Accurate quantification of atmospheric emissions from port service vessels remains challenging in many developing regions because detailed monitoring systems and advanced atmospheric-dispersion models are often unavailable. This study presents a bottom-up framework for estimating emissions and assessing their potential transport toward adjacent terrestrial environments, using the Port of Mohammedia (Morocco) as a case study. Operational data collected from seven service vessels during 2024, including fuel consumption, vessel activity, and engine characteristics, were combined with standard emission factors to estimate annual emissions of carbon dioxide (CO2), nitrogen oxides (NOx), sulfur dioxide (SO2), particulate matter (PM2.5), and particulate matter (PM10). A quality-control procedure was implemented to identify anomalous records and improve inventory reliability. Annual emissions were estimated at 961.24 t CO2, 15.20 t NOx, 0.55 t SO2, 0.26 t PM2.5, and 0.31 t PM10, with substantial temporal variability driven by vessel activity and fuel consumption. Two vessels alone contributed 65.8
The present study investigates wave interaction with two Semi-trapezoidal Porous Breakwaters (STPB) through detailed experimental and soft-computing-based analyses. The hydrodynamic behaviour of the STPB is examined in a wave flume by considering different structural parameters, including variations in porosity and the relative placement of the breakwaters. The influence of these parameters on wave reflection, transmission, and energy dissipation characteristics is systematically analysed under regular wave conditions. The hydrodynamic parameters obtained experimentally are further utilized to develop predictive models for assessing the performance of the porous breakwaters. To enhance prediction capability and reduce the dependency on time-consuming experimental investigations, advanced soft computing techniques such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are employed. The experimental dataset is divided into training and testing subsets for model development and validation. The predictive performance of the developed models is evaluated using statistical indicators including the coefficient of correlation, root mean square error (RMSE), and scatter index (SI). The importance of the present study lies in the combined experimental and comparative soft-computing-based investigation of STPB, which has received limited attention in previous coastal engineering studies. The study not only develops efficient predictive models for hydrodynamic characteristics of porous breakwaters but also compares the effectiveness of ANN, SVM, and ANFIS approaches in modelling wave-structure interaction problems. The research contributes toward the development of computationally efficient tools for predicting the performance of submerged porous coastal structures in both near-shore and deep-water regions. The findings provide valuable information for the design, optimization, and sustainable application of porous breakwaters for coastal protection and wave energy management.
The enforcement of the IMO MARPOL Annex VI global sulphur cap has accelerated the widespread adoption of exhaust gas cleaning systems (EGCS) as a primary compliance technology in marine transport. Despite extensive evaluation of their sulphur removal efficiency, limited understanding exists regarding the internal operational dynamics, transient behaviour, and environmental risk characteristics of EGCS under real ship operating conditions. This study presents a comprehensive, high-frequency, data-driven analysis of an open-loop EGCS installed on a commercial vessel, based on approximately 12,000 time-synchronised observations collected from five diesel generators and 58 physically measured operational variables. The dataset captures authentic shipboard behaviour across inactive, nominal, high-load, and transient operating regimes, encompassing hydraulic demand, energy intensity, pressure dynamics, safety signals, and transient disturbances. A composite environmental risk index integrating flow stress, energy intensity, transient intensity, and pressure effects is formulated to provide a unified, regulation-relevant metric. Results demonstrate that environmental risk is highly non-uniform in time and is dominated by high-load and transient operation rather than average conditions. Risk contributions are unevenly distributed across diesel generators, with a small subset accounting for the majority of high-risk events. A constrained machine-learning model accurately predicts composite environmental risk and reveals physically consistent driver hierarchies, while time-resolved analysis confirms clear early-warning capability. This work advances EGCS environmental assessment from static compliance verification toward predictive environmental assurance, providing a directly implementable framework for real-time shipboard monitoring.
Deep water and ultra-deep water oil exploration require overcoming several technical challenges, particularly those associated with riser design. In the search for solutions to enable the use of rigid steel risers at great depth, the use of buoyancy devices that adjust the curvature of the riser has been extensively studied. This configuration reduces the tension and wear of the riser by decoupling the movement of the floating unit from the contact point of the riser with the seabed, thus minimizing the structural load, leading to improved operational efficiency and extended system lifespan. This paper proposes a procedure to determine and evaluate the static configuration of the riser structure in the lazy wave regime, using practical input parameters such as the total length, depth, position of the well relative to the floating unit, and arrangement of buoyancy devices. An analytical approach based on the catenary equations is conducted, and a numerical approach is carried out using a finite element model developed in ANSYS Mechanical. The results obtained from both approaches show a consistent correlation and provide a solid theoretical foundation for future dynamic analyses of risers in the lazy wave configuration.
This paper gives a comparative numerical analysis of different propeller boss cap fin (PBCF) geometries using computational fluid dynamics (CFD) software. Previous experimental and numerical studies on PBCF applications are first summarized, highlighting their reported impact on ship propulsion and propeller efficiency. The incentive for the study comes from industry demand and regulatory requirements for new builds to be more environmentally friendly and to reduce fuel consumption. A propulsion system with small ship propeller and PBCF has been studied, seven propeller cap models and nine PBCF models were designed and evaluated through CFD open water test simulations. The numerical setup represents standard open water test conditions, and the results are compared against those of the initial propeller without PBCF. The initial propeller model is five blades fixed pitch propeller designed for 55 m passenger ship. Thrust, torque, and propeller efficiency are evaluated and compared to analyse the impact of PBCF geometry on propeller performance. The results indicate that the open water efficiency gains are small comparable to the estimated numerical uncertainty, which indicates that energy savings with PBCF on small passenger vessels are insignificant. Therefore, detailed flow field analyses are performed in the region behind propeller to examine changes in vortex structure and velocity distribution induced by the fins. Those numerical results provide the basis for future research of PBCF impact on noise and vibration reduction rather than achieving high efficiency gains.
Bengkulu Province is located along the Indian Ocean coastline, providing abundant potential for ocean wave energy. However, the utilization of this resource, particularly for low-wave condition and wave energy conversion systems with varying transmission and buoy configurations, remains largely unexplored. Therefore, this preliminary study developed a simple buoy-type wave energy converter prototype and investigated the effect of different gear transmission and buoy configurations. Two gears configuration consisting of 12 and 16 teeth and two buoy geometries, namely spherical dan cylindrical were examined. An INA219-based current and voltage sensing system integrated with a microcontroller was employed to monitor the electrical power generated by the prototype. Each configuration was examined for 30 minutes under actual sea condition off the coast of Bengkulu City. The results indicate that the 12-tooth (12 T) gear configuration produced a higher average power output of 29.30 mW compared with 28.25 mW for the 16-tooth (16 T) configuration. The performance of the 12-tooth gear is attributed to lower mechanical losses, reduced transmission resistance, and lower rotational inertia, which enabled more effective transfer of wave-induced mechanical energy to the generator. For buoy geometry, cylindrical buoys exhibited significantly better performance than the spherical buoy, producing an average power output of 37.61 mW compared with 29.30 mW. This improvement is attributed to the larger projected area and stronger hydrodynamic interaction of the cylindrical buoy with incoming waves, resulting in greater vertical displacement and enhanced energy transfer to the transmission system. The maximum system efficiencies obtained for the cylindrical and spherical buoys were 0.57
Single Point Anchor Reservoir (SPAR) floating offshore platforms are essential for deep-water oil and gas extraction, but their structural integrity is challenged by hydrodynamic loads and progressive structural degradation. This study develops an integrated survivability framework combining hydrodynamic load modelling, structural response analysis, and degradation mechanisms into a composite degradation index. A MATLAB-based model was developed to simulate the components. The outputs were normalized and consolidated into a composite degradation index. It quantifies structural integrity and establishes the life cycle stages. Results show that long-term structural degradation mechanisms had a greater effect on the structure than hydrodynamic loads and motions. Among them, corrosion and fatigue are identified as the major concerns. The framework is validated for the North Sea conditions using sensitivity analysis across varying sea states. Harsher sea states also increase the vulnerability to structural degradation. The higher wave heights are also revealed as the dominant driver. The gauged composite degradation index ranges from 0.92 to 0.99 over 10 to 60 years. Therefore, the SPAR structure entails repurposing due to the structural degradation and harsher environments. Accordingly, the composite degradation index serves as a decision support tool for lifecycle management and sustainable offshore engineering.
To address the challenge of real-time sensing and accurate prediction of the wear status of marine water-lubricated stern bearings under complex operating conditions, a wear monitoring system based on multi-source sensor fusion was developed. The system utilizes eddy current displacement sensors and vibration acceleration sensors to construct a distributed hardware acquisition architecture. Based on LabVIEW and Python hybrid programming technology, a monitoring software system integrating multi-channel data acquisition, real-time display and control, anomaly warning, and trend analysis was developed. Furthermore, two machine learning algorithms, Long Short-Term Memory and Gated Recurrent Unit, were integrated to address the difficulty in characterizing nonlinear wear trends. Accelerated wear experiments were conducted using a large-scale simulation test bench, constructing a large-scale sample database with high-density data. Experimental results demonstrate that the system possesses good adaptability under complex operating conditions. The trend prediction accuracy for key parameters reached 85 percent, effectively realizing the perception of wear status and the analysis of wear evolution trends for water-lubricated stern bearings. These research results hold significant reference and application value for the operation and maintenance of water-lubricated stern bearings.
Deepwater drilling riser operability envelopes are of significant importance for guiding on-site decision-making in deepwater drilling operations. To meet the real-time computational requirements for operability envelopes, an Artificial Vine Algorithm that mimics the winding behavior of vines for searching boundary values within these envelopes was proposed. The algorithmic search strategy and theoretical model were established, and the algorithm was analyzed and compared using the drilling riser hard hang-off operability envelope as an example. The results indicate that while the root positions and exploratory growth directions of the Artificial Vine Algorithm have a certain impact on search efficiency and cost, they have little effect on search accuracy. Through a heuristic boundary search mechanism, the algorithm’s computational nodes concentrate near the boundaries, resulting in high search efficiency; with a tolerance of 2
Static analysis and design optimization of multi-segment catenary mooring lines equipped with clump weights pose significant computational challenges due to the high non-linearity of the governing equations. These complexities stem not from the number of design variables, but from the severe non-linearity of the governing equations and the geometric discontinuity introduced by the clump weights. To address these challenges, this study proposes a flexible optimization-based framework specifically designed for the analysis of a single mooring line within a two-dimensional (2D) vertical plane. To solve the highly implicit static analysis equations, the problem is formulated as a constrained optimization model integrating fundamental catenary relations, force equilibrium, and displacement continuity. Two metaheuristic algorithms—Particle Swarm Optimization (PSO) and Genetic Algorithm (GA)—are employed to rigorously cross-validate the mathematical formulation and demonstrate the robustness of the proposed framework. The method is verified against Finite Element Analysis (FEA) using Sap2000, showing negligible discrepancies. Furthermore, the framework is subsequently applied to complex design optimization scenarios, including the simultaneous optimization of clump weight position and mooring line length. Results demonstrate that both algorithms exhibit rapid convergence and high consistency, yielding identical optimal solutions. This confirms the proposed approach as an effective tool for the preliminary design of a single catenary mooring line, which can be generalized to evaluate 3D multi-line systems by vectorially superimposing the individual 2D line responses. Crucially, by providing explicit analytical solutions, the framework establishes a cost-effective foundation to overcome the high computational expense associated with 'black-box' constraint evaluation in conventional numerical optimization tools.
Accurate sediment classification is essential for understanding riverbed dynamics, geomorphological evolution, and supporting water resource management and civil engineering projects. In the Taedong River, Democratic People’s Republic of Korea (DPRK), conventional sediment sampling methods face significant logistical and technical constraints, necessitating rapid, cost-effective, and reliable alternatives. This study proposes a novel modeling framework that uses drilling speed—measured during shallow rotary drilling—as a diagnostic parameter for sediment classification. A response surface methodology (RSM)-based experimental design was employed to systematically investigate the statistical relationships between the rate of penetration (ROP) and key sediment properties, including median grain size (D50), bulk density, moisture content, and consistency. Fieldwork comprised 18 boreholes across the upper, middle, and lower reaches of the Taedong River, complemented by comprehensive laboratory analyses. The developed second-order RSM model demonstrated strong predictive performance, with a coefficient of determination (R²) of 0.92, enabling classification of sediments into four primary categories: clay, silt, sand, and gravel. Validation against laboratory grain-size analysis yielded an overall classification accuracy of 87.5
Ship and offshore design is a complex engineering process involving the integration of multiple interdependent systems, requiring a balance between performance, safety, and feasibility. Traditional methodologies rely on iterative cycles and fragmented data exchange, often leading to inefficiencies, incompatibilities, and design inconsistencies. To address these challenges, this paper proposes an innovative approach inspired by Building Information Modeling (BIM), integrating Computational Fluid Dynamics (CFD), High-Performance Computing (HPC), Artificial Intelligence (AI), and Virtual Reality (VR) technologies to enhance the design process. Central to this framework is Synapse, a multidisciplinary optimization platform that facilitates the seamless coupling of simulation tools within a user-friendly and collaborative environment. The process begins with a parametric hull modeling system, allowing dynamic adjustment of design variables based on user-defined criteria. Initial analyses rely on well-established hydrodynamic methods, followed by CFD simulations. The use of HPC resources enables extensive design space exploration through parallel simulations and optimization tasks, significantly improving execution time and scalability. VR interfaces provide immersive 3D visualization for post-processing of final configurations, promoting intuitive exploration, effective validation, and enhanced communication among multidisciplinary teams. The AI can significantly reduce optimization computational cost. As a practical demonstration, the proposed methodology was applied to the optimization of two cases: real-world mooring system and a submarine design, achieving a 75
In this work, the effects of seawater exposure and specimen geometry on the mechanical performance of 3D-printed Acrylonitrile Butadiene Styrene (ABS) and carbon fiber-reinforced ABS (ABS/CF) were investigated. Enclosed 3D printers were used to avoid shrinkage and warping of ABS and ABS/CF. The standard specimen types in this research work were ASTM D638 type IV, ISO 527-2 type 1BA, and ASTM D3039 full-section specimens. The tensile tests were strain-controlled tests with a strain rate of 0.1 min− 1. Firstly, the effects of the different specimen types on the tensile strength and Young’s modulus of unaged (as-printed) ABS and ABS/CF were analyzed. Secondly, the three types of specimens were immersed in seawater for 1, 2, and 3 weeks to evaluate moisture absorption and the degradation of the mechanical properties. The results indicated that the test specimen type had a statistically significant effect on the mechanical properties of ABS and ABS/CF, with the ISO 527 specimen exhibiting the lowest tensile strength and Young’s modulus. Moreover, all ABS and ABS/CF specimens showed moisture uptake of less than 1
To reveal the hydrodynamic mechanisms of ship navigation through the bridge of inland waterways, this study adopts a Computational Fluid Dynamics (CFD) approach based on an overset mesh technology to simulate the entire process of an integrated hull-propeller-rudder self-propelled ship passing through a bridge pier. Unlike traditional studies based on towing conditions or simplified propulsion models, the coupling effects of self-propulsion and shallow water are incorporated within a unified framework. Unsteady hydrodynamic responses during pier passage are analyzed, with emphasis on the viscous flow structures around the hull and the evolution of propeller-induced vortices. The results show that the lateral force has a typical “pus-suction-push” pattern, with peak suction increasing by about 70
Dependency on fossil fuels has increased with time; as a result, CO2 emissions, greenhouse gas emissions, etc. have also increased. So, there is no alternative to using renewable energy. Wind energy is a significant form of renewable energy source which has grown in prominence over time. Wind turbines are designed and optimized to convert wind energy into power. Horizontal Axis Wind Turbines (HAWT) are the most widely used wind turbines for capturing wind power. This study presents a site-specific aerodynamic comparison of two scaled horizontal-axis wind turbine (HAWT) concepts for a low-wind offshore area of the Bay of Bengal. The novelty of the work lies in the comparative evaluation of two geometrically different reference turbines one derived from the NREL 5 MW turbine and the other from the NREL Phase VI turbine after rescaling them to the same target offshore operating condition, thereby enabling a like-for-like assessment for Bangladesh’s low-wind-speed coastal environment. Simulations were carried out in QBlade using the Blade Element Momentum (BEM) method, and the adopted numerical approach was validated against published FAST and experimental reference results for the NREL 5 MW turbine. The results show that Model 1 achieved a maximum power coefficient of 0.475, whereas Model 2 achieved 0.424. The maximum aerodynamic power outputs of Model 1 and Model 2 were 738 kW and 655 kW, respectively, at the rated wind speed of 8.3 m/s. Although both models yielded the same capacity factor of 27
Computer vision has numerous difficulties in marine environments, including challenges (that significantly impede object detection and image quality) for applications such as biodiversity monitoring, pipeline inspection, and autonomous underwater vehicle navigation, due to light being absorbed and scattered, colour distortion and low contrast. While the use of deep learning has improved the performance of both object detection and image enhancement significantly, the literature often investigates these tasks in isolation (historically), with little consideration given to the incorporation of current transformer-based architectures, and real-time deployment issues. In this review, the authors conduct a thorough analysis of the most recent deep learning methods for marine object detection and image enhancement by reviewing the available literature on convolutional neural networks, transformer-based models, and hybrid approaches. The authors review 70 peer-reviewed manuscripts published from 2015 to 2025 to determine the detection performance, enhancement quality and generalisation of the detection and/or enhancement performance of the algorithm under a range of conditions in an underwater environment. Using a comparative approach, the authors observed that single-stage detectors had better real-time performance than all other detectors; however, advanced enhancement methods had better visual quality than all other methods but experienced difficulties achieving cross-domain robustness. Ultimately, the authors identified significant limitations associated with the literature in the areas of domain adaptation, computation restrictions and the absence of robust standardised datasets, and identified emerging research directions, such as hybrid transformer-based models, self-supervised learning and edge deployment. This review aims to provide a unified perspective and guide future research toward robust, efficient, and real-time marine vision systems.
Marine heatwaves and tropical cyclones are two major ocean–atmosphere extremes whose compound interactions remain poorly understood in the Southern Indian Ocean. Using satellite-derived sea surface temperature, ocean reanalysis, and best-track tropical cyclone data spanning 1982–2024, this study investigates the occurrence, characteristics, and physical impacts of marine heatwave-associated tropical cyclones in the southeastern Indian Ocean. Tropical cyclones were classified as marine heatwave-associated when their intensification phase overlapped spatially and temporally with active marine heatwaves, based on sensitivity-tested criteria. Of the 231 tropical cyclones identified, only eight satisfied these interaction conditions, highlighting the rarity of marine heatwave–tropical cyclone coupling in this basin. Despite their limited frequency, these events exhibited systematically stronger intensification than non-marine heatwave cyclones, developing over sea surface temperature anomalies of 1–2 °C, elevated ocean heat content, and deeper warm layers extending to at least 100 m. These oceanic conditions enhanced air–sea enthalpy fluxes, suppressed storm-induced surface cooling, and sustained energy transfer during the critical pre-maximum-intensity period, resulting in steeper sea level pressure drops, stronger winds, and more compact storm structures. Seasonal analysis shows that marine heatwave-associated cyclones occur primarily during the Southern Hemisphere cyclone season but may extend cyclone-favourable conditions into shoulder months, while interannual variability reveals no single dominant linkage to ENSO or the Indian Ocean Dipole, indicating that regional oceanic processes and local air–sea coupling play a more direct role. Overall, the results demonstrate that although rare, marine heatwave–tropical cyclone interactions exert a disproportionate influence on cyclone intensification in the southeastern Indian Ocean, underscoring the importance of subsurface thermal structure and regional ocean–atmosphere coupling in a warming climate.
Graded vertical terminals (GVTs) are designed for environments with extreme water level fluctuations. Accurately assessing the throughput capacity of these terminals during the design phase is crucial for engineering purposes. This study analyzes the throughput capacity of GVTs based on operational areas and proposes a comprehensive three-stage evaluation framework that integrates the throughput capacity of the terminal’s waterside, storage yard, and landside. We developed two distinct algorithms to address the priorities of operating platforms: “prioritizing high-water level platform operations” and “prioritizing low-water level platform operations.” These algorithms account for the impact of the compatible water level range between adjacent operating platforms on annual operating days. For calculating terminal throughput capacity under specific conditions, we introduce methods for dividing annual operating days and annual freight volume. Case studies demonstrate the practicality of the proposed method, indicating that setting a compatible water level range between adjacent platforms can enhance terminal throughput capacity. Sensitivity analysis reveals that terminal throughput capacity is positively correlated with the ship’s hourly loading and unloading efficiency and the effective berth utilization rate, while being inversely related to auxiliary and technical operation times.
This study assesses occupational hazards in Nigerian shipyards using classical Hazard Identification and Risk Assessment (HIRA), fuzzy logic inference, and Bayesian network modelling. The study, which was conducted across three vital coastal yards named Yard A (West), Yard B (Central), and Yard C (East), involved six experienced experts evaluating 22 core shipbuilding risks. HIRA ratings highlighted geographical differences: Yard A had the highest concentration of Very High and Priority 1 hazards, especially during surface preparation and assembly; Yard B had moderate but contradicting expert opinions; and Yard C had the lowest overall hazard levels. To address rating discrepancies, a fuzzy-logic approach was used to construct a continuous risk index using discrete exposure, likelihood, and outcome ratings. These refined outputs were embedded into a Bayesian network to simulate conditional dependencies among hazards. Findings identified sharp object exposure, toxic fumes, and equipment disorganization as critical drivers of health and operational risks.
Coastal areas are increasingly threatened by erosion and storm waves, but natural solutions like coastal plants can helpprotect shorelines. Vegetation such as marsh grasses reduces wave energy before it reaches the coast, acting as a natural barrier. However,predicting how well these plants reduce waves is challenging. In this study, we developed a computer model to better understand how wavesinteract with coastal vegetation. Instead of modelling each plant individually, the vegetation is treated as a porous zone that slows down anddissipates wave energy. The model was tested against laboratory and field data and showed strong agreement with real observations. Theresults confirm that denser vegetation provides greater protection and that plants extending above the water surface are more effective thanthose fully submerged. This approach offers a fast and reliable way to assess how nature-based coastal defences perform, helping engineersand decision-makers design more sustainable solutions for shoreline protection.
