To address the issue that navigational drag performance of an unmanned quadramaran is affected by both longitudinal and lateral separations of multi-bodies, the entire layout of an self-designed unmanned quadramaran is optimized by comprehensively comparing drag coefficients of various schemes under different navigation conditions. Firstly, a variety of layout schemes are constructed to compare and analyze calm-water resistance coefficients under three different speed conditions by using computational fluid dynamics numerical simulation methods and Fluent solver. Secondly, a comprehensive resistance coefficient evaluation function is designed to optimize the layout scheme considering resistance performance. Finally, the improvement effect of layout optimization on the overall navigation performance is further verified by comparing the wave height and surface pressure before and after optimization. Simulation results show that the longitudinal distance is the main factor affecting the overall navigation resistance of quadramaran, while the optimized layout of multi-bodies reduces the comprehensive resistance coefficient by 12.9% compared to that before optimization, thereby effectively reducing wave height and surface pressure of the unmanned quadramaran during navigation.

A self-following dual-mode obstacle avoidance model predictive control method based on improved dynamic window method was proposed for the trajectory tracking control problem of unmanned surface vehicle (USV) formation under the influence of sudden obstacles. Firstly, the longitudinal velocity and bow angular velocity oscillation constraints were introduced to reduce the jitter of the velocity and bow angle in the design of the evaluation function of the dynamic window algorithm so as to achieve better integration of obstacle avoidance planning and control. A strategy of calculating the steering azimuth based on intermediate distance was proposed to reduce the sharp turns in the obstacle avoidance process of the USV, while an evaluation term based on the deviation of the predicted and the expected position ending was designed. Meanwhile, the formation keeping information was combined to correct the obstacle avoidance endpoint and reduce the deviation of the USV formation. Secondly, based on the linearized Taylor expansion principle, a prediction model for USV formation was designed. The prediction value of the model was corrected by the prediction error between the system output measurement and the model prediction values. At the same time, a rolling finite time domain iterative online optimization strategy was adopted to propose a trajectory tracking model prediction control method for USV formation that integrated autonomous following dual-mode obstacle avoidance strategy. Finally, a nonlinear disturbance observer was designed to compensate the environmental disturbances, while the Lyapunov function was constructed to prove the stability of the system by combining with the terminal penalty theory. Eventually, the effectiveness and reliability of the proposed USV formation obstacle avoidance and trajectory tracking control algorithm were verified by simulation experiments.

A ship intelligent collision avoidance decision model based on deep reinforcement learning (DRL) algorithm was proposed to address the frequent collision accidents caused by the global increase in the number of ships at sea. The model was based on adversarial dual deep Q-learning (Dueling DDQN) and the establishment of ship domain models, and when designing the reward function, factors such as COLREGs (International Regulations for Preventing Collisions at Sea) and ship deviation were fully considered to ensure the compliance and rationality of collision avoidance decisions. A simulation environment was constructed to simulate the scenario of multiple ships encountering, and neural network models were used to process complex environmental information for model training and validation. Experimental results show that compared with traditional deep Q-learning algorithms, the model proposed in this paper exhibits significant advantages in convergence speed and stability, which can accurately determine the encounter situation and take appropriate collision avoidance measures based on COLREGs, demonstrating high decision accuracy and reliability. It can provide effective decision support for intelligent navigation of ships in complex sea conditions.

In view of current efficiency of VTS and AIS manual analysis cannot meet the increasingly severe situation of water traffic supervision, a method for detecting abnormal behaviors of ships in the waters of harbor waterways was proposed by considering the navigation characteristics of ships entering and leaving ports. Firstly, considering influence factors such as ship type and navigation rules, a ship trajectory clustering method based on semantic trajectory multi-dimensional similarity was established to identify traffic patterns of ships entering and leaving the port that comply with navigation rules. Secondly, a semantic transformation model was constructed to convert traffic pattern trajectory data into pattern trajectory text, while the text cosine similarity method was used to match the traffic pattern of the target ship. Furthermore, a ship anomaly behavior detection model was constructed by using kernel density estimation. Taking Tianjin Port as an example, 40 kinds of inbound and outbound traffic patterns were extracted from historical ship trajectory data to construct the ship abnormal behavior detection method, and validated by using simulated data from navigation simulator. Results show that the proposed method can effectively detect ship abnormal behaviors, providing assistance in waterway supervision. 

In order to effectively utilize the large amount of boil off gas (BOG) generated during the operation of liquefied natural gas (LNG) ships, the ship allocation problem for LNG sea transportation routes considering BOG management was studied. A ship allocation model was established for LNG sea transportation routes at economical speeds using BOG generated from LNG as propulsion fuel. On this basis, a BOG management strategy was designed based on the relationship between ship speed and BOG demand, while a joint optimization model for LNG sea transportation route allocation and ship speed considering BOG management was established. Taking an LNG shipping company in China as an example, the lowest cost, optimal ship allocation plan, and voyage schedule corresponding to two models were obtained. Comparing the optimal solutions of the two models, it can be seen that the BOG management strategy can effectively reduce costs by more than 8%, while corresponding management suggestions are proposed from the perspective of LNG spot price fluctuations and the business scale of shipping companies. Research results show that the proposed method can provide decision support for LNG shipping companies to reasonably allocate transportation resources and effectively manage LNG ship evaporation gas.

Considering the impact of the boundary width of the emission control area (ECA), a mixed-integer nonlinear programming model is established with the objective of minimizing the total operational costs of the vessel, thereby optimizing emission reduction strategies and sailing speed. The Southeast Asian route of a certain company is taken as an example for solution and sensitivity analysis. Results show that the cost comparison of different emission reduction schemes is mainly affected by the level of fuel oil prices, while carbon taxes have no impact on the cost comparison results of different schemes. From the current various fuel oil prices, desulfurization towers installation is the most economical emission reduction measure, which is followed by the ECA internal and external fuel conversion strategy, using MGO fuel strategy, and using LNG fuel strategy, respectively. As the width of ECA decreases and the difference in fuel oil prices decreases, the cost of fuel conversion schemes will be lower than that of installing desulfurization towers. When the initial investment cost of using LNG fuel is reduced to 80% or less, or the price of LNG fuel is reduced to 50% or less, while the price of MGO fuel is invariable or higher, the cost of using LNG fuel will be lower than that of using MGO fuel. The setting of the ECA width will affect the emission reduction strategy of shipping companies, while by appropriately increasing the width of the ECA, the behavior of ship companies switching fuels inside and outside the ECA can be reduced.

To realize the non-contact measurement of propeller geometric parameters, a region growing algorithm based on adaptive octree was proposed to extract the point cloud model of an propeller. Firstly, the adaptive octree was utilized to partition the point cloud data into non-uniform voxels. Secondly, by combining with the spatial connectivity and smoothness of voxels, the region growth criteria were set, such the feature attributes of voxels were utilized for region growth to extract the propeller point cloud model. Then, the orthogonal experimental method was used to optimize the algorithm parameters. Finally, a comparative experiment was conducted between the proposed algorithm and the traditional octree based region growing algorithm as well as the point based region growing algorithm. Results show that the algorithm can achieve accurate extraction of the propeller point cloud model, while the segmentation accuracy can reach 99.5%, which is 1.8% and 1.3% higher than the other two algorithms. The execution time is 1045 ms, which is 4.1% and 5.6% of the other two algorithms. The efficiency of point cloud segmentation can be significantly improved.

The issue of reclaiming scheduling in dry bulk terminal yards was examined, with the objective of enhancing operational efficiency, minimizing delays, and optimizing the advantages of ship loading mixing operations. An optimization model was developed to schedule the reclaiming operations at a dry bulk terminal, considering the loading mixing process, in order to reduce delays. The integrated optimization of reclaiming scheme formulation, stacking position allocation, and reclaimer scheduling was conducted, by considering constraints such as dynamic yard storage of various goods, mutual interference of reclaimer operations, and proportional synchronous reclaiming of multiple stacking positions. Based on the characteristics of the problem and model, a heuristic algorithm was designed to efficiently solve large-scale problems by using adaptive large neighborhood search. The experimental results show that, compared to a general solver, the method proposed in this paper can obtain high-quality material retrieval scheduling schemes for large-scale cases within 1 hour. Compared with the squeaking wheel algorithm, the average improvement of the objective function value in solving large-scale cases is 19.9%. Meanwhile, this method comprehensively considers the integrated optimization of stacking allocation and reclaimer scheduling for mixed loading. Compared with the results obtained without considering mixed loading, the total delay of reclaimer operations reduced by an average of 28.35%，which can provide theoretical support for dry bulk terminals to allocate yard resources and plan operations when considering mixed loading process.

When optimizing the reservation quota scheme, existing methods faced many challenges, including difficulty in capturing dynamics and randomness, limited theoretical assumptions, and complex data acquisition and processing. GPS trajectory data of container trucks was used to comprehensively capture the entire process of container truck turnover from arrival at the port to completion of operations, and based on this, establishes a mapping relationship between the number of container truck arrivals within the unit appointment time window and their total turnover time at the port. A more precise and concise quota optimization model was constructed by using this mapping relationship, and the quota appointment system for container trucks was optimized and solved. The experimental results show that the optimized reservation quota scheme effectively reduces the arrival volume of container trucks during peak periods, while increasing the number of vehicles arriving during off peak periods, significantly reducing the total turnover time of container trucks at the port, improving the operational efficiency of the terminal, and thereby enhancing the competitiveness of the port.

Focusing on the core audience of the Mobility as a Service （MaaS）service platform, namely public transportation users, this study aimed to investigate the underlying mechanisms of their willingness to use the MaaS service platform and gained a deeper understanding of the driving factors that encouraged this group to use MaaS services. This paper divided the public transportation user group into passive passenger group and selective passenger group. Based on the questionnaire survey data of Dalian City, Liaoning Province, a multi-index and multi-factor structural equation model was used to deeply explore the satisfaction level of these two groups with the current level of public transportation services, and their attitudes and behavioral intentions toward using MaaS services. Research has found that passengers’ attitudes and willingness to use MaaS services are positively correlated with their satisfaction with public transportation services, but there are mechanism differences between the two types of passengers in the process of converting satisfaction into MaaS usage willingness. Selective passengers’ satisfaction directly converts into usage intention, while passive passengers first change their attitudes towards MaaS, thereby affecting their usage behavior. Therefore, when promoting MaaS services, differentiated strategies should be developed for different passenger groups，that is，for selective passengers, the efficiency and convenience of MaaS services should be highlighted, while for passive passengers, the quality of MaaS services and environmental comfort should be reflected. In addition, factors such as gender, age, and transfer frequency also affect the willingness to use MaaS. The above findings provide decision-making basis for relevant departments to plan MaaS service platforms, optimize public transportation services, and improve MaaS penetration rates.

A dual-branch residual convolutional neural network was proposed for image enhancement in PIV velocity technique to obtain high-quality particle images. Firstly, a dual-branch convolutional neural network composed of residual blocks was designed to extract features from the input particle image pairs, while a coding-decoder was used to effectively fuse the feature information of the particle image pairs. Secondly, a challenging image enhancement dataset was autonomously generated to train model parameters, including Gaussian noises of different concentrations, light intensity noise and various real interference backgrounds, thereby fully simulating real fluid scenes. Results show that the proposed method can effectively deal with noise interference in both synthesized and real images, achieving image enhancement. Meanwhile， higher precision velocity fields can be obtained by using velocity field estimation algorithm to process the particle image pairs enhanced by the proposed method in this paper.

The feature differentiation of different categories of ship targets in SAR images is not clear, and the recognition accuracy may decrease when there are many ship categories. To better extract category features, this paper proposed a recognition model DCN-MSF-TR, which drawed on the idea of Transformer encoder-decoder and added a deformable convolutional module (DCN) to the backbone network. At the same time, the feature layers processed by Transformer multi-scale self attention were fused at appropriate positions in the model in a feature pyramid manner, and each layer can not only utilize its own information, but also comprehensively utilize the features of other layers. The validation results on the Open SARShip-3-Complex three class dataset and Open SARShip-6-Complex six class dataset show that the average recognition accuracy reaches 78.1% and 66.7%, respectively, which show that the proposed method can more effectively identify ship categories in SAR images compared to other recognition models.

To improve the conversion rate of HC in natural gas/diesel dual fuel engines under low load, a three-way catalytic converter based on reverse flow was designed, and a mathematical model of the chemical reaction process of HC and CO catalytic conversion in a single channel was established, which was solved by ANSYS FLUENT software, while the numerical simulation of the working characteristics of the reverse flow three-way catalytic converter was carried out. Results show that the designed reverse flow three-way catalytic converter can effectively control the emission of HC when the engine is under low load condition. The flow direction transformation operation significantly improves the conversion rates of HC and CO compared to unidirectional flow. If the influence of shift mutation is ignored, the conversion rate of HC and CO is increased by 3.9%~6.4% and 2.0%~2.7%, respectively, while the conversion rate of NO_x is relatively low, i.e., only about 0.6%. The design of the switch time of the converter is not easy to be too large or too small, and it is recommended that the switch time is 10 s~15 s. With the increase of inlet velocity, the maximum temperature of the converter decreases, while the emissions of HC and CO increase and the conversion rate decreases, but the impact on the conversion of NO_x is small.

In order to accurately predict the stiffness of the elastic squirrel cage structure, the genetic algorithm based on the stiffness theoretical formulation was adopted to optimize the geometric parameters of the squirrel cage, and a three-dimensional finite element model for the optimized squirrel cage was established. The stiffness of a squirrel cage under Remote force and Bearing load were calculated and compared by using ANSYS software, and the effect of root fillet dimensions on the stiffness of the squirrel cage was analyzed. An elastic cage stiffness experimental system was designed and built, and the stiffness test results of the elastic cage specimens were compared with the numerical simulation results. The results show that the deviation between the stiffness calculated by finite element method using Remote force and Bearing load and the stiffness tested in the experiment is within 3%. In the engineering design analysis of elastic cages, the finite element method can significantly improve the prediction accuracy of the stiffness of elastic cages compared to the theoretical formula method.

A low Reynolds number flow simulation around a cylinder for non-zero mean oscillatory inflow was carried out to study the differences in stress and flow field morphology of structures under different oscillation periods. Firstly, based on the self-developed underwater flow field simulation software zFlower, a module for inlet boundary conditions under unsteady inflow was developed. On the basis of a uniform and fully developed flow field, oscillation flow simulations with high and low frequency ratios were carried out to obtain the force state of the cylinder and the evolution process of the flow field under oscillation flow conditions. Results show that when the frequency of the incoming flow is high, the oscillating incoming flow will affect the vortex shedding morphology of the flow, while the peak transverse force on the cylinder is relatively large. Meanwhile, when the incoming frequency is low, the absolute value of the lateral force acting on the structure does not change significantly, but the frequency spectrum of the force is complex, which may make it easier to excite the flow induced vibration of the structure.