Containerized Research Articles

Promoting or Inhibiting? Influence of Railway Container Transportation on Regional Economic Development

Promoting or Inhibiting? Influence of Railway Container Transportation on Regional Economic Development

Classification of Ship Type from Combination of HMM–DNN–CNN Models Based on Ship Trajectory Features

This study proposes an enhanced ship-type classification model that employs a sequential processing methodology integrating hidden Markov model (HMM), deep neural network (DNN), and convolutional neural network (CNN) techniques. Four different ship types—fishing boat, passenger, container, and other ship—were classified using multiple ship trajectory features extracted from the automatic identification system (AIS) and small fishing vessel tracking system. For model optimization, both ship datasets were transformed into various formats corresponding to multiple models, incorporating data enhancement and augmentation approaches. Speed over ground, course over ground, rate of turn, rate of turn in speed, berth distance, latitude/longitude, and heading were used as input parameters. The HMM–DNN–CNN combination was obtained as the optimal model (average F-1 score: 97.54%), achieving individual classification performances of 99.03%, 97.46%, and 95.83% for fishing boats, passenger ships, and container ships, respectively. The proposed approach outperformed previous approaches in prediction accuracy, with further improvements anticipated when implemented on a large-scale real-time data collection system.

Research on Course-Changing Performance of a Large Ship with Spoiler Fins

The poor maneuverability inherent to large ships is a non-negligible problem that restricts the development of the shipping industry, as large ships can only cruise at an excessively conservative speed when they encounter complicated traffic conditions; nevertheless, ship collision accidents still occasionally occur. In the present study, the novel concept of spoiler fins for modern large ships is proposed. In order to assess their effectiveness in enhancing ship maneuverability, a KRISO container ship (KCS) was selected to carry a pair of spoiler fins, after which a simplified simulation approach for saving the calculation resource was designed for ship collision avoidance conditions, and a full-scale numerical model, including the ship hull, fin, and fluid field domain, was established. Transient-state hydrodynamic forces were calculated during collision avoidance maneuvers using the CFD method; the pressure and velocity contours around the ship were demonstrated; and the ship motion trajectories under different initial ship speeds were simulated and predicted through the adoption of overset mesh and 6-DOF dynamic mesh techniques. Eventually, the improved course-changing performance, dependent on the spoiler fins, was validated.

Investigation of container strength when fixed in an open wagon equipped with pneumatic bags

Ensuring the efficiency of railway transport in international traffic needs the development of combined transport systems. Container transportation is the most relevant among them. Containers are usually transported by rail on platform wagons. Along with this, the lack of platform wagons in operation makes it necessary to use other types of wagons for container transportation, such as open wagons. The fastening of containers in open wagons is carried out using pneumatic bags. To study the effectiveness of applying such a fastening scheme, the load of the container during transportation in an open wagon was determined. The conducted research will contribute to creating recommendations for improving rail transport operations' efficiency.

A unified cross-series marine propeller design method based on machine learning

Propeller design diagrams, like Bp-δ diagram, are widely applied in ship propeller design. However, different propeller series use various selection approaches, so comparisons between designs are only possible after individual candidates are chosen.This paper proposes a unified approach based on machine learning to allow efficient comparison and facilitate the selection of the optimal propeller amongst the available propeller series. The process starts by compiling propeller series data to generate a comprehensive dataset on propeller performance. This dataset is then used to train an Artificial Neural Network (ANN) model, which accurately predicts open-water propeller performance. Optimization techniques are applied to maximize propeller efficiency based on the specific needs of the vessel, while ensuring compliance with cavitation and noise constraints for safety. The model's accuracy is validated using data from the KRISO Container Ship (KCS), demonstrating the prediction's reliability. The method is then applied to select both open and ducted propellers for a variety of ship types to meet specific operational requirements. Ultimately, the optimized results are ranked by efficiency, offering an organized set of options for selecting the most suitable propeller. This approach eliminates the need for manual dataset correlation, significantly improving the efficacy of generating an outperforming initial design.

Directional wave spectrum estimation through onboard measurement data utilizing B-spline basis functions

The utilization of digital twin technology, which integrates real-world measurement data with a digital representation of the vessel, is garnering increasing attention within the realm of structural integrity management. Due to limitations in sensor deployment on ships, the integration of the digital twin framework, which merges measurement data with the ship's digital design model, becomes crucial for ensuring the effectiveness of structural integrity management. This investigation introduces a methodological approach for estimating localized responses at unmeasured locations, leveraging both measurement and design datasets. To approximate the response at unobserved sites, the directional wave spectrum is initially determined utilizing the least squares method, thereby, minimizing the disparity between the estimated and measured response spectra. The methodology notably incorporates cubic B-spline interpolation specifically to smooth the directional wave spectrum deployed on the heading-frequency domain. This deliberate choice of employing cubic B-spline interpolation underscores the emphasis on achieving a refined and continuous representation of the directional wave spectrum, thus enhancing the accuracy and reliability of the estimation process. To validate the proposed methodology, synthetic pseudo-measurement data derived from the wave spectrum and RAO of a 13,000 TEU container ship are employed. Subsequently, the directional wave spectrum is estimated utilizing the pseudo-measurement data, and the estimated directional wave spectrum, in conjunction with the Response Amplitude Operator (RAO), is harnessed to approximate the response spectrum at the location where sensors are not installed.

Train assignment and handling capacity arrangement in multi-yard railway container terminals: An enhanced adaptive large neighborhood search heuristic approach

Expanding terminal scale and constructing multiple railway handling yards have become popular strategies for leading railway container terminals globally to cope with the ever-increasing handling volume. Although such an approach greatly enhances the terminal’s productivity, it also intensifies handling operations and introduces additional inter-yard interactions, complicating terminal management. To address these challenges, this paper investigates an integrated optimization approach for multi-yard railway container terminals, which feature both rail-road and rail-rail container transshipment operations. The train assignment plan for each yard and the handling capacity arrangement for each train are jointly optimized while considering inter-yard container transits, workload allocation for yards, and safety requirements in train shunting. This problem is formulated as a nonlinear programming model, with the objective to minimize operational delay and service time for each incoming train, and to minimize workload differences and container transit volume among yards. To efficiently solve this problem, this research develops an enhanced adaptive large neighborhood search (EALNS) heuristic, which includes several customized operators and feasibility repair methods, and is further enhanced with a local search method and backtracking mechanism compared to the standard ALNS framework. Computational experiments with different data scales and problem settings demonstrate the superiority of the EALNS in terms of solution quality and stability compared with three other solution methods. Additionally, practical insights for terminal operations are drawn through detailed analysis of different infrastructure configurations, transshipment train characteristics, and unit cost settings.

Enhancing energy efficiency in shipping container house: a novel approach using hybrid louver systems

Reusing shipping containers for residential purposes offers a promising approach to address global energy consumption challenges from economic and environmental perspectives. This study parametrically designed hybrid louver shadings by combining fixed vertical triangular slats with variable-depth horizontal rectangular slats, offering a novel approach to shading prefabricated buildings. The energy consumption, daylighting performance, and visual comfort were assessed for various shading configurations. Results indicated that implementing the proposed hybrid louvers, featuring fixed vertical triangular slats and variable-depth horizontal rectangular slats, significantly reduced Energy Use Intensity (EUI) to 133.95 kWh/m2. Moreover, Useful daylight illuminance (UDI), Daylight Autonomy (DA), and Glare Autonomy (GA) values improved to 95.38%, 89.97%, and 91.16%, respectively. The study highlighted the potential of the proposed shading system to significantly reduce overall energy consumption across various ASHRAE climate zones, including Miami (1A), Guangzhou (2A), Melbourne (3C), Esperance (3C), San Diego (3A), and Milan (4A). Notably, energy reductions of up to 50.2% were projected for climates such as Miami (1A) and San Diego (3A). Furthermore, container buildings in warm climate zones exhibited a significantly lower EUI range of 76.58 to 91.95 kWh/m². This study underscores the transformative potential of hybrid louver systems in promoting the widespread adoption of sustainable residential architecture, contributing to global sustainability efforts.

The impact of roughness on the resistance of full-scale ships

Abstract This study aims to explore the impact of hull roughness on ship resistance, which provides a reference for enhancing the operational efficiency and energy-saving levels of ships. Initially, the development and accuracy of Computational Fluid Dynamics (CFD) numerical simulations are elucidated through literature, and the current state of research on hull roughness by scholars both domestically and internationally is introduced. Subsequently, taking the 2000 TEU container ship SITCCAGAYAN as the object of study, a CFD simulation is performed based on the unsteady Reynolds-averaged Navier-Stokes equations in Star CCM+ software. An improved wall function method and roughness function are used to design the simulation experiments, conducting the resistance prediction under various conditions, including the fully smooth hull, fully rough hull, and different levels of roughness at various parts of the hull. The experiment, based on results such as the increase in resistance, roughness impact factor, and roughness Reynolds number, demonstrates that the surface roughness of the hull significantly affects the magnitude of ship resistance. An increase in local and overall roughness leads to an increase in total resistance, but the roughness at the bow part of the ship has the greatest impact on resistance.

Investigating temperature fluctuations in the wine and liquor maritime supply chain from South Africa to the United Kingdom: A case study

This study explored temperature fluctuations in dry shipping containers transporting wine and liquor along maritime supply chains. It also examined how these variations affect product quality, and evaluated the effectiveness of thermal foil container liners in mitigating temperature changes. Temperature trials were conducted from the South African loading depot and concluded at the first distribution centre in the United Kingdom. Ambient temperature sensors were placed on cartons of wine and liquor inside the thermal container liner and between the thermal liner and the door of the container. Weather data was also collected during the trials. Temperature profiles showed large fluctuations in temperature inside containers because of day and night cycles while the containers were stacked at the departure and destination ports, posing challenges for supply chain management. Descriptive statistics were used to analyse the temperature profiles, whereas logit analysis was used to determine the impact of the thermal container liner on the temperatures to which the wine and liquor were exposed. Blind tastings were used to evaluate product quality after exposure to temperature fluctuations. Results showed that thermal liners significantly reduced temperature variability, which is critical for maintaining wine and liquor quality. Despite this, blind tastings confirmed that some products were negatively affected by temperature deviations. The study recommends the use of thermal foil liners for maritime transport of wine and liquor to minimise product damage and financial losses. This finding is particularly relevant for exporters aiming to preserve the quality of their wine and liquor throughout long-distance shipping routes.

Comparison Study on the Fatigue Damage of a Container Ship Applying Hydroelastic Fatigue Analysis Procedures of LR and BV Classification Societies

<i>Container ships, which have hatch openings, are subject to low natural frequencies and exhibit elastic behavior due to wave loads, a phenomenon referred to as the hydroelastic effect. Classification societies have established hydroelastic fatigue analysis procedures to address the increased fatigue damage caused by this effect. This study compares the fatigue damage increase ratios at the hatch coaming top corners according to the procedures provided by Lloyd's Register (LR) and Bureau Veritas (BV). The weight distribution was adjusted using mass and interpolation elements, and normal mode analysis was conducted to obtain the natural frequencies and mode shapes of the ship, which were then used in frequency-domain hydroelastic motion analysis. The fatigue analysis was performed based on LR and BV procedures, using mode response amplitude operators (RAOs) and hydrodynamic coefficients derived from the hydroelastic motion analysis. Despite the differing methodologies between LR and BV, similar stress RAOs were obtained, with the midship showing a higher fatigue damage increase ratio than the forward and aft ends. For the LR procedure, more modes are needed for greater accuracy at the aft end, and for the BV procedure, further investigation is required to address the unreasonable response of the dynamic stress RAO in the low-frequency region, which is distant from the resonance frequency.</i>

Identifying possible ways for adapting an open wagon for transporting containers

The object of this study is the processes of perception and redistribution of loads in the supporting structure of a open wagon loaded with containers, taking into account the new scheme of their fastening. For safe transportation of containers in an open wagon, it is suggested to use a removable module. This module works according to the principle of an intermediate adapter between the container and the open wagon body. Fastening of the module itself in the open wagon is carried out through the fitting stops, which are placed on the floor of the open wagon. Mathematical modeling was carried out to determine the longitudinal dynamic load acting on the container fixed according to the new scheme in the open wagon. To this end, a mathematical model was built that characterizes the longitudinal movements of the "open wagon - removable module - container" system. Determination of the accelerations that act on the supporting structure of the open wagon loaded with containers was also carried out by means of computer simulation. Verification of the formed model of the dynamic load of the open wagon was carried out according to the F-criterion. Also, as part of the study, a modal analysis of the load-bearing structure of a open wagon loaded with containers was carried out, which made it possible to assess its traffic safety. A feature of the results obtained as part of the research is that the proposed design of the removable module could be used not only for fastening containers but also for transportation of other types of cargo. The field of practical application of the results is railroad transport, including other transportation industries. The conditions for the practical use of the results are a symmetrical scheme of loading the body of a open wagon with containers. The results of this study will contribute to increasing the efficiency of container transportation and the profitability of railroad transport

A Proposed Design Technique for Recycling of Very Large Container Ship

A Proposed Design Technique for Recycling of Very Large Container Ship

Containerized Freight Index Prediction Integrating Deep Learning and Time Series Analysis

In the context of globalization, the shipping industry, as a core pillar of international trade, is crucial for global economic stability. This study focuses on the China Containerized Freight Index (CCFI) and proposes a composite deep learning model combining CEEMDAN, CNN, LSTM, and SENet for CCFI analysis and prediction. Initially, the CEEMDAN algorithm is used to decompose the CCFI time series, extracting useful signals from noise. Then, CNN extracts features, LSTM model sequences, and SENet enhances feature representation. The CEEMDAN-CNN-LSTM-SENet model achieves superior predictive performance with MAE of 29.5171, RMSE of 33.7638, and MAPE of 1.56%. Compared to other models like ECL (MAE 77.0951, RMSE 57.3559, MAPE 3.25%), our model shows approximately 61.7% improvement in MAE. This research offers a robust tool for shipping market analysis and policy-making, providing new perspectives for handling complex economic data.

Ship Type Selection and Cost Optimization of Marine Container Ships Based on Genetic Algorithm

In the context of the deep-sea transportation supply chain, this paper addresses the complex decision-making problem of vessel allocation and carbon emission optimization for container shipping routes. A bi-level programming model is established, with the upper level aiming to minimize the total operational cost and the lower level focusing on minimizing carbon emissions. Using an example of an operator with five different types of vessels, a genetic algorithm is employed to determine the optimal vessel allocation scheme. The results indicate that the vessel allocation scheme obtained through multiple iterations of the model effectively reduces both carbon emissions and operational costs. Under the condition that the preset labor cost increases year by year, the use of model optimization can significantly reduce the growth of total operating costs. This paper provides theoretical support and practical guidance for shipping companies aiming to optimize decision-making in order to reduce operational costs and carbon emissions.

Utilizing Artificial Neural Network Ensembles for Ship Design Optimization to Reduce Added Wave Resistance and CO2 Emissions

Increased maritime cargo transportation has necessitated stricter management of emissions from ships. The primary source of this pollution is fuel combustion, which is influenced by factors such as a ship’s added wave resistance. Accurate estimation of this resistance during ship design is crucial for minimizing exhaust emissions. The challenge is that, at the preliminary parametric design stage, only limited geometric data about the ship is available, and the existing methods for estimating added wave resistance cannot be applied. This article presents the application of artificial neural network (ANN) ensembles for estimating added wave resistance based on dimensionless design parameters available at the preliminary design stage, such as the length-to-breadth ratio (L/B), breadth-to-draught ratio (B/T), length-to-draught ratio (L/T), block coefficient (CB), and the Froude number (Fn). Four different ANN ensembles are developed to predict this resistance using both complete sets of design characteristics (i.e., L/B, B/T, CB, and Fn) and incomplete sets, such as L/B, CB, and Fn; B/T, CB, and Fn; and L/T, CB, and Fn. This approach allows for the consideration of CO2 emissions at the parametric design stage when only limited ship dimensions are known. An example in this article demonstrates that minor modifications to typical container ship designs can significantly reduce added wave resistance, resulting in a daily reduction of up to 2.55 tons of CO2 emissions. This reduction is equivalent to the emissions produced by 778 cars per day, highlighting the environmental benefits of optimizing ship design.

GLSD: a global large-scale ship database with baseline evaluations

ABSTRACT In this paper, we introduce a challenging Global Large-scale Ship Database (GLSD), designed specifically for ship detection tasks. The designed GLSD database includes a total of 212,357 annotated instances from 152,576 images. Based on the collected images, we propose 13 ship categories that widely exist in international routes. These categories include Sailing boat, Fishing boat, Passenger ship, Warship, General cargo ship, Container ship, Bulk cargo carrier, Barge, Ore carrier, Speed boat, Canoe, Oil carrier, and Tug. The motivations for developing GLSD include the following: 1) providing a refined and extensive ship detection database that benefits the object detection community, 2) establishing a database with exhaustive labels (bounding boxes and ship class categories) in a uniform classification scheme, and 3) providing a large-scale ship database with geographic information (covering more than 3000 ports and 33 routes) that benefits multi-modal analysis. Additionally, we discuss the evaluation protocols corresponding to image characteristics in GLSD and analyze the performance of selected state-of-the-art object detection algorithms on GLSD, aiming to establish baselines for future studies.

Enhancement of Operational Safety in Marine Cargo Cranes on a Container Ship Through the Application of Authenticated Wi-Fi Based Wireless Data Transmission from Multiple Sensors.

The use of wireless technology in common marine engineering applications as a medium for data transaction in measurement and control systems, is not as popular as it should be. This article aims to demonstrate the advantages of using wireless technology in maritime engineering applications through a proposed Wi-Fi based wireless system dedicated to performance and safety monitoring in marine cargo cranes. The system is based on some concepts that were suggested in the earlier literature to perform authenticated data transmission from multiple sensors through using both the ESP-NOW protocol and the WebSerial remote serial monitor. The introduced system will be integrated with an already installed system in order to render the means for implementing effective principles in automation and control engineering, such as functional safety and predictive maintenance. Additionally, this article will highlight the economic efficiency of adopting wireless technology instead of cabling as a medium for data transaction in measurement and control systems in marine engineering applications such as cargo cranes.

Dynamic Flexible Allocation of Slots in Container Line Transport

Due to the imbalance between supply and demand, liner container transportation often faces the problem of low slot utilization, which will occur in the shipping process, such as dry container demand exceeding the available dry slots and reefer slots not being fully utilized. This makes it important and challenging to maintain a balance between the actual demand and the limited number of slots allocated for liner container transport. Therefore, this study proposes a flexible allocation method: expanding the types of containers that can be loaded in the same slot. This method is suitable for handling each dynamic arrival container booking request by shipping enterprises, making decisions to accept or reject, and flexibly allocating shipping slots. In order to maximize the total revenue generated by accepting container booking requests during the entire booking acceptance cycle, we establish a dynamic programming model for the flexible allocation of slots. For model solving, we use the Q-learning reinforcement learning algorithm. Compared with traditional heuristic algorithms, this algorithm can improve solving efficiency and facilitate decision-making at the operational level of shipping enterprises. In terms of model performance, examples of different scales are used for comparison and training; the results are compared with the model without flexible allocation, and it is proved that the model proposed in this paper can obtain higher returns than the model without flexible allocation. The results show that the model and Q-learning algorithm can help enterprises solve the problem of the flexible allocation of shipping slots, and thus, this research has practical significance.

Numerical analysis of coupling effect between sloshing and motion responses of a KCS in uni- and bi-directional waves

The seakeeping behaviour can be severely affected by the coupling between sloshing and ship motions in waves. The sloshing behaviour of liquid-loaded ships in bi-directional waves differs significantly from that in uni-directional waves. Given that ships operate in short-crested or multi-directional waves during their whole service period, it is significant to gain a deeper understanding of the effect of wave directionality on the coupling between sloshing and ship motions. In this study, a KRISO container ship (KCS) model with type-B liquefied natural gas (LNG) cargo tanks is adopted for coupling analysis. The 2D long-crested regular wave is adopted as an uni-directional wave, and three dimensional (3-D) cross wave is adopted as a bi-directional wave. The computational fluid dynamics (CFD) method is applied for the sloshing and seakeeping simulation of the ship model in different wave fields. The differences in wave elevation, sloshing forces, and coupling effects between ship motions and sloshing are studied in these two wave fields by adjusting control parameters such as ship heading angle and filling ratio. The comparative results indicate that sloshing behaviour and coupled motion responses in cross waves should be paid enough attention during ship design and evaluation.