In order to prevent the substantial rolling of ships in intricate and harsh marine environments, ensuring the safety of personnel and cargo during maritime transportation, this paper proposes an intelligent prediction model for ship roll damping based on CTGAN-XGBoost. Firstly, a dataset for roll damping is constructed using ship geometry model data and ship motion data to improve data quality. Then, a Conditional Table Generative Adversarial Network is used to balance and generate more roll damping data, thereby augmenting the quantity of available data. Finally, taking advantage of XGBoost's strong generalization ability and low overfitting degree, an intelligent prediction model for roll damping is established. Experimental results demonstrate that the CTGAN-XGBoost model achieves the highest accuracy with MSE and MAE values of only 0.325 and 0.451 respectively, along with an R2 value of 0.975. It significantly outperforms other comparative models in terms of predictive accuracy.
The multi-moment based numerical model is proposed for performing large-eddy simulation (LES) of wall-bounded incompressible turbulent flows on the unstructured grids. In this model, velocity is defined as volume integral average (VIA) and point values (PVs) which are updated as prognostic variables simultaneously in time. Using VIA and PV in each cell, this method constructs higher-order polynomial on a compact stencil and more importantly, provides a new approach to design subgrid-scale (SGS) models for LES. Various SGS models are developed in this framework where the eddy-viscosity is computed separately at both VIA and PVs using the least-square method on different interpolation stencils. Compared with conventional finite volume schemes, the proposed method effectively reduces numerical dissipation and largely suppresses the sensitivity of numerical solution on SGS models and resolves more elaborated flow structures on the same mesh resolution. It also reduces solution dependence on the shape and quality of grid elements, as demonstrated by comparing numerical results on the different types of grids. The proposed method can attain high-fidelity simulation without loss of numerical efficiency and robustness, which is highly appealing for complex turbulent problems with high Reynolds numbers and complicated geometries.
In order to study repair effect of carbon fiber composites on the ultimate strength of pitting reinforcement plates, feasibility of the numerical simulation method is firstly verified by comparing the numerical simulation with the test results of repairing cracked steel plates with carbon fiber composite.Considering loading characteristics of the reinforced plate unit, ultimate load carrying capacity of the CFRP-repaired pitting reinforced plate was investigated by uniaxial compression, biaxial compression, lateral load and combined load（biaxial compression, lateral load） respectively. The results show that ultimate strength of the repaired pitting reinforced plate can be effectively improved.
针对无人船因控制系统延时而导致的路径跟踪性能退化,提出计及系统延时的路径跟踪控制策略,并设计了基于模型预测控制算法的双层路径跟踪控制器.基于线性模型预测控制算法,设计了 USV运动学控制器,以此减小无人船路径跟踪偏差并得到期望速度.将系统延时处理为一阶系统,并将其和无人船的动力学模型耦合.基于非线性模型预测控制,设计了 USV动力学控制器,用于响应无人船的期望速度.数值仿真结果表明,在存在系统延时的仿真环境中,论文提出的针对控制系统延时的处理策略可减小无人船的路径跟踪偏差和改善控制形态输入的平顺性.
船舶在极地海域航行时,将面对恶劣的冰载荷工况,冰-桨的相互作用致使船舶推进系统产生转速降,系统同时产生较大的扭矩波动,动力推进系统的安全受到威胁.论文以某重型破冰船电力推进系统为研究对象,建立电力推进系统转速-时间仿真模型和扭振时域动态响应计算数学模型,重点研究了冰载荷激励力矩作用时电力推进系统在不同调速控制策略下的电机转速降及轴系扭矩动态响应,通过全转速分析得到不同调速控制策略下电机转速降和轴段最恶劣冰载荷扭矩幅值随初始转速的分布规律,并对推进系统在各冰载荷工况及各冰厚条件下的螺旋桨破冰性能作出了预测,结果可为冰区电力推进装置的设计及性能分析提供参考.
