To predict the delivered power of full-formed ships using a combined EFD/CFD method, this work investigated methods of obtaining the hull form factor (1+k) for three full-formed ships under various loading conditions, and analyzed their applicability. The hull form factors of each vessel were obtained using model test and numerical simulation, respectively. They were compared and then utilized to perform full-scale predictions of the delivered power using the combined method. The research indicated that the hull form factor obtained from numerical simulation resulted in a closer match between the predicted full-scale delivered power and the trial data, with an allowable difference. The research also validated the applicability of the delivered power prediction of full-formed ships using the combined method.

In a road transport port collection-distribution system involving container trucks and a drayage fleet, it is necessary to conduct joint scheduling of trucks, tractors, and semi-trailers to achieve coordinated optimization of transport structure and routing simultaneously. This paper proposes a joint scheduling optimization model for multi-fleet combined transport, formulated as a mixed integer programming model. A simulated annealing algorithm based on heuristic rules is designed to solve the proposed problem. The impacts of different transport demands on the total transport cost and the number of workable vehicles under different freight modes are explored. Results show that, compared with the single freight mode, the multi-fleet combined freight mode demonstrates significant advantages in effectively reducing transport costs by 13.3% for tasks uniformly distributed in the freight network. Additionally, by optimizing the allocation of its own vehicle resources, the multi-fleet combined transport mode can mitigate the influence of fixed cost weight coefficient changes and transport demand compactness on the total transport cost.

In response to the IMO Preliminary Strategy for Greenhouse Gas Emission Reduction from Ships and the domestic "3060 Double Carbon Goal", carbon reduction routes applicable to the domestic fleet are proposed. Using the fleet carbon reduction analysis model, the carbon reduction amount, carbon intensity and carbon reduction cost of a domestic shipping company's fleet based on the above fuel routes are analyzed by defining the fossil fuel, methanol fuel and ammonia fuel routes. The results of the study show that the fleet based on methanol and ammonia fuel paths can meet the requirements of the "Preliminary Strategy for Ship Temperature IMO Room Gas Emission Reduction" and the domestic "3060 Double Carbon Goal", and that the fleet can be transitioned from the traditional bunker fuel type to the methanol/ammonia ready type as soon as possible in the near future, and then to methanol/ammonia powered type in the medium and long term. In the near future, we can transition from traditional fuel oil ships to methanol/ammonia fuel ready (methanol/ammonia fuel power system preset) ships as soon as possible, and in the middle and long term, we can gradually transition to methanol/ammonia fuel-powered ships; green methanol and green ammonia have their own advantages, and the number of future medium and long term commercial applications mainly depends on the differences between green methanol and green ammonia in the aspects of availability and economy.

The use of diesel auxiliaries by ships during port calls causes a large amount of fossil energy consumption and pollutant emissions, and the use of shore power for energy supply is a good alternative. For the energy problem of shore power system, the article introduces a hybrid energy system composed of offshore wind turbines, shore power and hydrogen-based energy storage, and proposes a hydrogen-based energy storage planning model based on hybrid stochastic regularization-information gap decision theory. Aiming at the uncertainty of offshore wind turbine output, stochastic planning is used to get the time-sequence typical output scenario; for the difficulty of accurately portraying the probability distribution of shore power port call ship load and shore power price, IGDT is used to form a dual-objective model to deal with the uncertainty of the two as well as to introduce two different risky strategy planning models and analyze them by considering the seasonal factors. The results of the example show that the hybrid energy system can improve energy utilization and interaction, provide a planning basis for decision makers, and verify the effectiveness of the proposed model.

With the rapid development of computer technology, numerous new optimization methods and processes have emerged in the field of ship type optimization. However, there is still a lack of open-source and free optimization platforms in China that can efficiently integrate these optimization methods and processes. This article constructs an optimization platform based on the Grasshopper visual programming environment, integrating the fundamental steps of ship type optimization. By incorporating variable complexity methods into the optimization process, the platform addresses issues such as long optimization times and high computational costs, thereby enhancing its functionality and optimization capabilities. Using this platform, drag reduction optimization is performed on the KCS bulbous bow of container ships, and the newly designed ship form demonstrates superior drag performance compared to the original form. This verifies the correctness and feasibility of the platform and lays the foundation for further expansion of its functionalities.

To clarify the development stages and characteristics of China's major coastal ports since its accession to the WTO, a quantitative study is conducted based on the monthly container throughput data of seven major coastal ports from 2002 to 2023. Multivariate change point analysis is introduced to objectively reflect the stage characteristics of port development, and the Chow test is used to verify the reliability of the quantitative research results. The study shows that the major coastal ports in China can be divided into six development stages over the 22-year research period. By combining the characteristics of port container throughput growth in different stages and considering key events such as China's accession to the WTO at the end of 2001, the outbreak of the global financial crisis in 2008, and the spread of COVID-19 in 2020, the six stages are identified as: rapid growth period (2002~2005), fluctuating development period (2006~2010), recovery growth period (2011~2013), stable platform period (2014~2017), development differentiation period (2018~2020), and resilient growth period (2021~2023). Each development stage exhibits distinct characteristics, closely related to the economic and trade conditions of China and the world during the respective periods.

In order to solve the key problem of collision avoidance action of close ships, the steering avoidance process during collision encounter is studied, combining with the ship's maneuvering performance, the time characteristics of the ship's initial turning semicircle elements and turning angle are analyzed; through the decomposition of the relative motion diagram and the inverse approximation algorithm, the mathematical model of the close-quarter situation distance and collision distance is obtained when the ship is steering avoidance. The application of the model is also given; the results of the model application are verified by arithmetic examples and simulation tests. The results show that the proposed two distance models can be used to guide the collision avoidance actions of ships at close range, and provide a theoretical basis for the study of collision risk and the establishment of an automatic collision avoidance decision-making system for ships. At the same time, according to the model, the collision avoidance actions that should be taken by the ship at different stages of the encounter are analyzed for the target ships with different speed ratios and different bearings when there is a collision danger, and the close-range ship collision avoidance action mode is also given. It also gives the model of close-range ship collision avoidance action. It provides the ship deck officers with the support of close-range ship collision danger prediction and ship collision avoidance decision-making. The research results are of great significance to ship navigation safety and navigation intelligence.

With the continuous increase in seaborne trade volume, ship traffic density in port areas is rising, and navigation conditions in port waters are becoming more complex. Short-term ship traffic prediction in port waters is playing an increasingly critical role in ship traffic control and navigation safety management. To address the limitation of low accuracy in aggregate models, this paper, based on ship Automatic Identification System (AIS) data, employs a disaggregate method to construct a hybrid prediction model. This model combines the Long Short-Term Memory (LSTM) network with ships' historical trajectories to calculate short-term ship trajectories in port waters. The counts of ships' trajectories intersecting with an approach channel section are used to predict the short-term ship flow across the section. A numerical example from Ningbo-Zhoushan Port during June to December 2020 demonstrates that the forecasting accuracy of the proposed model reaches up to 80%, significantly higher than that of traditional aggregate models. The model developed here provides a technical foundation for ports to implement ship traffic control methods and improve channel utilization rates.

Existing studies indicate that the longer the queue length of vessels, the greater the channel saturation. To predict channel congestion, this study proposes a congestion prediction method that considers the maximum queue length based on the fundamental principles of traffic wave theory. The model utilizes Automatic Identification System (AIS) data to extract traffic flow characteristic parameters and, considering the differences in navigation behavior among ships in different waters, proposes a method for dividing the channel into characteristic areas. The queue length in traffic wave theory is selected as the evaluation index for congestion, and a method for predicting the maximum queue length based on Gaussian process regression is proposed to achieve the prediction of waterway congestion levels. A case study is conducted in the Yuxi River section of the Yangtze River Basin. The results show that the theoretical value of the maximum queue length in this section in July 2020 is 0.98 km, and the Adjusted R² index of the established regression model is 0.88, predicting a maximum queue length of 1.34 km with an error of 0.37 km compared to the theoretical value. The research results demonstrate that the proposed model has a high degree of interpretability and can effectively predict the maximum queue length, thereby enabling the prediction of channel congestion. This study provides a theoretical basis for improving the level of maritime supervision services.

Image feature registration is a critical step for stitching and generating large-field images during the inland navigation of ships. To address the problems of sparse water surface feature points and low efficiency in traditional feature matching algorithms for image registration in inland navigation environments, this paper proposes a feature matching method based on image super-resolution reconstruction. Firstly, the input images are subjected to super-resolution reconstruction using generative adversarial networks to enrich image details and increase the number of image feature points. Secondly, the ORB operator and BEBLID algorithm are employed for feature point detection and description. Then, coarse matching is performed based on Hamming distance. Finally, an improved random sampling consistency algorithm is utilized to further eliminate gross errors and purify inliers, achieving robust matching results. The study conducts experiments using five sets of inland navigation environment images with challenges such as low visibility, varying lighting conditions, scale changes, blur, and rotation. The results demonstrate that the proposed approach, leveraging image super-resolution reconstruction for feature point extraction, achieves an increased number of feature points and outperforms comparative algorithms in terms of matching accuracy and speed. This method meets the requirements of high-precision and real-time image matching in inland navigation environments.