Search Results (55)

With the development of shipping to harsh marine environment, it is very important to understand the transient behavior of a marine diesel engine in high sea conditions. Wave-induced hull motion will lead to severe load fluctuations and air-fuel ratio imbalance. In this study, an integrated simulation platform coupled with environmental loads, hull dynamics, propeller characteristics and a high-fidelity thermodynamic engine model was constructed to explore the response characteristics of the propulsion system. The model integrates a zero-dimensional multi-zone combustion method, turbocharger dynamic characteristics and an incremental PID governor, and has been verified based on the bench test data of TBD234V12 diesel engine and the 20 m Wigley standard ship. The simulation results under the sea conditions from level 7 to 9 show that the transient load has a nonlinear amplification effect. Specifically, from sea state 7 to sea state 9, the engine load fluctuation range expands by 2.0 times, while the main peak amplitude of speed fluctuation increases by 3.7 times. Furthermore, the peak exhaust pressure rises by 1.8 times, and the exhaust temperature fluctuation amplitude broadens by 35%. Frequency domain analysis further identified the low-frequency energy concentration phenomenon in the exhaust pressure spectrum and the precursor characteristics of compressor surge. The research results quantify the deterioration law of thermodynamic stability and mechanical stress under wave disturbance, and provide an important reference for the formulation of an engine robust control strategy and fatigue life assessment under high sea conditions. Full article

As the core pillar of international trade, the global shipping industry has seen its carbon and pollutant emissions become a key challenge in global environmental governance. Statistics indicate that ship carbon emissions account for 3% of the world’s total anthropogenic CO₂ emissions, while contributing 20% of global NO_x and 12% of SO₂ emissions, posing a serious threat to coastal ecosystems and public health. In response to the International Maritime Organization (IMO) “Net Zero Framework” and national green shipping policies, the transformation of ship power systems toward low-carbon and zero-carbon operation has become an inevitable trend. This paper systematically reviews the research progress and application status of green energy materials for ships, focusing on the working principles, technical characteristics, and engineering application cases of solar photovoltaic (PV) materials, wind energy utilization technologies, fuel cell materials, and alternative clean energy fuels (e.g., liquefied natural gas (LNG), methanol, and hydrogen energy). It also discusses the integration mode and optimization strategy of multi-energy hybrid power systems. The research findings show that solar photovoltaic technology has achieved large-scale application in coastal ships; hydrogen fuel cells are suitable for long-range ocean navigation scenarios due to their high energy density; LNG and methanol have become the current mainstream alternative fuels, relying on mature infrastructure; and hybrid energy systems can significantly improve power supply reliability and emission reduction efficiency through multi-energy complementarity. Finally, aiming at the existing bottlenecks (e.g., cost, energy storage, and safety) of various technologies, future development directions are proposed. This study provides a reference for the technological breakthrough and engineering practice of green energy power systems for ships and contributes to the realization of the “carbon neutrality” goal in the global shipping industry. Full article

Maritime emergency response requires broadband and reliable communications in sea areas where shore coverage is limited or emergency connectivity is temporarily unavailable, making rapid on-demand aerial networking essential. Unmanned aerial vehicles (UAVs) acting as aerial base stations can be rapidly deployed to provide on-demand coverage; however, ship mobility, heterogeneous emergency priorities, and UAV endurance limitations make the joint optimization of user association and multi-UAV deployment a challenging mixed-integer, long-horizon decision problem. This paper considers a multi-UAV maritime emergency communication system where ships are categorized into multiple priority classes and served links must satisfy a minimum signal-to-noise ratio (SNR) constraint. We formulate a long-term system-utility maximization problem that jointly determines (i) per-slot association between UAVs and ships under capacity, priority, and SNR constraints, and (ii) dynamic UAV deployment under mobility, geofencing, and battery constraints. To obtain tractable and high-quality solutions, we decompose the problem into two coupled subproblems. For user association, we propose a Priority-Aware Branch-and-Cut (PA-BAC) algorithm that integrates linear programming relaxation, cutting-plane tightening, and priority-guided branching, with a priority-greedy feasible initialization to accelerate incumbent improvement. For dynamic deployment, we develop an Enhanced Multi-Agent Proximal Policy Optimization (E-MAPPO) method featuring a global value network, entropy regularization, and sequential actor updates to enhance learning stability and exploration. Importantly, the PA-BAC association is embedded into the learning loop to provide reliable, constraint-satisfying per-slot rewards and reduce the burden of end-to-end learning over hybrid-action spaces. Simulation results demonstrate that PA-BAC consistently improves normalized priority-weighted throughput over heuristic association baselines. Moreover, by mathematically enforcing priority and QoS feasibility at every slot and delegating only continuous mobility to MARL, the integrated E-MAPPO-PA-BAC framework achieves higher long-term system utility, improved energy efficiency, and strong robustness across varying ship densities—properties that are vital for time-sensitive maritime emergency communications. Additional runtime, sensitivity, and AIS-driven trace evaluations further verify the computational practicality of PA-BAC and the applicability of the proposed framework under realistic ship mobility patterns. Full article

Maritime transport accounts for around 3% of global anthropogenic greenhouse gas (GHG) emissions, a share expected to grow without effective technological and regulatory intervention. Recent policy developments, including the IMO Revised GHG Strategy (2023), the extension of the EU Emissions Trading System to maritime transport, and the FuelEU Maritime Regulation, require ports and shipping stakeholders to evaluate multiple decarbonization technologies under complex and often conflicting constraints. These decisions involve trade-offs across economic, technical, environmental, social, and cyber–physical security dimensions, which are not adequately addressed by conventional decision-support tools. This paper introduces XAI–MCDA-HoDEM, an explainable multi-criteria decision framework integrating Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and SHAP-based explainability. The framework explicitly incorporates cyber–physical security as a core evaluation criterion and provides transparent, criterion-level explanations of decision outcomes. Using real-world data, the methodology is demonstrated through an illustrative case study and empirically validated at the Port of Rotterdam. Results show stable and robust rankings, alignment with observed port decarbonization strategies, and improved interpretability of decision drivers. The proposed framework supports transparent, policy-relevant decision-making for the maritime energy transition. Full article

Maritime transport is under increasing pressure to cut greenhouse gas and pollutant emissions to meet global decarbonization goals and tighter environmental standards. Ship electric propulsion systems offer a promising solution for short-range maritime operations, particularly for small vessels and coastal activities. Full-electric vessels can significantly reduce operational emissions; however, a key challenge is the extensive charging time for onboard energy storage, which can affect operational continuity and logistical efficiency. This study examines mission planning and energy management for a hybrid multi-source electric mail boat operating in the Aeolian archipelago. It evaluates the viability and performance of a daily inter-island route powered by a high-temperature methanol fuel cell, batteries, and photovoltaic panels. A routing and simulation framework was developed to model the boat’s itinerary among seven islands, accounting for realistic navigation speeds, scheduled stops, solar energy availability, and battery state-of-charge constraints. The study analyzes distance, travel time, energy consumption, solar power generation, and fuel–electric usage with high temporal resolution, enabling detailed analysis of power flows during sailing and docking. Several operational strategies were assessed, including periods of increased speed supported by battery assistance and fuel–electric cell output, combined with coordinated energy management to keep battery levels above a lower acceptable threshold while completing the route in a single day. The methodology provides a practical tool for planning low-emission island networks and supports the integration of innovative energy systems into small electric workboats operating in specific maritime regions. Full article

With increasingly stringent greenhouse gas emission regulations in the shipping industry, there is an urgent need for an efficient energy management strategy for new energy ship power systems. However, existing dispatch models often overlook the dynamic energy-saving potential of active drag reduction technologies and lack effective optimization algorithms capable of handling high-dimensional, multi-constrained problems. To address these problems, this paper proposes a novel integrated dispatch framework for hybrid energy ship power systems that incorporates air lubrication systems. First, a unified multi-energy dispatch model is established, coupling the dynamic operation of air lubrication systems with electrical, thermal, and propulsion energy flows. Second, an Improved Traffic Jam Optimizer algorithm is proposed, which enhances global exploration and local exploitation through a nonlinear parameter adaptation mechanism, differential mutation strategy, and dynamic hybrid search architecture. Convergence analysis based on Markov chain theory is provided to guarantee algorithmic reliability. Simulation results demonstrate that the proposed algorithm outperforms existing methods in terms of convergence speed, solution accuracy, and stability. Furthermore, integrating air lubrication systems into the ship power system reduces total operating costs and greenhouse gas emissions by up to 20.569% and 6.310%, respectively. Full article

The dynamic response of the secondary circuit system in nuclear propulsion plants is critical to the power output, safety, and energy efficiency of nuclear-powered ships. High-fidelity thermo-hydraulic simulation models can accurately capture system transients but are computationally expensive and unsuitable for real-time applications. To address this limitation, this study proposes a reduced-order dynamic parameter prediction method that integrates high-fidelity simulation with deep learning. A multi-operating-condition simulation model of a typical nuclear-powered ship secondary circuit system is developed to generate time-series data covering load ramping and propulsion mode switching. Based on this dataset, a conventional recurrent neural network (RNN) and a multilayer long short-term memory (LSTM) network are constructed for multivariate autoregressive prediction of 17 key dynamic parameters, and their performances are systematically compared. Results show that the LSTM significantly outperforms the RNN in capturing long-term temporal dependencies, achieving average RMSE and MAPE values of 0.0228% and 0.365%, respectively. The proposed model completes 50-step-ahead prediction within 0.84 s, satisfying real-time requirements. The hybrid simulation-driven and data-driven framework provides a practical solution for intelligent monitoring and control optimization of nuclear-powered ship propulsion systems. Full article

To systematically review the research progress, methodological frameworks, and application characteristics of energy management technologies for All-Electric Ships (AES), this review provides a comprehensive and critical survey of studies published over the past two decades, following the technical trajectory of multi-energy coupling–multi-objective optimization–engineering-oriented operation. Based on a structured analysis of representative literature, the review first elucidates the overall architecture and operational characteristics of AES energy systems from a system-level perspective, highlighting their core advantages as “mobile microgrids” in terms of multi-energy coordination and dispatch flexibility. On this basis, a structured classification framework for energy management strategies is established, and the theoretical foundations, applicable scenarios, and engineering feasibility of rule-based, optimization-based, uncertainty-aware, and intelligent/data-driven approaches are comparatively reviewed and discussed. Furthermore, focusing on key research themes—including multi-energy system optimization, ship–port–microgrid coordinated operation, battery safety and lifetime-oriented management, and real-time energy management strategies—the review synthesizes the main findings and engineering validation progress reported in recent studies. The analysis indicates that, with the integration of fuel cells, renewable energy sources, and Hybrid Energy Storage Systems (HESS), energy management for AES has evolved from a single power allocation problem into a system-level optimization challenge involving multiple time scales, multiple objectives, and diverse sources of uncertainty. Optimization-based and Model Predictive Control (MPC) methods have shown promising performance in many simulation and pilot-scale studies for improving energy efficiency and emission performance, while robust optimization and data-driven approaches offer useful support for enhancing operational resilience, prediction capability, and decision quality under complex and uncertain conditions. These advances collectively contribute to the environmental, economic, and operational sustainability of maritime transport by reducing greenhouse gas emissions, extending equipment lifetime, and enabling efficient integration of renewable energy sources. At the same time, the current literature still reveals important limitations related to model fidelity, data availability, validation maturity, and the gap between methodological sophistication and practical deployment. Overall, an increasingly structured but still evolving research framework has emerged in this field. Future research should further strengthen ship–port–microgrid coordinated energy management frameworks, develop system-level optimization methods that integrate safety constraints and uncertainty, and advance intelligent Energy Management Systems (EMS) oriented toward sustainable zero-carbon shipping objectives. Full article

This study aims to explore and propose a design-oriented methodology for solar–thermal methanol reforming (ST-MSR) hydrogen production equipment suitable for marine applications. To address key challenges such as the intermittency of solar energy, spatial and environmental constraints on board ships, operational safety, and user experience, a multidisciplinary integrated-design decision-making framework is established. First, the Kano model is employed to systematically analyze the latent needs of target users regarding ST-MSR equipment, while the analytic hierarchy process (AHP) is used to determine the weighting of evaluation criteria. Second, the theory of inventive problem solving (TRIZ) is applied to generate innovative conceptual design solutions. Finally, the technique for order preference by similarity to an ideal solution (TOPSIS) is adopted to conduct a multi-dimensional comprehensive evaluation and optimization-based selection of the conceptual alternatives. The optimal design scheme is thus identified in terms of energy performance, product characteristics, user experience, economic feasibility, and environmental adaptability. The results indicate that the microchannel and phase-change thermal-storage integrated solar–thermal-tracking chemical reactor achieves the highest comprehensive evaluation score among the proposed schemes, demonstrating superior performance in terms of safety, energy efficiency, and adaptability to marine environments. This research provides a systematic industrial design methodology and practical reference for the design and product development of clean energy equipment for ships, contributing to the green and sustainable transformation of the maritime industry. Full article

The marine centrifugal pump is one of the most energy-intensive pieces of equipment in ship auxiliary machinery, and the efficient design of its hydraulic components can effectively reduce the total energy consumption of the ship system. Aiming at the complex three-dimensional twisted blade profile structure of the marine centrifugal pump, this paper optimized the clonal selection algorithm and constructed an automatic hydraulic optimization design method for the high-efficiency centrifugal pump impeller. Considering the multi-condition operation characteristics of the marine centrifugal pump, a performance test platform for the marine centrifugal pump was built, and the actual operating conditions of the model pump were tested to obtain its performance characteristics under operating conditions. The numerical simulation method was employed to capture and analyze the internal flow field and flow characteristics of the model pump. Addressing the design challenges of the marine centrifugal pump impeller, which involve multiple parameters with significant interactions, a traditional clonal selection algorithm was enhanced using a Slime Mold Algorithm, and a hybrid Clonal Selection Algorithm integrated with Slime Mold and Tangent Flight mechanisms was established. Based on the MATLAB and ANSYS platforms, an automated hydraulic optimization design framework for the centrifugal pump impeller was established. Using the optimized clonal selection algorithm, with the operational efficiency of the model pump as the optimization objective and controlling ten key geometric parameters of the blade profile through Bézier curves, the blade profile optimization design was achieved. The pump hydraulic efficiency under the rated flow condition increased by 7%. The unsteady internal flow efficiency of the optimized marine centrifugal pump was significantly improved. The blade optimization alleviated flow separation phenomena on the tangential surface of the impeller and in partial regions of the volute, reduced the flow loss area, and significantly decreased overall flow losses. Full article

In the context of global efforts to promote energy conservation and emission reduction, geopolitical conflicts have intensified the challenges of mitigating marine climate change, posing increasingly severe economic and climatic pressures on the shipping industry worldwide. Research on multi-objective route optimization is of great significance for addressing climate challenges and enhancing economic efficiencies. This field focuses on constructing multi-objective optimization models that aim to reduce voyage time, fuel consumption, navigational risks, and carbon emissions and solving them using various algorithms. However, determining the optimal route and sailing speed under complex and variable meteorological conditions remains a significant challenge owing to the presence of numerous unevenly distributed feasible solutions within a vast solution space, making it difficult for traditional intelligent algorithms to effectively explore this space. To address this issue, this study proposes a hybrid algorithm named NRRT by integrating the Rapidly exploring Random Tree (RRT) algorithm with the Non-dominated Sorting Genetic Algorithm III (NSGA-III). By improving the sampling logic of the RRT algorithm and combining the vessel’s voluntary speed loss with the sampling step size, the algorithm efficiently explored the feasible route set, enhancing the quality and diversity of the solutions. Subsequently, the NSGA-III algorithm treats sailing speed and heading as direct decision variables to perform multi-objective optimization on the explored routes and generate Pareto-optimal solutions. The optimization results demonstrate that the proposed method excels at generating route plans that effectively reduce costs, minimize emissions, and mitigate risks compared with the 3D Dijkstra algorithm and the improved NSGA-III algorithm. Full article

To address the challenges of energy conservation and emission reduction in the shipping industry, this study proposes an innovative scheduling strategy for the ship integrated energy system (SIES) based on data-driven fuel consumption prediction and multi-objective optimization. A multi-feature dual-time scale Long Short-Term Memory (LSTM) network is developed, integrating Automatic Identification System (AIS) data with an average resolution of 6 min, meteorological conditions, and vessel state parameters, achieving fuel consumption prediction across dual time scales. The model outperforms other machine learning models (e.g., CNN, XGBoost) in terms of R², MAE, RMSE, and SMAPE. Dynamic simulation of annual cooling, heating, and power loads for crew accommodation areas, based on spatiotemporally matched customized meteorological data, reveals that the annual load is dominated by cooling demand, with significant seasonal fluctuations; summer loads are higher and more volatile than winter loads. A hybrid energy system integrating photovoltaic (PV) generation, energy storage, carbon capture and storage (CCS), and diesel engines is constructed. By treating the CCS load as a adjustable resource, the Non-dominated Sorting Genetic Algorithm (NSGA-II) is employed to solve the environmental–economic multi-objective optimization problem, simultaneously minimizing carbon emissions and present value of the total cost (PVC). Case studies conducted on a 79,970 DWT bulk carrier (Guangzhou–Qinhuangdao route) demonstrate the strategy’s effectiveness. The synergistic operation of solar energy and the energy storage system facilitates carbon emission reductions of 23.6% to 40.0% through fuel savings; during summer with abundant solar resources, over 95% of the CCS load can be covered. Economic analysis indicates that fuel savings from renewable energy can recover the investment in the PV and battery storage system within approximately 6 years. This integrated data-driven energy management framework mitigates CCS-induced parasitic loads and emissions, partially resolving the “carbon emissions vs. cost” dilemma, and provides a viable pathway for decarbonizing conventional diesel-powered ships, contributing to sustainable maritime operations. Full article

The global shipping industry faces severe carbon emission challenges. Harbor tugs, as significant contributors to port emissions, require improved energy efficiency. However, their sailing conditions are complex and dynamic, making temporal feature characterization difficult with traditional static or simplistic clustering methods. To address this, this study proposes a novel method for constructing typical sailing conditions by integrating an enhanced clustering approach with Hidden Markov Models (HMM). First, kinematic segments are extracted from processed ship speed data, and key features are selected and reduced via Principal Component Analysis (PCA). Subsequently, an improved clustering model combining the Whale Optimization Algorithm (WOA) and K-means++ is developed to categorize segments into six distinct condition types. These clustered states then serve as the hidden states of an HMM, whose learned transition matrix synthesizes a 3600 s typical sailing condition profile. The constructed profile is validated through multi-dimensional comparison with original data, demonstrating high fidelity in statistical characteristics, temporal properties, and distribution similarity. The results confirm that the proposed method can accurately replicate the operational patterns of harbor tugs. This study provides a reliable data foundation for the energy efficiency assessment and optimization of harbor tugs and offers a new methodological perspective for constructing operational profiles for ships and other mobile machinery. Full article

The maritime sector is undergoing a profound transformation driven by disruptive technologies and global decarbonization objectives, placing new demands on Maritime Education and Training (MET) systems. Equipping maritime professionals with competencies for low-carbon shipping is now as critical as technological advancement itself. This study examines how disruptive technologies can be effectively integrated into MET frameworks to support environmental sustainability, using Panama as a representative case study of a major flag and maritime service state. A mixed-methods approach was adopted, combining a structured literature review, expert surveys, and a multi-criteria decision-making analysis based on the Analytic Hierarchy Process (AHP). The findings reveal a significant misalignment between existing MET curricula and the competencies required for decarbonized maritime operations. Key gaps include limited training in alternative fuels, emissions measurement and reporting, energy-efficient technologies, digital analytics, and regulatory compliance. Stakeholders also reported fragmented training provision, uneven access to emerging technologies, and weak coordination between academia, industry, and regulators, particularly in developing contexts. The results highlight the urgent need for curriculum reform and stronger cross-sector collaboration to align MET with evolving technological and regulatory demands. The study provides an applied, evidence-based framework for MET reform, with insights transferable to other systems facing similar decarbonization challenges. Full article

Ship weather routing optimization has evolved from deterministic great-circle navigation to sophisticated frameworks that account for dynamic environmental conditions and operational constraints. This paper presents a waypoint-sequencing Model Predictive Control (MPC) approach for energy-efficient ship weather routing under forecast uncertainty. The proposed rolling horizon framework integrates neural network-based vessel performance models with ensemble weather forecasts to enable real-time route adaptation while balancing fuel efficiency, navigational safety, and path smoothness objectives. The MPC controller operates with a 6 h control horizon and 24 h prediction horizon, re-optimizing every 6 h using updated meteorological forecasts. A multi-objective cost function prioritizes fuel consumption (60%), safety considerations (30%), and trajectory smoothness (10%), with an exponential discount factor (γ = 0.95) to account for increasing forecast uncertainty. The framework discretises planned routes into waypoints and optimizes heading angles and discrete speed options (12.0, 13.5, and 14.5 knots) at each control step. Validation using 21 transatlantic voyage scenarios with real hindcast weather data demonstrates the method’s capability to propagate uncertainties through ship performance models, yielding probabilistic estimates for attainable speed, fuel consumption, and estimated time of arrival (ETA). The methodology establishes a foundation for more advanced stochastic optimization approaches while offering immediate operational value through its computational tractability and integration with existing ship decision support systems. Full article