An offset pre-swirl energy-saving device to improve ship propeller's propulsion efficiency is developed and evaluated usingCFD (Computational Fluid Dynamics). For illustration, the device particularly for a new bulk carrier is designed. TheRANS (Reynolds-Averaged Navier-Stokes) method is employed to numerically calculate the resistance on the ship and the wake field at the propeller disk plane. Based on the wake field characteristics, the offset pre-swirl energy-saving device is designed, and its key design parameters are optimized for efficiency of the wake flow rotational energy recovery. Numerical calculation shows that this device can lead to an energy-saving of 3.1%, approximately 20% improvement over conventional pre-swirl energy-saving devices.

A novel tail fin propulsion mechanism is developed for large-scale BCF (Body and Caudal Fin) biomimetic fish. This mechanism employs the crank-rocker principle to convert steady motor rotation into sinusoidal oscillations, enabling stable tail fin undulation and precise dual-joint angular adjustments through a single power source. This mechanism is characterized by compact structure and simplified control. The prototype of the large biomimetic fish adopts a metal skeleton-skin configuration, effectively resolving assembly precision issues in large-scale motion systems while enhancing mechanical reliability. Self-propulsion tests in a towing tank are conducted to measure swimming speeds by tail fin oscillations, validating the propulsion mechanism's efficacy. The achievement provides technical support for applications of large biomimetic fish in marine monitoring and underwater rescue.

The ship mast vibration caused by excitation source coupling is analyzed as an element of ship system. The case of a Panamax bulk carrier is analyzed for illustration. The mast vibration prediction method is derived based on the ship'sglobal-structure-mast-coupled finite element model established in ship design phase. During sea trials, multi-channel vibration measurement technology is applied to conduct full-scale vibration measurement of the mast. The accuracy of the numerical predict method is verified against the full-scale measurement data. The results show that the error of the vibration predicting method can be controlled within 3%.

To address schedule management challenges in resource-constrained ship outfitting projects, a progress management model is developed based on global project resource scheduling. This model utilizes the AHP (Analytic Hierarchy Process) to decide resource allocation priorities for subtasks, establishing a resource scheduling framework under constrained conditions. Network planning techniques and priority rule-based heuristic algorithms are applied to separately allocate resources for critical and non-critical tasks. Trial results demonstrate that this model enables proactive resource configuration interventions, effectively improves resource utilization efficiency, and resolves issues of resource shortages or wastage.

The fuel switching process of a large methanol dual-fuel powered container ship is simulated and analyzed to accurately predict the nitrogen purging effectiveness in the fuel supply system and investigate methanol fuel purging patterns. The numerical simulations of the purging process are performed under the ship's actual operation conditions based on the 3D model of the fuel supply system, methanol fuel's physical properties, and purging process flow. The purging performance under constant inlet pressure is predicted and the results are compared with freshwater flushing simulation results. This analysis clarified the effects of different media and pipeline volumes on purging time and methanol residue rates, providing practical references for safely and effectively executing nitrogen purging operations.

To enhance the safety of maritime operations, an in-depth analysis of cybersecurity risks faced by oceangoing vessels is conducted, along with the exploration of corresponding mitigation strategies. The study systematically categorizes major cybersecurity risks for oceangoing vessels, including external hacker intrusions, malware attacks, internal human errors, and inherent system vulnerabilities. Seven targeted mitigation strategies are proposed, emphasizing the adoption of advanced cybersecurity technologies and solutions-particularly the integration of AI (Artificial Intelligence) and ML (Machine Learning) technologies to strengthen threat detection capabilities. The strategies also highlight the need to intensify cybersecurity training for crew members to improve overall security awareness, implement rigorous data backup and recovery mechanisms, and employ dedicated networks and isolation technologies to reduce risks from external attacks.

With the globalization trend of the chemical industry, the operational efficiency and safety resilience of liquid chemical terminals have attracted much attention, and traditional management models are no longer suitable for the needs of modern logistics. This paper, based on real-life application scenarios, designs and develops a digital twin platform for smart liquid chemical terminals. The platform adopts a layered architecture, including data source layer, base engine layer, general capability layer, architecture fusion layer, and scenario empowerment layer. Implementation of integrated, visualized, and intelligent management and control of terminal operations at all levels is realized. Among them, the dock environment monitoring subsystem integrates multiple heterogeneous sensors to achieve real-time and comprehensive monitoring of the dock operation environment; The production safety management subsystem integrates multiple key systems to build a comprehensive safety protection system; The intelligent operation and maintenance subsystem of the dock utilizes digital twin technology to achieve real-time mapping of equipment status and intelligent operation and maintenance management. The system has been developed and integrated in a liquid chemical terminal shore area, achieving succesful results.

To address the congestion issues on highways caused by significant traffic flow variation to improve the road transportation efficiency and reduce the operational costs of traffic management departments, this paper conducts vehicle profiling research based on vehicle travel behavior and trajectory characteristics. The key vehicles that frequently appear on the road network are identified and occasional driving data are ignored; A travel behavior portrait system is constructed. The system operates based on a three-level labeling system, covering six core dimensions including vehicle information, travel frequency, duration, speed, distance, and travel preferences. It produces comprehensive and multidimensional vehicle portraits for peak hours and off-peak hours; An improved Analytic Hierarchy Process (AHP) algorithm and entropy weight method are used to decide the weights of lower level labels and calculate the score of the top level labels; The vehicle portraits are categorized into 6 categories by the K-Means clustering algorithm based on their scores; The PrioritSpan algorithm is used to mine the typical path patterns of the six types of vehicles at different time periods, generating sets of travel patterns for different types of vehicles. The effectiveness of the system is proved by experiments.

Drawbacks of traditional soil and water conservation monitoring methods, such as low efficiency, terrain limitations, and incomplete data integrity is overcome by introduction of UAV (Unmanned Aerial Vehicle) in the comprehensive soil erosion control project in Qingshan Small Watershed. By processing UAV imagery to generate high-precision DOM (Digital Orthophoto Maps) and DSM ( Digital Surface Models), geographic information such as bare land area, vegetation coverage, slope length, slope width, and slope gradient were automatically extracted. Using the acquired slope length and gradient data, the RUSLE (Revised Universal Soil Loss Equation) was applied to simulate soil loss. A comparison between simulated and field-measured data from six monitoring sample areas was conducted. The experimental results demonstrated minor discrepancies between simulated and measured values, with relative errors ranging from 3.13% to 11.80%, indicating that the simulation model effectively reflects soil loss dynamics. This project has confirmed that UAV technology combined with modeling can provide robust support for soil and water conservation monitoring.

A study on pollution control performance evaluation indicators for stormwater drainage systems is conducted to support urban stormwater management planning and to safeguard river water quality. Focusing on stormwater pumping systems within separated drainage systems, this research systematically analyzes pollutant sources in stormwater networks, and key factors influencing their discharge, through literature review and practical case studies. The formation mechanism of overflow pollution is studied. Based on drainage engineering characteristics, multiple critical indicators reflecting pollution control effectiveness in stormwater networks are set up. The analysis reveals that pollutant management in stormwater systems can be performed in three aspects: source control, process control, and end-of-pipe control. Specifically refined indicators are developed for each aspect with clear definitions, providing foundational references for establishing a comprehensive evaluation framework for pollution control in stormwater drainage systems.