Mooring ropes are essential components of ships and offshore floating structures and they are subjected to cyclic axial loads. This study investigates the evolution of the full-cycle stiffness of fibre polyester ropes under long-term static and dynamic loading. First, the static stiffness characteristics of the ropes, including the rope elongation properties at different stages, shrinkage rates, and creep coefficients after an idle period, are examined under static loads; an empirical formula for static stiffness is established. Second, the dynamic stiffness characteristics of the ropes are investigated under cyclic loads that are typical of platform production operations. The stabilities of the structure under different tensions are compared; the effects of mean tension, tension amplitude, and load cycle on the dynamic stiffness of the ropes are analysed and an empirical formula is established to predict the dynamic stiffness during the engineering design phase. The results of this study can be helpful for the rational design of deep-sea taut-leg mooring systems because they present the evolution of the full-cycle stiffness characteristics of mooring ropes.

To determine the loading conditions considering the action of both bending and torque moment for a passenger catamaran moving among waves, a method for calculating equivalent design waves under multiple load control parameters was derived based on wave load prediction results using three-dimensional potential flow theory. The method was developed by defining the wave amplitude discrepancy factors between the primary and second load of the combined bending and torquing equivalent design wave. The primary goal was to find a reasonable design wave. Finally, the design waves of a target passenger catamaran ship were calculated using the proposed method, and each load component of every design wave for the target hull was recalculated. The average error compared with the object load component was less than 1%, which verifies the effectiveness of the method and offers an effective engineering evaluation method for a catamaran.

This paper presents a novel dynamic modeling framework for gantry crane systems based on the cart double pendulum with dual cables (CDPD) model. The CDPD model systematically incorporates the effects of dual suspension cables, equalizer beams, and closed-chain kinematic constraints, enabling an accurate simulation of both symmetric and asymmetric lifting scenarios. Utilizing Kane’s method, the model efficiently handles redundant coordinates and holonomic constraints, resulting in a compact and numerically robust formulation. Validation results demonstrate strict energy conservation and consistency with traditional models in limiting cases. The proposed approach provides a unified and extensible foundation for the advanced analysis, control, and optimization of large-scale gantry crane operations.

Ship structural health monitoring (SHM) systems are essential for ensuring operational safety. Monitoring stress distribution is a critical function of such systems. Analysis of stress data collected by pressure sensors can enhance understanding of SHM performance and facilitate the prevention of structural failures. In this study, we propose a machine learning–based computational method to analyze the stress distribution of a crane ship. The method first employed extreme gradient boosting (XGBoost) to evaluate relationships among stress data from multiple pressure sensors, and then used multilayer perceptron (MLP) to construct regression models. For each pressure sensor, an optimal MLP regression model was established to infer its stress values from those of related sensors. These models demonstrated strong fitting performance on both training and independent test datasets. Furthermore, the method identified key pressure sensors whose measurements were particularly important for recovering stresses at most monitoring points. These findings improve interpretability of the method and provide insights into the mechanisms of the monitoring system. The robustness and rationality of the approach were further examined through ablation tests, confirming its effectiveness for ship SHM applications.

As large common areas, cruise ship atriums affect passenger comfort and HVAC efficiency. Due to their complexity and high occupancy, maintaining a suitable thermal environment is difficult. Experimental measurements, thermal load analysis, and CFD simulation are used to assess and improve the atrium’s summer thermal climate. Experimental data supported the use of the RNG $k$-$\epsilon$ turbulence model to forecast airflow and temperature. To meet the cooling demand of 28,784 W, a supply air volume of 10,742 m³/h was required. Various air-supply methods were evaluated for temperature distribution, airflow velocity, PMV, and air age. Larger diffusers and better air dispersion increased temperature homogeneity, air age, and comfort. Redistributing airflow to corridors reduced localized overheating but raised core temperatures, whereas adding diffusers without boosting supply volume caused interference. The configuration with larger diffuser areas and equilibrated airflow maintained a temperature of 21–23 $°\mathrm{C}$, a PMV of −0.1 to 0.1, an air velocity of 0–0.3 $\mathrm{m}/\mathrm{s}$, and an average air age of 350 s. The findings provide theoretical and engineering guidance for energy-efficient HVAC systems in cruise ship atriums and other large public spaces.

To reduce diagnostic uncertainty in hydraulic systems during vessel operation, this study investigated the correlation between fault features and monitoring parameters. Using monitoring data collected under normal operating conditions as the baseline, the association strength between multi-source monitoring features and ten fault modes was quantified via standardized deviation (Z-scores). On this basis, a hierarchical weight matrix, incorporating correlations between faults and features, devices and features, and subsystems and features, as well as lateral coupling coefficients, was constructed to capture hidden associations among signals, devices, and subsystems. Subsequently, a PGC-LightGBM algorithm was proposed, which embedded prior knowledge from the hierarchical weight matrix into model training through differentiated sample weighting: fault samples exhibiting larger deviations on physically correlated features were assigned higher training weights, thereby enabling multi-level fault diagnosis that accounts for multi-fault coupling and operational interactions across the entire hydraulic system. On a marine hydraulic pump station test bench dataset, the proposed method improved the macro-averaged F1 score from 75.3% to 77.8% under 5-fold stratified cross-validation, and from 63.2% to 68.2% on the independent test set. The fault-class average F1 rose from 59.9% to 65.4%, while the normal-class F1 of the weighted model reached 96.3%, remaining unaffected compared with 95.7% for the standard model. Ablation studies indicated that fault-level physically informed weights were the main contributing factor to the performance improvement, while device-level, subsystem-level, and lateral coupling components provided richer physical interpretability. This study provides an effective reference for reliability analysis and risk prediction in marine hydraulic systems.