Search Results (100)

S355NL structural steel is extensively employed in bridges, ships, and power station equipment owing to its excellent tensile strength, weldability, and low-temperature toughness. However, pronounced fluctuations in its Charpy impact energy at low temperatures significantly compromise the reliability and service life of critical components. In this study, vacuum-induction-melted ingots of S355NL steel containing 0–0.086 wt.% rare earth cerium were prepared. The effects of Ce on microstructures, inclusions, and impact toughness were systematically investigated using optical microscopy (OM), scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and Charpy V-notch testing. The results indicate that appropriate Ce additions (0.0011–0.0049 wt.%) refine the average grain size from 5.27 μm to 4.88 μm, reduce the pearlite interlamellar spacing from 204 nm to 169 nm, and promote the transformation of large-size Al₂O₃-MnS composite inclusions into fine, spherical, Ce-rich oxysulfides. Charpy V-notch tests at –50 °C reveal that 0.0011 wt.% Ce enhances both longitudinal (269.7 J) and transverse (257.4 J) absorbed energies while minimizing anisotropy (E_t/E_l  =  1.01). Conversely, excessive Ce addition (0.086 wt.%) leads to coarse inclusions and deteriorates impact performance. These findings establish an optimal Ce window (0.0011–0.0049 wt.%) for microstructural and inclusion engineering to enhance the low-temperature impact toughness of S355NL steel. Full article

Harmonics are a common phenomenon widely present in power systems. The presence of harmonics not only increases the energy consumption of equipment but also poses hidden risks to the safety and stealth performance of large ships. Thus, there is an urgent need for a detection method for the harmonic characteristics of time series. We propose a novel harmonic feature estimation method, termed Harmonic Aggregation Entropy (HaAgEn), which effectively discriminates against background noise. The method is based on bispectrum analysis; utilizing the distribution characteristics of harmonic signals in the bispectrum matrix, a new Diagonal Bi-directional Integral Bispectrum (DBIB) method is employed to effectively extract harmonic features within the bispectrum matrix. This approach addresses the issues associated with traditional time–frequency analysis methods, such as the large computational burden and lack of specificity in feature extraction. The integration results, $I_x$ and $I_y$, of DBIB on different frequency axes are calculated using cross-entropy to derive HaAgEn. It is verified that HaAgEn is significantly more sensitive to harmonic components in the signal compared to other types of entropy, thereby better addressing harmonic detection issues and reducing feature redundancy. The detection accuracy of harmonic components in the shaft-rate electromagnetic field signal, as evidenced by sea trial data, reaches 96.8%, which is significantly higher than that of other detection methods. This provides a novel technical approach for addressing the issue of harmonic detection in industrial applications. Full article

This paper develops a model to calculate carbon emissions during the construction period of dredging projects. Carbon emission quotas for various types of dredgers and auxiliary vessels in different construction conditions and geotechnical soil types during the dredging project’s construction period are established, as well as the power consumption quota for management activities. Taking the construction of the main project of the cross-river channel from Shenzhen to Zhongshan (S09)’s foundation trench excavation and channel dredging, the Thilafushi Island reclamation project in Malé, and the second phase of the southern section of the Guangzhou Port Area channel maintenance project (2022–2023) as case studies, the validity of the quotas is verified. During the construction period, under the same dredging soil quality and the same working condition level, the carbon emissions of different types of dredgers are different. Conversely, under different dredging soil qualities and different working condition levels, the carbon emissions for the same dredger or auxiliary vessel are different. The carbon emissions of each dredger or auxiliary vessel increase with the increase in the ship’s specifications. The carbon emissions of dredging projects are huge, with direct carbon emissions accounting for 97%, and indirect carbon emissions from equipment deployment and management activities accounting for 3%, among which the carbon emissions from electricity consumption in management activities account for only 0.3%. Full article

The maritime shipping sector is a major contributor to greenhouse gas emissions, particularly in coastal regions. In response, the adoption of electric ferries powered by renewable energy and supported by battery storage technologies has emerged as a viable decarbonization pathway. This study investigates the operational impacts of coordinated electric ferry charging on a medium-voltage distribution network at Gladstone Marina, Queensland, Australia. Using DIgSILENT PowerFactory integrated with MATLAB Simulink and a Python-based control system, four proposed ferry terminals equipped with BESSs (Battery Energy Storage Systems) are simulated. A dynamic model of BESS operation is optimized using a balanced hybrid metaheuristic algorithm combining GA-PSO-BFO (Genetic Algorithm-Particle Swarm Optimization-Bacterial Foraging Optimization). Simulations under 50% and 80% transformer loading conditions assess the effects of charge-only versus charge–discharge strategies. Results indicate that coordinated charge–discharge control improves voltage stability by 1.0–1.5%, reduces transformer loading by 3–4%, and decreases feeder line loading by 2.5–3.5%. Conversely, charge-only coordination offers negligible benefits. Further, quasi-dynamic analyses validate the system’s enhanced stability under coordinated energy management. These findings highlight the potential of docked electric ferries, operating under intelligent control, to act as distributed energy reserves that enhance grid flexibility and operational efficiency. Full article

A LoRaWAN application architecture comprises three functional components: (i) nodes, which convert and wirelessly transmit data as LoRaWAN messages; (ii) gateways, which receive and forward these transmissions; and (iii) network servers, which process the received data for application delivery. The nodes convert data into LoRaWAN messages and transmit them wirelessly with the hope that one or more LoRaWAN gateway will receive the messages successfully. Then, the gateways pass on the received messages to a distant network server, where various processing steps occur before the messages are forwarded to the end application. If none of the gateways can receive the messages, then they will be lost. Although this default behaviour is suitable for some applications, there are others where ensuring messages are successfully delivered at a higher rate would be helpful. One such scenario is the application in this paper: monitoring maritime vessels and fishing equipment in offshore environments characterised by intermittent or absent shore connectivity. To address this challenge, the Custodian project was initiated to develop a maritime monitoring solution with enhanced connectivity capabilities. Two additional features are especially welcome in this scenario. The most important feature is the transmission of messages created in offshore areas to end users who are offshore, regardless of the unavailability of the ground network server. An example would be fishermen who are offshore and wish to position their fishing equipment, also offshore, based on location data transmitted from nodes via LoRaWAN, even when both entities are far away from the mainland. The second aspect concerns the potential use of gateway-to-gateway communications, through gateways on various ships, to transmit messages to the coast. This setup enables fishing gear and fishing vessels to be monitored from the coast, even in the absence of a direct connection. The functional constraints of conventional commercial gateways necessitated the conceptualisation and implementation of C-Mesh, a novel relay architecture that extends LoRaWAN functionality beyond standard protocol implementations. The C-Mesh integrates with the Custodian ecosystem, alongside C-Beacon and C-Point devices, while maintaining transparent compatibility with standard LoRaWAN infrastructure components through protocol-compliant gateway emulation. Thus, compatibility with both commercially available nodes and gateways and those already in deployment is guaranteed. We provide a comprehensive description of C-Mesh, describing its hardware architecture (communications, power, and self-monitoring abilities) and data processing ability (filtering duplicate messages, security, and encryption). Sea trials carried out on board a commercial fishing vessel in Sesimbra, Portugal, proved C-Mesh to be effective. Location messages derived from fishing gear left at sea were received by an end user aboard the fishing vessel, independently of the network server on land. Additionally, field tests demonstrated that a single C-Mesh deployment functioning as a signal repeater on a vessel with an antenna elevation of $15m$ above sea level achieved a quantifiable coverage extension of 13 km (representing a 20% increase in effective transmission range), demonstrating the capacity of C-Mesh to increase LoRaWAN’s coverage. Full article

Ship detection technology represents a significant research focus within the application domain of synthetic aperture radar. Among all the detection methods, the deep learning method stands out for its high accuracy and high efficiency. However, large-scale deep learning algorithm training requires huge computing power support and large equipment to process, which is not suitable for real-time detection on edge platforms. Therefore, to achieve fast data transmission and little computation complexity, the design of lightweight computing models becomes a research hot point. In order to conquer the difficulties of the high complexity of the existing deep learning model and the balance between efficiency and high accuracy, this paper proposes a lightweight dual-domain feature fusion detection model (LD-Det) for ship target detection. This model designs three effective modules, including the following: (1) a wavelet transform method for image compression and the frequency domain feature extraction; (2) a lightweight partial convolutional module for channel feature extraction; and (3) an improved multidimensional attention module to realize the weight assignment of different dimensional features. Additionally, we propose a hybrid IoU loss function specifically designed to enhance the detection of small objects, improving localization accuracy and robustness. Then, we introduce these modules into the Yolov8 detection algorithm for implementation. The experiments are designed to verify LD-Det’s effectiveness. Compared with other algorithm models, LD-Det can not only achieve lighter weight but also take into account the precision of ship target detection. The experimental results from the SSDD dataset demonstrate that the proposed LD-Det model improves precision (P) by 1.4 percentage points while reducing the number of model parameters by 20% compared to the baseline. LD-Det effectively balances lightweight efficiency and detection accuracy, making it highly advantageous for deployment on edge platforms compared to other models. Full article

Reactive power sharing in distributed generators (DGs) is one of the key issues in the control technologies of greenship microgrids. Reactive power imbalance in ship microgrids can cause instability and potential equipment damage. In order to improve the poor performance of the traditional adaptive droop control methods used in microgrids under high-load conditions and influenced by virtual impedance parameters, this paper proposes a novel strategy based on the deep reinforcement learning DQN-VI, in which a deep Q network (DQN) is combined with the virtual impedance (VI) method. Unlike traditional methods which may use static or heuristically adjusted VI parameters, the DQN-VI strategy employs deep reinforcement learning to dynamically optimize these parameters, enhancing the microgrid’s performance under varying conditions. The proposed DQN-VI strategy considers the current situation in greenships, wherein microgrids are generally equipped with cables of different lengths and measuring the impedance of each cable is challenging due to the lack of space. By modeling the control process as a Markov decision process, the observation space, action space, and reward function are designed. In addition, a deep neural network is used to estimate the Q function that describes the relationship between the state and the action. During the training of the DQN agent, the process is optimized step-by-step by observing the state and rewards of the system, thereby effectively improving the performance of the microgrids. The comparative simulation experiments verify the effectiveness and superiority of the proposed strategy. Full article

As the core equipment in ship power systems, the accurate and real-time diagnosis of ship diesel engine faults directly affects navigation safety and operation efficiency. Existing methods (e.g., expert systems, traditional machine learning) can hardly cope with the complex failure modes and dynamic operation environment due to the problems of relying on artificial features and insufficient generalization ability. In this paper, we propose a BiLSTM-CRF-based knowledge graph construction method for ship diesel engine faults, aiming at integrating multi-source heterogeneous data through deep learning and knowledge graph technology, and mining the deep semantic associations among fault phenomena, causes, and solutions. The research framework covers data acquisition, ontology modeling, and knowledge extraction and storage, and the BiLSTM-CRF model is used to fuse bi-directional contextual features with label transfer probability to achieve high-precision entity recognition and relationship extraction. Finally, a scalable knowledge graph is constructed by Neo4j. Experiments show that the model significantly outperforms baseline methods such as HMM, CRF, and BiLSTM, and the graph visualization clearly presents the fault causality network, which supports knowledge reasoning and decision optimization. For example, “high exhaust temperature” can be related to potential causes such as “turbine failure” and “poor cooling”, and recommended measures can be taken. This method not only improves fault diagnosis accuracy and efficiency but also provides a novel method for intelligent ship health management. Full article

During the waiting period before passing through locks, inland vessels typically rely on diesel generators to power their onboard equipment, which leads to air pollution and poses more direct threats to the surrounding residents and ecological environment. To assess the extent to which green and efficient lock passage strategies can reduce air pollution, this study takes the Linked Control Policy for Vessels Passing Through Locks released by the Three Gorges Navigation Authority in China in December 2017 as the research object. It collected air quality monitoring data for six years before and after the policy implementation (2014–2020) and used a Regression Discontinuity model (RD model) to analyze the policy’s effect. The results show that compared to 2014, the average concentration of SO₂ in the air decreased by 67% in 2020, along with NO₂ decreasing by 32%, PM_2.5 by 42%, PM₁₀ by 46%, and the AQI (Air Quality Index) by 27%. The robustness test of the RD model also confirmed the causal relationship between the policy implementation and the improvement in air quality. This research is the first to systematically disclose the environmental benefits of the “soft” management policy of optimizing the lock passage process, uncovering the positive influence of active ship lock passage policy on air quality, and providing a scientific basis for promoting the implementation of policies related to lock management. Full article

This study investigates the seakeeping performance of a wind power generation ship (WPG ship). This type of vessel uses rigid sails for propulsion and submerged turbines in the form of either two or four booms to generate energy. The research includes both tank tests and simulations using Ansys AQWA, validated with the new strip method (NSM). The vessel used in this study is the container ship KCS. Overall, the power generator increases the ship’s stability and reduces roll but has almost no impact on pitch. The findings show that the 4-boom configuration offers better stability and seakeeping than the 2-boom configuration. The ship’s speed has a significant impact on the ship’s RAO, especially roll and pitch, both for the bare hull and the hull with power generation equipment. When the ship’s speed increases slightly, the roll RAO tends to decrease, but as the speed becomes higher, the RAO tends to increase. Wind conditions notably increase the roll RAO peak, reducing stability, while pitch changes are minimal. The KCS model maintains operational capability in winds up to Beaufort scale 11. Full article

This research aims to assess the integration of different fuel cell (FC) options with battery and waste heat recovery systems through a mathematical modelling process to determine the most feasible retrofit solutions for a marine electricity generation plant. This paper distinguishes itself from existing literature by incorporating future cost projection scenarios involving variables such as carbon tax, fuel, and equipment prices. It assesses the environmental impact by including upstream emissions integrated with the Energy Efficiency Existing Ship Index (EEXI) and the Carbon Intensity Indicator (CII) calculations. Real-time data have been collected from a Kamsarmax vessel to build a hybrid marine power distribution plant model for simulating six system designs. A Multi-Criteria Decision Making (MCDM) methodology ranks the scenarios depending on environmental benefits, economic performance, and system space requirements. The findings demonstrate that the hybrid configurations, including solid oxide (SOFC) and proton exchange (PEMFC) FCs, achieve a deduction in equivalent CO₂ of the plant up to 91.79% and decrease the EEXI and the average CII by 10.24% and 6.53%, respectively. Although SOFC-included configurations show slightly better economic performance and require less fuel capacity, the overall performance of PEMFC designs are ranked higher in MCDM analysis due to the higher power density. Full article

Hydraulic motors have been widely used in large-scale machinery such as ground heavy equipment and heavy-duty vehicles, ships, and so on because of their high-power drive capability. However, the driving device is confronted with constraints related to its size and weight. Typically, the hydraulic axial piston motor is preferred for its simplicity and efficiency. However, the oil distributor in traditional hydraulic motors faces significant challenges, such as evident oil leakage and power loss from the mating surfaces of the fixed oil distributor and rotating cylinder block. To enhance the reliability and performance of hydraulic motors employed in paper driving applications, this paper introduces a digital radial hydraulic motor used for heavy-duty vehicle traction. The motor is powered by an on-board pump station from which several on/off valves can distribute the hydraulic oil. This design effectively mitigates the performance degradation issues associated with friction and wear in traditional hydraulic motor oil distributors. The drive characteristics of the motor can be flexibly adjusted through the combination of valves. Our investigation into the motor’s design principles and parameter analysis is poised to make an indirect yet significant contribution to the optimization of heavy-duty vehicle traction systems. This paper delineates the application conditions and operational principles of the digital hydraulic motor, thoroughly analyzes the intricate topological interrelationships of its parameters, and meticulously develops a detailed component-level model. Through comprehensive calculations, it reveals the impact of configuration and flow valve parameters on motor efficiency. A simulation model is established for the purpose of verification. Furthermore, the influence of the flow allocation method on efficiency and pressure pulsation is examined, leading to the proposal of a novel flow allocation strategy, the efficacy of which is substantiated through simulation. In conclusion, this paper formulates critical insights to inform the design and selection of components for digital hydraulic motors. These findings may provide a feasible solution for heavy-duty vehicle traction application scenarios. Full article

The decarbonization of energy systems is forcing the development of renewable energy generation and consumption technologies. Photovoltaic systems are being used in almost every industry, including autonomous power systems used on ships, space vehicles, or flying platforms, where the voltage supplying specific equipment can change in an overridingly controlled manner. Feeding energy from a renewable source into a power system with highly dynamic frequency changes is not possible for traditional grid converter control strategies. This is caused by the synchronization system, which is designed for a fixed value of the grid voltage frequency, and by the proportional-resonant controllers used. In this paper, it is shown that frequency tracking correction causes deviations from the unit amplitude of synchronization signals, causing errors in the reference signals responsible for the active and reactive components of the converter current. To solve this problem, a new variable frequency adaptation system using a generalized second-order integrator was proposed. As a result, synchronization signals of unit amplitude were obtained. Due to the proposed method, the proportional-resonant controller was able to control the active and reactive components of the current even when the voltage frequency changes, adjusting the resonant frequency. Full article

The diesel engine on a ship is crucial as it serves as the primary power source, significantly influencing both the vessel’s efficiency and safety. Monitoring metal wear particles found in lubricating oil is essential for assessing the lubrication condition of mechanical equipment onboard and anticipating potential failures. Analyzing these metal wear particles allows us to gauge the wear status of bearing pairs within the machinery, thereby providing a technical foundation for routine maintenance activities. However, under real operating conditions, it can be challenging to prevent multiple metal particles from simultaneously passing through sensors. To address this issue, this research introduces an innovative three-coil induction sensor that employs a variable-frequency excitation technique to explore how induction and eddy currents interact. The findings indicate that when the excitation frequency changes, the peak value of the signal from 337 μm iron particles only increases by 3.35 times, while the peak value of the signal from 340 μm copper particles increases by 22.69 times. Consequently, this study recommends using changes in excitation frequency to differentiate between mixed metal particles made of various materials. Full article

Submerged cargo pumps installed on board tankers are one of the most important components of their cargo system. As they are installed directly in the cargo tanks, they are usually equipped with a hydraulic drive whose power and capacity are controlled by constant-torque controllers. These have a significant impact on the technical and performance characteristics of the cargo pumps. This paper presents a methodology for calculating the performance characteristics of submerged cargo pumps, taking into account the energy efficiency of their hydraulic drive motors. Due to their number and power, the cargo pumps are powered from the ship’s hydraulic central loading system. This paper describes the main parts of the hydraulic power system structure and the functions of the constant torque controller of the STC type. A mathematical model has been developed to use the basic characteristics of submerged cargo pumps made for the base cargo (water) sent by the pump manufacturer for the case of handling liquid cargo with different parameters. The model considers the characteristics of the hydraulic drive, including a Bosch Rexroth A2FM type hydraulic drive motor and a constant torque controller. The results of simulation calculations of the performance characteristics of an example cargo pump are presented and compared with measurements of the characteristics of this pump on a product tanker. The mathematical model presented in this paper is of utilitarian value, enabling calculations to be carried out without the need for time-consuming CFD numerical methods, making it useful for port and fuel terminal logistics services, ship crews and services managing the operation of product tanker fleets. Full article