This study proposes a hybrid integrated method for floating offshore wind turbines (FOWTs) incorporating nonlinear hydrodynamic load modeling. The method establishes a hybrid nonlinear wave field–structure coupling framework by integrating the fully nonlinear incident wave field generated by OceanWave3D into OpenFAST. To enhance the prediction accuracy of nonlinear wave-induced responses, the nonlinear Froude-Krylov forces are evaluated from the precomputed wave pressure field and integrated over the dynamically reconstructed wetted surface. Hydrostatic restoring forces are corrected based on the instantaneous center of buoyancy, while diffraction and radiation contributions from potential-flow theory are scaled in proportion to the instantaneous wetted volume to accurately capture motion-induced variations in the hydrodynamic loading. The proposed integrated method is validated against experimental data from the WINDMOOR project, including free-decay and irregular wave tests, demonstrating good agreement in platform motions and mooring line tensions. Compared with conventional linear potential-flow tools, the hybrid approach significantly improves the prediction accuracy of low-frequency platform motions and mooring line tensions under irregular sea states. The hybrid integrated method provides a robust and computationally efficient tool for nonlinear analysis and design assessment of floating wind turbine systems.
Anomaly detection in Unmanned Surface Vehicles (USVs) is essential for maritime safety and autonomous navigation. Existing methods mainly rely on external observations such as GPS trajectories and navigational images, which are often insufficient for characterizing internal system states and explaining anomalous behaviors under complex marine disturbances. To address these challenges, we propose a Dual-Channel Explainable Multimodal Learning (DCEML) framework for tri-modal anomaly detection in USVs by integrating GPS trajectories, navigational images, and system logs. Firstly, we design a Log Dual-Channel Module (LDCM) that models log anomalies from structural and semantic perspectives to extract traceable internal evidence. Second, we develop a Tri-Modal Feature Fusion Module (TMFF) that aligns and integrates trajectory, image, and log features in a unified latent space through attention-based interaction, thereby improving robustness of anomaly discrimination under multisource ambiguity and conflict. Extensive experiments on a real-world USV dataset demonstrate that the DCEML framework consistently outperforms existing unimodal, general multimodal, and strong dual-modal baselines. Results show that introducing system logs not only improves detection accuracy and robustness, but also supports post-hoc traceability and diagnostic analysis. This work highlights the potential of jointly utilizing behavioral, environmental, and internal-state information for reliable anomaly detection in complex maritime environments. The code is publicly available at https://github.com/saxd-n/DCEML-TVCL.
In this paper, a numerical simulation is conducted to investigate the self-propulsion process of dolphins at a maximum Reynolds number of 3.5 × 106. The flexible deformation and overall self-propulsion of dolphins are achieved by employing dynamic mesh and overset mesh technique. This study systematically analyzes the effects of parameters, including traveling wave length, traveling wave frequency, and incoming flow velocity, on the propulsion performance of dolphins. The simulation results indicate that the average forward velocity of dolphins approximately exhibits a logarithmic relationship with the traveling wave length, while it approximately follows a linear correlation with the traveling wave frequency and incoming flow velocity. Specific fitting functions are provided in this paper, and this conclusion holds significant guiding value for the development and commissioning of subsequent bionic prototypes. Regarding propulsion efficiency, as the traveling wave length and traveling wave frequency increase, the propulsion efficiency first increases and then decreases, with optimal motion parameters corresponding to the maximum efficiency; the respective optimal parameters are λ* = 1.0 and f = 1.5 Hz. For the incoming flow velocity, the forward efficiency of dolphins gradually decreases with the increase of incoming flow velocity, which is mainly attributed to the increased energy consumed to resist the impact of the incoming flow. Finally, this paper analyzes the flow field structure under typical operating conditions and preliminarily explores the internal mechanism of the dolphins' high-efficiency propulsion from the perspectives of the evolution of the vortex structures in the wake of flow field and the pressure variation on the dolphin's surface.
The wave-induced pore-pressure diffusion and effective-stress around embedded foundations are strongly affected by seabed stratification. However, their interactions with suction-anchor boundaries remain resolved. This study develops a finite-volume model within OpenFOAM for multilayered poro-elastic seabeds containing a suction-anchor boundary. Hydrodynamic loading obtained from the olaFlow simulation and is transferred to the seabed solver as the excess pressure on the seabed surface and equivalent time-varying traction on the anchor lid. The seabed response is governed by Biot’s u−p approximation. Layer-dependent properties are assigned at cell centres, and a harmonic-normal internal-face treatment is used to preserve hydraulic transmissibility across soil-layer interfaces. A consolidation analysis supplies the initial effective stress field induced by layer-wise self-weight and structural loading. The validation against the previous layered-seabed solutions is followed by simulations of a three-layer seabed with a finite-thickness suction anchor. The results show that stratification changes both the amplitude and phase of transient pore-pressure and effective-stress responses. Local stress and seepage responses are amplified when the skirt tip coincides with a soil-layer interface. The liquefaction potential, shear-failure potential, and seepage response occupy related but non-identical regions. This highlights the need to interpret the layered-seabed instability around suction anchors through coupled hydraulic and stress mechanisms.
Anchorage management in crowded, regulated waters combines geometric packing with safety margin, relocation burden, and admission-control requirements: a chart-fitting layout may still leave too little clearance or require excessive relocation of already anchored vessels. This study presents Dynamic Relocation-Aware Anchorage Layout Optimization (DRALO), an event-driven framework for Izmir Anchorage Area No. 2 that separates geometric feasibility from operational acceptability. DRALO combines a direction-neutral circular vessel domain, chain-length and wind/current buffers, relocation-aware continuity, short-horizon preview, and bounded-deferral admission control, screening layouts for feasibility, acceptability, local-rule eligibility, and overload recovery. In the seven-event reference sequence, DRALO remained acceptable through the 12-vessel stress case and converted the 14-vessel overload into an acceptable 13-vessel state after one deferral. Against the strongest static comparator, a relocation-aware warm-start genetic algorithm, DRALO retained its late-sequence advantage in a seven-seed overload probe, remaining finally acceptable in 7/7 runs versus 5/7. Observed-data checks using five AIS snapshots, EMODnet depth reruns, sensitivity analysis, and transfer-style tests show that optimized layouts are geometrically recoverable, yet dense observed occupancy remains relocation-limited. The method is positioned as an offline planning and capacity-analysis tool.
