Search Results (1,229)

With the global economy’s relentless growth and heightened environmental consciousness, sustainable maritime transport emerges as a pivotal development trajectory for the shipping sector. This study systematically analyzes 478 publications searched in the Web of Science Core Collection, from 2000 to 2023, utilizing bibliometric methods to investigate the application areas in sustainable development within the shipping industry. This study begins with an analysis of annual publication trends, which reveals a substantial expansion in research endeavors within this discipline over recent years. Subsequently, a comprehensive statistical evaluation of scholarly journals and a collaborative network assessment are conducted to pinpoint the foremost productive journals, nations, organizations, and individual researchers. Furthermore, a keyword co-occurrence methodology is applied to delineate the core research themes and emerging focal points within this domain, thereby outlining potential research directions for future research. In addition, drawing on the keyword co-occurrence analysis, the advancements in intelligent shipping technologies and green port construction applications within sustainable maritime transport are discussed. Finally, the review discusses the existing challenges and opportunities of sustainable maritime transport from a theoretical and practical perspective. The research shows that, in terms of intelligent shipping technology, data security and multi-source data are the focus that people need to pay attention to in the future; a trajectory prediction for different climates and different ship types is also an area for future research. In terms of green ports, Cold Ironing (CI) is one of the key points of the green port strategy, and how to drive stakeholders to build sustainable green ports efficiently and economically is the future developmental direction. This review serves to enhance researchers’ comprehension of the current landscape and progression trajectory of intelligent shipping technologies, thereby fostering the continued advancement and exploration in this vital domain. Full article

This study comprehensively explores the intricate hydrodynamic and geomorphological processes that affect the Sulina Channel and bar area. It employs advanced hydrodynamic, wave, and sediment transport models to simulate the influence of marine currents, waves, and shipping traffic on sediment transport and deposition patterns, providing valuable insights for maintaining navigable conditions in the Sulina Channel. It is shown that sediment deposition is highly dynamic, particularly in the Sulina bar area, where rapid sediment recolonization occurs within one to two months after dredging. The simulation indicates that vessels with drafts of 11.5 m cause notable erosion. In comparison, drafts of 7 m have a minimal impact on sediment transport, emphasizing the importance of managing vessel drafts to mitigate sediment disturbances. This research highlights and quantifies the siltation phenomenon from the Black Sea to the mouth of the Sulina Channel, effectively addressing the challenges posed by natural and anthropogenic factors to ensure the Channel’s sustainability and operational efficiency. Full article

Underwater wireless sensor networks play an important role in exploring the oceans as part of an integrated space–air–ground–ocean network. Because underwater energy is limited, the equipment’s efficiency is significantly impacted by the battery duration. Underwater backscatter technology does not require batteries and has significant potential in positioning, navigation, communication, and sensing due to its passive characteristics. However, underwater backscatter signals are susceptible to being swamped by the excitation signal. Additionally, the signals from different reflection signals share the same frequency and overlap, and contain fewer useful features, leading to significant challenges in detection. In order to solve the above problems, this paper proposes a recurrent neural network that introduces time-frequency and reference signal features for underwater backscatter signal separation (TF-REF-RNN). In the feature extraction part, we design an encoder that introduces time-frequency domain features to learn more about the frequency details. Additionally, to improve performance, we designed a separator that incorporates the reference signal’s pure global information features. The proposed TF-REF-RNN network model achieves metrics of 28.55 dB SI-SNRi and 19.51 dB SDRi in the dataset that includes shipsEar noise data and underwater simulated backscatter signals, outperforming similar classical methods. Full article

Shipping containers that remain in ports after exporting or importing products cause an environmental and logistical problem. Transporting them to the port of origin is costly; therefore, some of them are stored in the regions of destination. Recycling or reusing them in an efficient and sustainable way represents a clean alternative. The purpose of this article is to analyze the feasibility and impact of implementing different insulating configurations on the energy demands required by a house based on a construction with standardized shipping containers. More specifically, it assesses the impact of the different orientations in which the dwelling can be arranged, depending on the location and its meteorological data. To this aim, a construction model will be developed in which first, the geometrical parameters are defined, and second, the energy characteristics are identified. The results show that, in Southwest Europe, the western orientation generates a saving of 10% of the energy demand compared to the less favourable orientation, which is the southern one. Full article

To address the challenge of measuring the dynamic characteristic parameters of the gas turbine rotor system affected by hull excitation, a vibration transmission model integrating a ship model slice, test data, and a three-dimensional entity is proposed, based on the two-dimensional slice theory, scaled ship model, and finite element model of the turbine rotor system. The transient dynamic responses of the front and rear bearing points were calculated and analyzed. Vibration response tests with significant wave heights of 0.5 m, 1.25 m, 2.5 m, and 4 m were carried out in the towing tank of the ship model to obtain the dynamic characteristic parameters of the deck position. Techniques including wavelet denoising, Fast Fourier Transform (FFT), and signal resampling were employed to filter out and reconstruct high-frequency noise, overcoming the technical challenges of a high sampling frequency and a low computational efficiency. The experimental data and simulation results were compared and analyzed, validating the accuracy of the vibration transmission model of the turbine rotor system with data and entity integration. By comparing the vibration signal values in the X and Z directions at the front and rear bearing points after vibration transmission, it is evident that the effective values of the vibration signals at the front bearing point are 0.03% to 0.1% greater than those at the rear bearing point. This model provides a theoretical basis and reference for the design of the gas turbine rotor system. Full article

Cavitation erosion, as a mechanical effect of destruction, constitutes a complex and critical problem that affects the safety and efficiency of the functioning of engineering components specific to many fields of work, the most well-known being propellers of ships and maritime and river vessels, seawater desalination systems, offshore oil and gas drilling platforms (including drilling and processing equipment), and the rotors and blades of hydraulic machines. The main objective of the research conducted in this paper is to experimentally investigate the phenomenology of this surface degradation process of maritime ships and offshore installations operating in marine and river waters. To reduce cavitation erosion of maritime structures made from Duplex stainless steels, the study used the deposition by welding of layers of metallic alloys with a high capacity for work hardening. The cavitation tests were conducted in accordance with the American Society for Testing and Materials standards. The response of the deposited metal under each coating condition, compared to the base metal, was investigated by calculating the erosion penetration rate (MDER) through mass loss measurements over the cavitation duration and studying the degraded zones using scanning electron microscopy (SEM), the energy-dispersive X-ray analysis, and hardness measurements. It was revealed that welding hardfacing with austenitic manganese alloy contributes to an approximately 8.5–10.5-fold increase in cavitation erosion resistance. The explanation is given by the increase in surface hardness of the coated area, with 2–3 layers of deposited alloy reaching values of 465–490 HV5, significantly exceeding those specific to the base metal, which range from 260–280 HV5. The obtained results highlighted the feasibility of forming hard coatings on Duplex stainless-steel substrates. Full article

Ship detection, a crucial task, relies on the traditional CFAR (Constant False Alarm Rate) algorithm. However, this algorithm is not without its limitations. Noise and clutter in radar images introduce significant variability, hampering the detection of objects on the sea surface. The algorithm’s theoretically Constant False Alarm Rates are not upheld in practice, particularly when conditions change abruptly, such as with Beaufort wind strength. Moreover, the high computational cost of signal processing adversely affects the detection process’s efficiency. In previous work, a four-stage methodology was designed: The first preprocessing stage consisted of image enhancement by applying convolutions. Labeling and training were performed in the second stage using the Faster R-CNN architecture. In the third stage, model tuning was accomplished by adjusting the weight initialization and optimizer hyperparameters. Finally, object filtering was performed to retrieve only persistent objects. This work focuses on designing a specific methodology for ship detection in the Peruvian coast using commercial radar images. We introduce two key improvements: automatic cropping and a labeling interface. Using artificial intelligence techniques in automatic cropping leads to more precise edge extraction, improving the accuracy of object cropping. On the other hand, the developed labeling interface facilitates a comparative analysis of persistence in three consecutive rounds, significantly reducing the labeling times. These enhancements increase the labeling efficiency and enhance the learning of the detection model. A dataset consisting of 60 radar images is used for the experiments. Two classes of objects are considered, and cross-validation is applied in the training and validation models. The results yield a value of 0.0372 for the cost function, a recovery rate of 94.5%, and an accuracy rate of 95.1%, respectively. This work demonstrates that the proposed methodology can generate a high-performance model for contact detection in commercial radar images. Full article

With the flourishing development of deep learning, synthetic aperture radar (SAR) ship detection based on this method has been widely applied across various domains. However, most deep-learning-based detection methods currently only use the amplitude information from SAR images. In fact, phase information and time-frequency features can also play a role in ship detection. Additionally, the background noise and the small size of ships also pose challenges to detection. Finally, satellite-based detection requires the model to be lightweight and capable of real-time processing. To address these difficulties, we propose a multi-domain joint SAR ship detection method that integrates complex information with deep learning. Based on the imaging mechanism of line-by-line scanning, we can first confirm the presence of ships within echo returns in the eigen-subspace domain, which can reduce detection time. Benefiting from the complex information of single-look complex (SLC) SAR images, we transform the echo returns containing ships into the time-frequency domain. In the time-frequency domain, ships exhibit distinctive features that are different from noise, without the limitation of size, which is highly advantageous for detection. Therefore, we constructed a time-frequency SAR image dataset (TFSID) using the images in the time-frequency domain, and utilizing the advantages of this dataset, we combined space-to-depth convolution (SPDConv) and Inception depthwise convolution (InceptionDWConv) to propose Efficient SPD-InceptionDWConv (ESIDConv). Using this module as the core, we proposed a lightweight SAR ship detector (LSDet) based on YOLOv5n. The detector achieves a detection accuracy of 99.5 with only 0.3 M parameters and 1.2 G operations on the dataset. Extensive experiments on different datasets demonstrated the superiority and effectiveness of our proposed method. Full article

Utilizing time-series data from ship trajectories to forecast their subsequent movement is crucial for enhancing the safety within maritime traffic environments. The application of deep learning techniques, leveraging Automatic Identification System (AIS) data, has emerged as a pivotal area in maritime traffic studies. Within this domain, the precise forecasting of ship trajectories stands as a central challenge. In this study, we propose the multi-gated attention encoder decoder (MGAED) network, a model based on an encoder–decoder structure specialized for predicting ship trajectories in canals. The model employs a long short-term memory network (LSTM) as an encoder, combined with multiple Gated Recurrent Units (GRUs) and an attention mechanism for the decoder. Long-term dependencies in time-series data are captured through GRUs, while the attention mechanism is used to strengthen the model’s ability to capture key information, and a soft threshold residual structure is introduced to handle sparse features, thus enhancing the model’s generalization ability and robustness. The efficacy of our model is substantiated by an extensive evaluation against current deep learning benchmarks. Through comprehensive comparison experiments with existing deep learning methods, our model shows significant improvements in prediction accuracy, with an at least 9.63% reduction in the mean error (MAE) and an at least 20.0% reduction in the mean square error (MSE), providing a new solution to improve the accuracy and efficiency of ship trajectory prediction. Full article

Efficiency estimation of a propeller behind a vessel’s hull while sailing through ice floes, together with the ship’s resistance to motion, is a key factor in designing the power plant and determining the safety measures of a ship. This paper encloses the results from the experiments conducted at the CEHINAV towing tank, which consisted of analyzing the influence of the concentration at the free surface of artificial blocks, simulating ice, in propeller–block interactions. Thrust and torque were measured for a towed self-propelled ship model through simulated broken ice blocks made of paraffin wax. Three block concentrations of different block sizes and three model speeds were studied during the experimentation. Open-water self-propulsion tests and artificial broken ice towed self-propulsion tests are shown and compared in this work. The most relevant observations are outlined at the end of this paper, as well as some guidelines for conducting artificial ice-towed self-propulsion tests in traditional towing tanks. Full article

Energy policies around the world are increasingly highlighting the importance of hydrogen in the evolving energy landscape. In this regard, the use of hydrogen to produce biomethanol not only plays an essential role in the chemical industry but also holds great promise as an alternative fuel for global shipping. This study evaluates a system for generating biomethanol and biomethane based on anaerobic digestion, biogas upgrading, methanol synthesis unit, and high-temperature electrolysis. Thermal integration is implemented to enhance efficiency by linking the oxy-fuel gas turbine unit. The integrated system performance is evaluated through thermodynamic modeling, and Aspen Plus V12.1 is employed for the analysis. Our findings show that the primary power consumers are the Solid Oxide Electrolysis Cell (SOEC) and Methanol Synthesis Unit (MSU), with the SOEC system consuming 824 kW of power and the MSU consuming 129.5 kW of power, corresponding to a production scale of 23.2 kg/h of hydrogen and 269.54 kg/h of biomethanol, respectively. The overall energy efficiency is calculated at 58.09%, considering a production output of 188 kg/h of biomethane and 269 kg/h of biomethanol. The amount of carbon dioxide emitted per biofuel production is equal to 0.017, and the proposed system can be considered a low-carbon emission system. Key findings include significant enhancements in biomethanol capacity and energy efficiency with higher temperatures in the methanol reactor. Full article

The continuous growth of global trade and maritime transport has significantly heightened the challenges of managing ship traffic in port waters, particularly within fairways. Effective traffic management in these channels is crucial not only for ensuring navigational safety but also for optimizing port efficiency. A deep understanding of ship behavior within fairways is essential for effective traffic management. This paper applies machine learning techniques, including Decision Tree, Random Forest, and Gradient Boosting Regression, to model and analyze the behavior of various types of ships at specific moments within fairways. The study focuses on predicting four key behavioral parameters: latitude, longitude, speed, and heading. The experimental results reveal that the Random Forest model achieves adjusted $R^2$ scores of 0.9999 for both longitude and latitude, 0.9957 for speed, and 0.9727 for heading. All three models perform well in accurately predicting ship positions at different times, with the Random Forest model particularly excelling in speed and heading predictions. It effectively captures the behavior of ships within fairways and provides accurate predictions for different types and sizes of vessels, especially in terms of speed and heading variations as they approach or leave berths. This model offers valuable support for predicting ship behavior, enhancing ship traffic management, optimizing port scheduling, and detecting anomalies. Full article

Stern tube lubricants are essential in maritime operations, safeguarding ship propeller shafts from wear and corrosion while ensuring efficient propulsion. Their role in reducing friction and maintaining system integrity is critical. With growing environmental concerns, the adoption of environmentally acceptable lubricants (EALs) for stern tubes has gained importance, balancing operational performance with environmental protection. This study investigates the rheological and tribological properties of EALs formulated for ship propeller stern tube applications. The primary focus is on comparing these EALs with conventional mineral oils to assess their suitability in marine environments. EALs are increasingly favored due to their biodegradability and reduced environmental impact. Key parameters such as shear stability, friction factor, and temperature dependency were evaluated using a range of experimental methods including rotational viscometry and tribological analysis. The results indicate that the newly formulated EALs based on synthetic esters exhibit the highest viscosity index, a higher range of shear stability, and lower friction factors, compared to commercially available mineral oils, especially under varying operational conditions. These findings contribute to the ongoing efforts to promote eco-friendly lubricants in maritime industries, aligning with global environmental protection initiatives. Full article

The study examined the potential changing roles of ports in terms of diversifying their revenue through the expansion of new markets in the Port of Ngqura. This is by means of the production and sales of renewable hydrogen as marine fuel produced from a wavefarm in Nelson Mandela Bay. A key objective of the study was to conduct a comprehensive techno-economic analysis of the feasible hydrogen production technologies based on the analysis performed, including alkaline electrolysis of seawater and renewable-powered electrolysis of seawater. The produced hydrogen aligns with global decarbonisation of ships and ports and will be used to supply the port with electricity, serve to refuel tugboats, and provide green hydrogen bunkering fuel for commercial shipping vessels. The Port of Ngqura is geographically well positioned to lead the production of zero carbon shipping fuel. This work considers the CAPEX and OPEX of a hydrogen plant using electrolysers and evaluates the current cost of production and selling price of hydrogen. The primary aim of this study was to examine the feasibility of hydrogen production through electrolysis of seawater at the Port of Ngqura. Through assessing resource and technological options, determining advantageous economic assumptions, and identifying existing limitations and potential opportunities, a feasibility study was conducted with special consideration of the site characteristics of Ngqura. The output of this study is a model that simulates the production, storage, and transportation of hydrogen gas from the Port of Ngqura, which was further used to analyse different case study scenarios. This approach directly addresses the main goal of the study. The results found showed that with wave energy convertors in a row of three next to each other, the energy produced by the wave farm was 2.973 TJ per month, which is equivalent to 18.58 tons of produced hydrogen when considering the lower heating value of hydrogen and assuming that hydrogen production efficiency is 75%. The anticipated hydrogen fuel will be able to refuel a tugboat with green hydrogen from the energy produced by the wave farm each month. It is predicted that the price of hydrogen is expected to drop, and the price of fossil fuel will gradually increase in the coming years. The fact that coal electricity can be produced on demand and wind and solar energy are weather dependent as a result lacks the ability to achieve a constant supply. There is currently an urgent need for energy storage and the efforts to study the production of hydrogen and ammonia. Hydrogen is still predicted to be more expensive than coal electricity; however, from this, maybe a critical cost for a kg of CO₂ could be calculated, which could make hydrogen competitive. The cost of green hydrogen production from wave energy in the Port of Ngqura was calculated as R96.07/kg (4.88 EUR/kg) of produced hydrogen, which is equivalent to 2.1 times the cost of the same energy supplied as Marine Diesel Oil (MDO) at current prices. Hydrogen from wave energy would thus become competitive with MDO; if a price is set for the emission of CO₂, this may also offset the difference in cost between MDO and hydrogen from wave energy. The carbon price necessary to make green hydrogen competitive would be approximately R6257/tonne CO₂, or 318 EUR/tonne CO₂, which is around 4.5 times the current trading price of carbon in the EU Emissions Trading Scheme. Full article

With the rapid development of the modern world, it is imperative to achieve effective and efficient monitoring for territories of interest, especially for the broad ocean area. For surveillance of ship targets at sea, a common and powerful approach is to take advantage of satellite synthetic aperture radar (SAR) systems. Currently, using satellite SAR images for ship classification is a challenging issue due to complex sea situations and the imaging variances of ships. Fortunately, the emergence of advanced satellite SAR sensors has shed much light on the SAR ship automatic target recognition (ATR) task, e.g., utilizing dual-polarization (dual-pol) information to boost the performance of SAR ship classification. Therefore, in this paper we have developed a novel cross-polarimetric interaction network (CPINet) to explore the abundant polarization information of dual-pol SAR images with the help of deep learning strategies, leading to an effective solution for high-performance ship classification. First, we establish a novel multiscale deep feature extraction framework to fully mine the characteristics of dual-pol SAR images in a coarse-to-fine manner. Second, to further leverage the complementary information of dual-pol SAR images, we propose a mixed-order squeeze–excitation (MO-SE) attention mechanism, in which the first- and second-order statistics of the deep features from one single-polarized SAR image are extracted to guide the learning of another polarized one. Then, the intermediate multiscale fused and MO-SE augmented dual-polarized deep feature maps are respectively aggregated by the factorized bilinear coding (FBC) pooling method. Meanwhile, the last multiscale fused deep feature maps for each single-polarized SAR image are also individually aggregated by the FBC. Finally, four kinds of highly discriminative deep representations are obtained for loss computation and category prediction. For better network training, the gradient normalization (GradNorm) method for multitask networks is extended to adaptively balance the contribution of each loss component. Extensive experiments on the three- and five-category dual-pol SAR ship classification dataset collected from the open and free OpenSARShip database demonstrate the superiority and robustness of CPINet compared with state-of-the-art methods for the dual-polarized SAR ship classification task. Full article