This study aims to reduce the impact of wind and wave loads on the output power, rotor speed, and motion fluctuations of a floating wind turbine, while enhancing the anti-interference capability and addressing the strong coupling of the wind turbine system. Using a joint simulation based on OpenFAST and Simulink, this study focuses on offshore floating wind turbines consisting of the NREL 5MW turbine and a semi-submersible platform. A Fuzzy PIDD² (FPIDD²) collective pitch controller is designed, building on the OpenFAST baseline controller. The collective pitch controller helps mitigate wind and wave disturbances and improve the system resistance to external forces. Furthermore, to address the problem of rapid population diversity decline and the tendency to fall into local optima caused by the grouping competition mechanism in the Tianji Horse Racing Optimization (THRO) algorithm, an improved version, ITHRO, is proposed. This hybrid algorithm is applied to optimally tune the parameters of the FPIDD² controller, thereby enhancing its control performance. Finally, the control performance of the FPIDD² controller is compared with that of Fuzzy PID (FPID), PIDD², and the OpenFAST baseline controllers under varying wind and wave conditions. The results indicate that the FPIDD² controller significantly improves the stability of generator output power and rotor speed, while also reducing the motion fluctuations of the floating platform.

Marine navigation is strongly influenced by oceanographic factors such as currents, rapidly changing sea conditions, and complex wave interactions. These elements affect both the structural integrity of a vessel and its overall operational safety, especially when navigating in harsh or unpredictable marine environments. Understanding how ships respond to different sea states is therefore essential for safe design and efficient operation.

This study investigates the impact of sea states on vessel motions and vertical bending moments, using a representative case study. Hydrodynamic analyses were performed through a panel-based numerical approach under stationary and advancing conditions, considering head and beam seas. Both frequency- and time-domain simulations were carried out, including the derivation of Response Amplitude Operators (RAOs) in regular waves and structural responses in irregular seas based on an empirical spectrum. The resulting hydrodynamic loads were applied to stress and fatigue assessments, and fatigue life predictions were made under extreme conditions. Comparisons with classification society rules provided further evaluation of structural performance.

The outcomes contribute to a better understanding of vessel response in complex wave environments, supporting design optimization and operational safety. The methodology also provides a foundation for integration with finite element modeling and machine learning for real-time structural health monitoring, with potential applicability across different ship types and scales.

The training of pilots in the Panama Canal relies on several tools and methodologies, one of which is the use of ship scale models, such as those used at the Panama Canal Authority Training Center, where Froude similarity ensures the hydrodynamic validity of maneuvers. These physical models are derived from the geometry of real vessels and adjusted experimentally through the distribution of ballast until the target draft, displacement, and inertia properties are achieved. This process, while robust, is largely iterative and depends on successive adjustments carried out during the construction and testing phases. In parallel, computational fluid dynamics (CFD) simulations are increasingly applied to investigate maneuvering performance and flow interactions in confined waters, offering highly detailed information but at the cost of significant computational resources and time. Together, these approaches constitute the current reference framework for the design and validation of ship behavior in restricted channels.

This work proposes an alternative methodology intended to support the dynamic design of ship scale models for pilot training. The approach is based on potential flow theory combined with Schwarz–Christoffel conformal transformations, enabling the analytical prescription of the dynamic conditions (drafts, displacement, and inertia properties) that must be satisfied for scale models to reproduce the maneuvering response of full-scale vessels.

The methodology aims to reduce the reliance on purely experimental adjustments, support the early stages of design, and provide an approach that complements both experimental and numerical methods. This contribution is limited to the formulation and scope of the methodology, which may serve as a foundation for subsequent validation through experiments and high-fidelity simulations.

Chellanam Harbour, located on the south west coast of India, is a fishing village withmechanised fishing operations, yet it is increasingly hindered by sediment accumulation that restricts navigation and disrupts daily activities. This study applies the MIKE21 LITPACK numerical modelling suite, developed by DHI, to investigate sediment transport pathways and evaluate wave conditions influencing Chellanam harbour tranquillity. Bathymetric inputs were obtained from GEBCO, while hydrodynamic and meteorological forcing were sourced from ERA5 reanalysis products. Simulations conducted within the MIKE21 Toolbox mapped spatial and temporal patterns of sediment deposition, identifying an average annual siltation rate of 0.144 m, with the most rapid build-up occurring in the entrance channel. This area is critical to safe vessel manoeuvring. The findings offer a predictive framework for optimising dredging cycles, improving sediment management, and guiding infrastructure modifications to sustain operational efficiency. While grounded in a specific regional context, the methodology is transferable to other small tropical harbours worldwide, where similar challenges are magnified by climate-driven changes in coastal processes. By linking applied coastal engineering with global resilience objectives, this research supports UN Sustainable Development Goals on climate action, sustainable fisheries, and resilient infrastructure, contributing to the long-term viability of working waterfronts in developing maritime economies.

The main purpose of this research was to investigate the factors that most strongly affect the occurrence of maritime piracy incidents within the period of 2015–2024. In order to achieve this, statistical data were collected, aggregated, and analyzed with the aim of identifying variables that significantly contribute to the risk of pirate attacks at sea. These variables were then used as essential input for the development of a predictive model constructed with the use of artificial intelligence (AI) and machine learning (ML) techniques. The dataset covered several important dimensions, including the geographical regions where incidents occurred, the classification of different attack types, the nature and degree of violence used, and the tools and methods employed by the perpetrators.

To establish the relative importance of these features, advanced algorithmic approaches to feature selection were applied, with particular emphasis on classifiers based on the Random Forest method. This technique allowed the identification of variables that exert the greatest influence on the likelihood of future piracy incidents. The results demonstrated that three factors stand out as having the strongest impact: geographical location (with Southeast Asia and Africa highlighted as the most vulnerable regions), the type of attack (especially cases involving boarding of vessels), and direct violence against crews, including hostage-taking and kidnappings carried out for ransom.

The outcomes of this study provide a solid basis for the design and implementation of AI-powered early warning systems. Such tools can play an important role for a wide range of stakeholders, including shipping companies, governmental agencies, and international organizations concerned with maritime safety. By integrating predictive analytics into maritime security strategies, it becomes possible to monitor risks dynamically, recognize potential threats in real time, plan safer navigational routes, and allocate protective resources in a more efficient and cost-effective manner. Ultimately, this research highlights the potential of combining data-driven methods with AI to strengthen maritime security and reduce the human and economic costs associated with piracy.

Coastal erosion poses a major threat in the context of global environmental change and is influenced by both natural and anthropogenic processes. Traditional shallow water models often neglect the mechanical response of the seabed, limiting their predictive capacity in simulating sediment redistribution and shoreline evolution. This study extends the classical one-dimensional Saint-Venant shallow water equations by incorporating sediment transport, bottom friction, wave dispersion, and viscoelastic seabed behavior. The latter is modeled using the Kelvin–Voigt constitutive relation, which captures both elastic deformation and time-dependent viscous damping. The coupled system is implemented in the COMSOL Multiphysics platform as a set of partial differential equations. Theoretical case studies with different bathymetric configurations (steps or depressions on the seabed) are simulated to assess the influence of friction, dispersion, and seabed rheology. The numerical simulations highlight the stabilizing effects of viscoelastic behavior, especially when combined with dissipative mechanisms such as friction and dispersion. While the classical Saint-Venant model reproduces basic hydrodynamic responses, the inclusion of rheological terms leads to smoother water surface profiles and more realistic sediment redistribution. Notably, cases with viscoelastic seabeds exhibit damped morphological evolution, with reduced local instabilities and better agreement with observed erosion–deposition patterns in natural systems. The results demonstrate that incorporating viscoelastic properties into morphodynamic models improves the physical realism of simulations and enhances predictive capabilities. These findings support the integration of rheological behavior in coastal modeling frameworks, with potential applications in sediment management, risk assessment, and nature-based coastal defense design.

Introduction

Estuaries worldwide have been closed off from marine influence for flood protection and freshwater supply management. While effective for these societal needs, this strategy significantly alters estuarine ecosystems by eliminating tidal dynamics and disrupting marine-freshwater interactions, leading to blocked fish migration routes and reduced biodiversity. Consequently, there is growing interest in reintroducing controlled saltwater inflows to restore estuarine functions without compromising freshwater availability.

A key challenge in this approach is to establish a dynamic equilibrium in which saltwater inflow and outflow balance over a tidal cycle. Achieving this balance is critical to preventing salt intrusion into upstream freshwater zones. It requires detailed insight into complex hydrodynamic processes – such as stratification, mixing, and the flow dynamics in (former) tidal channels – that govern the salinity distribution in the estuary. Understanding these processes is essential for developing effective management strategies

This study focuses on the Haringvliet, a former estuary in the Rhine-Meuse Delta in the Netherlands, where controlled saltwater inflow has been reintroduced via regulated sluice operations. These trials are supported by extensive ecological monitoring and measurements of hydrodynamics and salinity. Observations show that flushing salt from the estuary’s channels is difficult (Kranenburg et al., 2023), emphasizing the need for optimized sluice management.. However, presently it is still unknown what saltwater inflow duration and timing result in acceptable or excessive salt intrusion.

To address this, we investigate the conditions needed to establish a dynamic equilibrium in the Haringvliet. An approach is developed that combines a detailed 3D numerical model with a simplified analytical model. The analytical model serves two major purposes: (1) to develop a sluice operation protocol that optimizes controlled saltwater inflow while preserving upstream freshwater conditions, and (2) to enhance understanding of the stratified hydrodynamics in the Haringvliet, offering valuable knowledge for future estuarine restoration and management efforts.

Methods

The field measurements from the saltwater reintroduction trials, especially salinity and velocity profiles in the deeper pit structures, provided critical insights into the estuary’s response (Kranenburg et al., 2023). Achieving a stable dynamic equilibrium during these trials proved challenging. Therefore, the field data are not used directly to define equilibrium states but instead serve to validate controlled 3D numerical simulations, which allow for a systematic exploration of equilibrium states under known and reproducible conditions.

A detailed 3D D-HYDRO model of the Haringvliet (Deltares, 2023) was used, a hydrostatic model with an unstructured horizontal mesh (typical cell side lengths of 60 m) and a combined z-sigma vertical grid (z-layer resolution of 0.125 m) to accurately resolve halocline dynamics. The model was validated against field data and used to simulate various combinations of sluice operations and sea salinity conditions with steady tidal forcing, to systematically explore conditions that lead to dynamic equilibrium. Due to the high computational cost, only a limited number of scenarios could be explored.

To overcome this limitation, a simplified analytical model of the Haringvliet system was developed, enabling broader exploration of the parameter space. The estuary is schematized horizontally into pits and channels, connected by sills of fixed height, each with a defined hypsometry. Vertical stratification is represented by a two-layer system, with denser saltwater down in the channels and pits and freshwater above. The model simulates three processes: (1) saltwater inflow through the sluices, using dilution relationships derived from the 3D model; (2) vertical mixing at the salt–freshwater interface, parameterized using the Richardson number and calibrated with 3D model outputs; and (3) outflow of the upper layer. With this approach, the analytical model computes the salt intrusion length for the dynamic equilibrium state in the estuary based on an inflow, outflow and sea salinity conditions.

Results

Using the 3D model, 59 simulations were conducted, covering six distinct sea salinity levels (1,000–10,000 mg/L), inlet volumes ranging from 1.3 to 20 million m³ per tide, and outflow volumes between 10 and 80 million m³ per tide. For each simulation, the establishment of a dynamic equilibrium was assessed, along with the extent of salt intrusion into the system. Results align well with field trial observations. The 3D model allowed partial filling of the parameter space and provided insight into how equilibrium location depends on system parameters. However, due to limited resolution in the simulated parameter space, these relationships remain approximate.

The analytical model was calibrated with the vertical mixing parameter using 3D model results. The calibrated mixing intensity varies with outflow volume: stronger mixing for lower outflows and reduced mixing at higher outflows. The calibrated model closely reproduced both the presence of equilibrium and the salt intrusion length observed in the 3D simulations and field data, making it a fast and reliable alternative for predicting system behaviour without the computational cost of 3D modelling.

After calibration, the analytical model was used to perform nearly 10,000 simulations, enabling high-resolution exploration of the parameter space – something not feasible with the 3D model alone.

Both models showed that the formation of a dynamic equilibrium is highly sensitive to sea salinity levels. At higher salinities, equilibrium can only be maintained with small inflow volumes and is generally confined to the first pit in the system. Once inflow volumes become large enough for saltwater to pass the first sill, salt intrusion becomes difficult to control, regardless of outflow capacity. In contrast, at lower sea salinities, equilibrium can be sustained over a broader range of inflow volumes. In such cases, salt intrusion may extend farther upstream, requiring sufficiently large outflow volumes to maintain control.

Conclusions

This study demonstrates that achieving a dynamic equilibrium in former estuarine systems with controlled saltwater inflow critically depends on the interplay between sea salinity, inflow and outflow volumes. By calibrating an analytical model using results from a detailed 3D numerical model, we identified the parameter ranges under which salt intrusion can be effectively managed.

The analytical model captures key physical processes, including vertical stratification, and closely reproduces both 3D model results and field observations. It offers a fast and practical tool for designing sluice operation protocols, without the need for extensive computational resources.

These findings support to the development of effective management strategies for estuarine restoration, while safeguarding upstream freshwater resources, with broader applicability to similar modified estuarine systems worldwide.

Tsunamis pose a major threat to coastal regions in seismic zones such as Peru. Current tsunami response protocols require time-intensive technical procedures to generate reports, delaying critical decision-making. This study presents the development of a forecasting system that automatically generates standardized technical tsunami reports within minutes. The system integrates numerical simulations, geospatial exposure analysis, and interactive web visualization.

The proposed system employs bathymetric and topographic data, combined with seismic parameters, to automatically define simulation domains along the Peruvian coast. The tsunami generation and propagation are modeled using the Okada and TUNAMI-N2 formulations. Subsequently, census and cadastral data are geospatially intersected with inundation maps to quantify exposed populations and infrastructure, stratified by hazard and exposure levels.

A dynamic web-based platform was developed to visualize simulation outputs and generate downloadable, interactive reports, including wave arrival times, maximum inundation heights, and exposure statistics. The system was tested using historical tsunami scenarios (Camaná 2001 and Pisco 2007), demonstrating its capability to generate technical reports in minutes.

This innovation has direct application for disaster response agencies, national risk management systems, and local governments. The system supports rapid decision-making under emergency conditions and provides a scalable framework for future multi-hazard integration. The proposed tool represents a significant advancement in coastal engineering and disaster risk management for tsunami-prone regions.

Traditional tsunami risk communication tools, such as static inundation maps and educational videos, often lack interactivity and realism, which can limit community engagement and preparedness. In this study, an immersive virtual reality (VR) tsunami evacuation model was developed by integrating high-resolution Unmanned Aerial Vehicle (UAV) imagery and numerical simulation. The model was implemented in Ancon Bay, Lima, Peru, a tsunami-prone coastal zone of Central Peru.

Oblique UAV photogrammetry was used to generate a detailed 3D model of the study area, which was subsequently processed to produce a digital terrain model (DTM) suitable for both numerical modeling and VR integration. A high-resolution tsunami simulation was conducted using the TUNAMI-N2 model under a worst-case scenario that may affect the Central Peru subduction zone. The output inundation data, including flow depths and arrival times, were incorporated into the virtual environment to simulate flood progression over time.

The 3D model and simulation results were imported into a commercial game engine to construct an interactive VR system. This system allows users to explore evacuation routes, observe tsunami impacts from a first-person perspective, and engage with key preparedness elements such as signage, warning systems, and emergency protocols. The application provided an immersive and context-specific risk communication tool, designed to enhance public understanding and institutional training for tsunami evacuation.

This model demonstrates the feasibility of combining UAV-based photogrammetry, numerical modeling, and immersive visualization for disaster risk reduction. The approach is replicable in other coastal areas and offers a novel tool to bridge the gap between scientific hazard assessments and community-level preparedness strategies.

Accurate offshore wind speed forecasting plays a crucial role in site selection, turbine layout, and energy yield estimation for wind farms. This study presents an enhanced data-driven prediction framework based on a Backpropagation (BP) neural network optimized by Particle Swarm Optimization (PSO), aiming to improve short-term wind speed prediction accuracy under data-limited conditions. The model is trained using hourly wind speed data from 2020 to 2022, collected at a coastal meteorological station in northeastern China. Two modeling strategies are implemented: (1) a multi-year unified training approach capturing long-term temporal dependencies, and (2) a seasonal decomposition strategy in which spring, summer, autumn, and winter data are modeled independently using dedicated BP-PSO models.

To evaluate forecast performance, model predictions are compared with measured wind speed from January to July 2023. The multi-year model achieves superior performance with RMSE = 1.235 and MAE = 0.924, indicating strong generalization across different seasonal conditions. Seasonal models demonstrate varying accuracy: spring (RMSE = 1.243), summer (RMSE = 1.324), and combined seasonal (RMSE = 1.255). These results suggest that although season-specific training may enhance interpretability, it does not necessarily outperform global training due to limited seasonal data and lack of hyperparameter adaptation.

In conclusion, the proposed BP-PSO model offers a robust and low-cost solution for wind speed forecasting in offshore applications. The multi-year framework demonstrates better generalization, while seasonal modeling provides insight into intra-annual wind variations. These findings support the use of hybrid optimization algorithms in enhancing wind resource assessments under real-world operational constraints.