The rack cylinder is an important part of the pile leg structure of the jack up platform. Because of its complex structure, the flow field around the rack cylinder is different from that around the ordinary cylinder, which brings difficulties to the research of the rack cylinder. In this paper, using CFD(Computational Fluid Dynamics) solved the flow field of chords with different rack height and rack width under different KC and Re, the characteristics of the flow field around the cylinder with rack are obtained. The results show that Re, KC, rack height and rack width all have different effects on the flow field. When Re and KC are constant, Cd will increase with the increase of rack height ratio, the change of Cd and Cl is not significant, while the change of Cd and Cl varies with Re when the chord structure is fixed.

Port operational efficiency is considered as one of the most important competitive factors and plays a critical role in the port development all over the world, especially container ports. Haiphong Port, which is in the northern of Vietnam, is planned to become one of the national and regional ports. To do this objective, it is important to analyse the operational efficiency of its container terminals. The paper aims to comparatively analyse the operational efficiency of 16 container terminals in Haiphong Port from 2016 to 2022 by basic and Malmquist DEA models. With 112 observations collected and calculated in R software, DEA models have five inputs (container yard area, number of quay crane, berth draft, berth length, labour force) and one output (annual cargo throughput). Consequently, Hai An, Tan Vu, and Vip Greenport are more efficient terminals over the 7-year period, whereas Transvina and MIPEC have lower efficiency. Paper contributions are the literature review about port operational efficiency and references to propose resolutions in next author’s research as well as masterplans to develop Vietnam seaport’s system. Besides, the limitations are discussed as the number of observations and environmental factors in ports.

This study explores the course tracking control problem of unmanned surface vessels (USVs) under the influence of actuator faults and internal and external uncertainties. In the control strategy desig n, we first model the unknown dynamics and use adaptive technology to construct an online appro ximator to compensate for the unknown dynamics of the system. Under the framework of adaptive backstepping, a robust adaptive course tracking control scheme is constructed. This control strategy does not require any prior knowledge of the model in advance. The stability analysis of the theoret ical mathematical derivation of the control strategy was conducted based on Lyapunov stability theo ry. Finally, the effectiveness of the control strategy proposed in this paper was verified through sim ulation.

Truck no-show behavior has posed significant disruptions to the planning and execution of port operations. By delving into the key factors that contribute to truck appointment no-shows and proactively predicting such behavior, it becomes possible to make preemptive adjustments to port operation plans, thereby enhancing overall operational efficiency. Considering the data imbalance and the impact of accuracy for each decision tree on the performance of the random forest model, a model based on the Borderline Synthetic Minority Over-Sampling Technique and Weighted Random Forest (BSMOTE-WRF) is proposed to predict truck appointment no-shows and explore the relationship between truck appointment no-shows and factors such as weather conditions, appointment time slot, the number of truck appointments, and traffic conditions. In order to illustrate the effectiveness of the proposed model, the experiments were conducted with the available dataset from the Tianjin Port Second Container Terminal. It is demonstrated that the prediction accuracy of BSMOTE-WRF model is improved by 4%-5% compared with logistic regression, random forest, and support vector machines. Importance ranking of factors affecting truck no-show indicate that (1) The number of truck appointments during specific time slots have the highest impact on truck no-show behavior, and the congestion coefficient has the secondhighest impact on truck no-show behavior and its influence is also significant; (2) Compared to the number of truck appointments and congestion coefficient, the impact of severe weather on truck no-show behavior is relatively low, but it still has some influence; (3) Although the impact of appointment time slots is lower than other influencing factors, the influence of specific time slots on truck no-show behavior should not be overlooked. The BSMOTE-WRF model effectively analyzes the influencing factors and predicts truck no-show behavior in appointment-based systems.

Efficiently detecting the nearest navigational dangers in Electronic Chart Display and Information Systems (ECDIS) remains pivotal for maritime safety. However, the software implementation of ADMAR(Automatic Distance measurement and Ranging) functionality faced challenges, necessitating extensive computations across ENC cells and impacting real-time performance. To address this, we present a novel method employing dynamic programming. Our proposed algorithm strategically organizes nodes into a tree structure, optimizing the search process towards nodes likely to contain navigational hazards. Implementation of this method resulted in a notable sevenfold reduction in computation time compared to the conventional Brute Force approach. Our study established a direct correlation between the ADMAR functionality and node count, achieving error margins deemed acceptable for practical navigation scenarios. Despite this theoretical progress, minor errors in results prompt further refinement. Consequently, future iterations will explore varying values for N, considering hierarchy and cell sizes to enhance algorithmic precision. This research signifies a potential advancement in optimizing navigational danger detection within ECDIS, offering a promising avenue for improved efficiency. By introducing a dynamic programming-based approach, we have streamlined the detection process while acknowledging the scope for algorithmic refinement in subsequent studies. Our findings underline the feasibility of employing dynamic programming to enhance navigational danger detection, emphasizing its potential in ensuring maritime safety. This work lays a foundation for future research endeavors, aiming to fine-tune algorithms and advance navigational safety measures in ECDIS.

In recent years, deaths and missing people have continuously occurred due to man overboard (MOB) and suicide on passenger ships. However, due to the complex deck structure and enlargement of passenger ships, closed-circuit television (CCTV) coverage is limited, and it is difficult to prevent accidents for all passengers with limited crews. Therefore, a real-time system for detecting high-risk blind spots on passenger ships is needed through risk analysis. This study used a combination of the following three risk factors to calculate and evaluate the risk of MOB and suicide by deck area of the passenger ship: 1) distance away from guard rails, 2) the visibility of CCTV, and 3) ship operating conditions. Based on the survey from experts, risk scores of MOB and suicide accidents by deck area on a passenger ship were yielded.