Journal of Marine Science and Engineering

Route planning for the Northeast Passage must balance transportation costs against navigational risks arising from changing sea ice conditions. Route-wide navigability assessment under adverse scenarios requires explicit representation of sea ice forecast uncertainty. This study proposes a multi-objective route-planning method integrating seven-day sea ice forecasts, paired scenario construction, path-level conditional value-at-risk (CVaR) assessment, and receding-horizon planning. Sea ice scenarios are constructed from seven-day forecasts produced by a convolutional long short-term memory (ConvLSTM) model and historical paired residuals. A* search generates candidate routes using weighted edge costs based on voyage time, propulsion fuel consumption, and cumulative ice-condition severity, the time integral of a local severity score over sailing and waiting. For each scenario, the loss counts each edge of the remaining route once if it is unnavigable at any time within the seven-day forecast window. The CVaR of this loss is included as the fourth objective in Pareto nondominated filtering and fixed-scale scoring to select the route for execution. Model predictive control (MPC) replans over a seven-day horizon and executes for up to 24 h from the updated vessel position and cumulative execution state. In the 2024 evaluation, day-7 root mean square errors (RMSEs) for sea ice concentration and thickness were 24.23% and 22.87% lower, respectively, than those of the persistence baseline. The expanded evaluation compares four strategies in 64 simulations across 16 departure dates in 2024 using one parameterized Arc6-class Aframax tanker. The static strategy completes all 16 voyages; each of the three dynamic strategies completes 15. Across the 15 departure dates with completed voyages under all four strategies, the proposed strategy reduces mean cumulative ice-condition severity by 0.379 score·h relative to the dynamic three-objective strategy, with mean increases of 0.138 h in voyage time and 0.598 t in propulsion fuel consumption. The framework converts forecast uncertainty into path-level window CVaR for decisions on transportation cost, ice-condition severity, and forecast-window navigability risk.

Heterogeneous unmanned surface vehicle–unmanned aerial vehicle (USV-UAV) systems enable sea–air collaborative missions including marine monitoring, search and rescue, and coastal patrol, with rapidly growing demand in complex ocean environments. Existing formation control methods face critical challenges in unified formation description for heterogeneous dynamics, flexible maneuvering in constrained spaces, obstacle avoidance integration, and dependence on global velocity information. This paper proposes a distributed affine formation maneuver control scheme for heterogeneous USV-UAV systems. A hierarchical control architecture is established with a virtual leader layer for affine trajectory planning and a heterogeneous execution layer for distributed tracking. A stress-matrix-based dynamic affine adjustment mechanism is designed to realize formation maneuvering for obstacle avoidance. A distributed observer is developed to estimate unknown target velocity using only local position measurements. Rigorous theoretical analysis proves the global uniformly ultimately bounded stability of the closed-loop system. Simulation results under multiple complex scenarios verify the effectiveness of the proposed method. In the considered scenarios, the proposed method achieves lower formation-tracking RMSE and a higher obstacle-avoidance success rate than DMPC, CBF, and ETC, but at the cost of a slightly higher per-cycle computation time than CBF and ETC and a slightly higher communication load than the event-triggered baseline. The proposed scheme provides a practical and flexible solution for sea–air heterogeneous collaborative missions in constrained marine environments.

For offshore wind turbines, pile–bucket foundations enhance lateral resistance by mobilizing both shallow and deep soil resistance mechanisms. However, the combined influence of soil conditions and load eccentricity on their lateral performance has not been fully investigated. This study employs three-dimensional finite element simulations to examine the lateral behavior and failure mechanisms of pile–bucket foundations subjected to eccentric loads in three soil types: medium sand, silty sand, and medium clay. The results show that the lateral capacity of the pile–bucket system exceeds that of a single pile by approximately 19.4% in medium sand, 37% in silty sand, and 64% in medium clay. Compared with a standalone bucket foundation, the pile–bucket offers roughly 5.7, 4.0, and 3.0 times greater capacity, respectively. Increasing load eccentricity reduces the lateral bearing capacity by approximately 20–34% across all soil conditions. It also causes the rotation center to migrate upward by about 4–7% in medium sand and silty sand and by 6–8.5% in medium clay. Furthermore, the lateral load-sharing ratio decreases by approximately 6–13% in medium sand, 10–22% in silty sand, and 15–37% in medium clay, whereas the moment-sharing ratio increases slightly by about 2–3% in medium sand and silty sand and by 2–4% in medium clay. Under all soil conditions, the foundation exhibits a rotation-dominated failure mechanism characterized by deep rotational soil movement and wedge-type deformation near the mudline. Although eccentric loading affects the lateral response, the overall failure pattern remains unchanged. These findings provide new insights into how eccentric loading and soil conditions influence the lateral behavior of pile–bucket foundations.