware static paths; (3) a domain-adapted TD3 agent featuring a novel energy-aware reward function that optimizes for USV hydrodynamic constraints, making it suitable for long-endurance missions tailored for USVs to perform dynamic local path optimization and real-time obstacle avoidance, forming a key control element; and (4) CUDA acceleration to meet the computational demands of real-time ocean engineering applications. Simulations and real-world data verify the framework’s superiority over benchmarks like A* and RRT, achieving 30% shorter routes, 70% fewer turns, 64.7% fewer dynamic collisions, and a 215-fold speed improvement in map generation via CUDA acceleration. This research underscores the importance of integrating powerful AI components within a hierarchical synergy, encompassing AI-based perception, hierarchical decision planning for guidance, and multi-stage optimal search algorithms for control. The proposed solution significantly advances USV autonomy, addressing critical ocean engineering challenges such as navigation in dynamic environments, object avoidance, and energy-constrained operations for unmanned maritime systems. Full article

This study presents the design, fabrication, and performance assessment of a novel, small-scale (30–70 W), hybrid ocean energy system that captures energy from wave-induced heave motion using a point-absorber buoy and from ocean currents via a vertical axis water turbine (VAWT). Key innovations include a custom designed and built dual-rotor generator that accepts independent mechanical input from both subsystems without requiring complex mechanical coupling and a bi-directional mechanical motion rectifier with an overdrive. Numerical simulations using ANSYS AQWA (2024R2) and QBLADE(2.0.4) guided the design optimization of the buoy and turbine, respectively. Wave resource assessment for the Khobar coastline, Saudi Arabia, was conducted using both historical data and field measurements. The prototype was designed and built using readily available 3D-printed components, ensuring cost-effective construction. This mechanically simple system was tested in both laboratory and outdoor conditions. Results showed reliable operation and stable power generation under simultaneous wave and current input. The performance is comparable to that of existing hybrid ocean wave–current energy converters that employ more complex flywheel or dual degree-of-freedom systems. This work provides a validated pathway for low-cost, compact, and modular hybrid ocean energy systems suited for remote coastal applications or distributed marine sensing platforms. Full article

To address the limitations of conventional single-stack fuel cell hybrid systems using equivalent hydrogen consumption strategies, this study proposes a multi-stack energy management strategy incorporating fuel cell health degradation. Leveraging a fuel cell efficiency decay model and lithium-ion battery cycle life assessment, power distribution is reformulated as an equivalent hydrogen consumption optimization problem with stack degradation constraints. A hybrid Genetic Algorithm–Particle Swarm Optimization (GA-PSO) approach achieves global optimization. The experimental results demonstrate that compared with the Frequency Decoupling (FD) method, the GA-PSO strategy reduces hydrogen consumption by 7.03 g and operational costs by 4.78%; compared with the traditional Particle Swarm Optimization (PSO) algorithm, it reduces hydrogen consumption by 3.61 g per operational cycle and decreases operational costs by 2.66%. This strategy ensures stable operation of the marine power system while providing an economically viable solution for hybrid-powered vessels. Full article

Liquefied natural gas (LNG) is increasingly used as a marine fuel due to its capacity to significantly reduce emissions of particulate matter, sulfur oxides (SO_x), and nitrogen oxides (NO_x), compared to conventional fuels. In addition, LNG combustion produces less carbon dioxide (CO₂) than conventional marine fuels, and the use of non-fossil LNG offers further potential for reducing greenhouse gas emissions. However, this benefit can be partially offset by methane slip—the release of unburned methane in engine exhaust—which has a much higher global warming potential than CO₂. This study presents an experimental evaluation of methane emissions from a RoPax vessel powered by low-pressure dual-fuel four-stroke engines with a direct mechanical propulsion system. Methane slip was measured directly during onboard testing and combined with a year-long analysis of engine operation using an Engine Load Monitoring (ELM) method. The yearly average methane slip coefficient (C_slip) obtained was 1.57%, slightly lower than values reported in previous studies on cruise ships (1.7%), and significantly lower than the default values specified by the FuelEU (3.1%) Maritime regulation and IMO (3.5%) LCA guidelines. This result reflects the ship’s operational profile, characterized by long crossings at high and stable engine loads. This study provides results that could support more representative emission assessments and can contribute to ongoing regulatory discussions. Full article

This paper proposes a control method that combines a fractional-order sliding mode observer and a cooperative control strategy to address the problem of path-following for underactuated autonomous underwater vehicles (AUVs) in complex marine environments. First, a fractional-order sliding mode observer is designed, combining fractional calculus and double-power convergence laws to enhance the estimation accuracy of high-frequency disturbances. An adaptive gain mechanism is introduced to avoid dependence on the upper bound of disturbances. Second, a formation cooperative control strategy based on path parameter coordination is proposed. By setting independent reference points for each AUV and exchanging path parameters, formation consistency is achieved with low communication overhead. For the followers’ speed control problem, an error-based expected speed adjustment mechanism is introduced, and a hyperbolic tangent function is used to replace the traditional arctangent function to improve the response speed of the system. Numerical simulation results show that this control method performs well in terms of path-following accuracy, formation maintenance capability, and disturbance suppression, verifying its effectiveness and robustness in complex marine environments. Full article

In this paper, a hybrid flying robot that utilizes water thrust and aerial propeller actuation is proposed and analyzed, with the aim of applications in hazardous tasks in the marine field, such as firefighting, ship inspections, and search and rescue missions. For such tasks, existing solutions like drones and water-powered robots inherited fundamental limitations, making their use ineffective. For instance, drones are constrained by limited flight endurance, while water-powered robots struggle with horizontal motion due to the couplings between translational motions. The proposed hydro-aerodynamic hybrid actuation in this study addresses these significant drawbacks by utilizing water thrust for sustainable vertical propulsion and propeller-based actuation for more controllable horizontal motion. The characteristics and mathematical models of the proposed flying robots are presented in detail. A state feedback controller and a proportional–integral–derivative (PID) controller are designed and implemented in order to govern the proposed robot’s motion. In particular, a linear matrix inequality approach is also proposed for the former design so that a robust performance is ensured. Simulation studies are conducted where a purely water-powered flying robot using a nozzle rotation mechanism is deployed for comparison, to evaluate and validate the feasibility of the flying robot. Results demonstrate that the proposed system exhibits superior performance in terms of stability and tracking, even in the presence of external disturbances. Full article

This study identifies the optimal location for an offshore energy island to supply sustainable power to desalination plants along the Red Sea coast. As demand for clean energy in water production grows, integrating renewables into desalination systems becomes increasingly essential. A decision-making framework was developed to assess site feasibility based on renewable energy potential (solar, wind, and wave), marine traffic, site suitability, planned developments, and proximity to desalination facilities. Data was sourced from platforms such as Windguru and RETScreen, and spatial analysis was conducted using Inverse Distance Weighting (IDW) and Multi-Criteria Decision Analysis (MCDA). Results indicate that the central Red Sea region offers the most favorable conditions, combining high renewable resource availability with existing infrastructure. The estimated regional desalination energy demand of 2.1 million kW can be met using available renewable sources. Integrating these sources is expected to reduce local CO₂ emissions by up to 43.17% and global desalination-related emissions by 9.5%. Spatial constraints for offshore installations were also identified, with land-based solar energy proposed as a complementary solution. The study underscores the need for further research into wave energy potential in the Red Sea, due to limited real-time data and the absence of a dedicated wave energy atlas. Full article

The constitutive model significantly influences the accuracy of predicting the complex rheological behavior of hot isostatically pressed powders. The temperature plays a crucial role in determining material properties during hot isostatic pressing (HIP), making it essential to account for its effect on the yield model parameters to more accurately describe the densification evolution of powders. In this study, HIP experiments were conducted using two different process schemes, and the shrinkage deformation of the envelope under each scheme was analyzed. High-temperature uniaxial compression experiments were performed on HIP samples with varying densities to analyze and characterize the stress–strain response of the powder during HIP. A mesoscopic particle-scale high-temperature uniaxial compression model was developed based on the discrete element method (DEM), and the strain and stress values corresponding to different densities in the high-temperature uniaxial compression simulations were validated through experimental comparison. The strain evolution during the uniaxial compression process was analyzed, and the relationship between the parameters of the Shima–Oyane model and the temperature was established, leading to the development of a temperature-compensated Shima–Oyane model. Based on the obtained parameters at various densities and temperatures, a yield stress map for the nickel-based alloy was constructed. The accuracy of this model was verified by comparing experimental results with finite element method (FEM) simulations. The findings of this study contribute to a more precise prediction of densification behavior in thermally driven isostatic pressing. Full article