Search Results (1,344)

To address structural redundancy and excessive vibration in a specific range hood model, this study focuses on structural lightweighting design optimization. Under strength and resonance avoidance constraints, optimization integrates experimental testing and finite element analysis. Modal analysis reveals a prominent resonance at 49.8 Hz, which coincides with the test results. Topography optimization of the impeller side plate ribs shifts its natural frequency, eliminating resonance while reducing weight significantly. Subsequently, topography optimization of key parts such as the fan housing improves stiffness, facilitating further lightweighting. Two optimization methods, direct solver and orthogonal experiment were applied to minimize the total mass under strength and dynamic constraints. Both schemes met all design requirements with weight reductions of 17.7% and 18.3%, respectively. Vibration test of the optimized design shows that accelerations at key points were reduced significantly. Full article

Ship–bridge collisions in cross-sea bridge waterways are rare but potentially catastrophic events, making conventional accident-based risk assessment difficult to implement effectively. Existing AIS-based indicators capture vessel behaviors but insufficiently quantify bridge-passage safety margins, especially dynamic aspects such as crossing posture and readiness prior to bridge transit. To address this limitation, this study proposes a data-driven framework for detecting unsafe ship–bridge passages using two bridge-passage-oriented surrogate safety measures (SSMs) and extreme value theory (EVT). The Bridge-passage Lateral Clearance Margin (BLCM) quantifies the effective lateral safety margin retained during the realized bridge-crossing stage, while the Bridge-passage Readiness Lead Time (BRLT) measures how early a vessel becomes stably prepared for bridge passage before crossing. The Peaks Over Threshold (POT) model is first used to characterize the marginal extremes of the two indicators, and a bivariate threshold exceedance model (BTE) is then established to examine their joint risk behavior. Case studies of the Jintang Bridge and Zhoudai Bridge waterways demonstrate that the proposed framework can effectively screen and identify trajectories with unsafe or margin-deficient bridge-passage characteristics. The results show that unsafe passages are typically associated with both reduced lateral clearance and insufficient preparation time, and that joint modeling of the two indicators improves risk identification performance. The findings suggest that ship–bridge risk is better interpreted from the perspective of passage quality deficiency rather than simple geometric proximity. The proposed framework provides an interpretable tool for retrospective unsafe passage screening, traffic monitoring support, and post-event safety analysis in complex bridge waterways. Full article

Hydrogen enrichment of compression ignition (CI) engines has emerged as a promising strategy to simultaneously enhance thermal efficiency and reduce carbon-based emissions. This study numerically investigates how hydrogen enrichment affects engine performance and emissions in methanol–diesel dual-fuel CI engines, a combustion mode gaining increasing attention for replacing fossil diesel with sustainable fuels, particularly in hard-to-abate sectors such as maritime transport. The simulations are based on the Unsteady Reynolds-Averaged Navier–Stokes (URANS) equations, incorporating the RNG k–ε turbulence model, the Eddy Dissipation Concept (EDC) for turbulence–chemistry interaction, and the G-equation for turbulent premixed flame propagation. The numerical model is validated against experimental data for in-cylinder pressure and heat release rate at 45% methanol substitution ratio (by energy). The results indicate that increasing the hydrogen enrichment ratio (HER, defined on an energy basis) from 5% to 20% raises the Sauter mean diameter (SMD) of the diesel fuel from 20.2 µm to 28.0 µm (+38%), driven by reduced aerodynamic breakup intensity associated with modified gas-phase properties under hydrogen enrichment. Furthermore, hydrogen’s elevated adiabatic flame temperature and superior mass diffusivity intensify combustion, raising peak in-cylinder pressure from 75.2 to 79.1 bar (+5.2%), amplifying the peak heat release rate from 129 to 211 J/°CA (+63.6%), and elevating maximum in-cylinder temperature from 1542 to 1735 K (+193 K). Under the investigated CFD operating conditions, these thermodynamic gains translate into an engine-level 6% improvement in indicated thermal efficiency and a 14% reduction in indicated specific fuel consumption (accounting for hydrogen, methanol, and diesel) at HER 20%. On the emissions front, CO₂ declines by 24% in direct proportion to the carbon-containing fuel mass displaced by hydrogen substitution, while NOₓ increases approximately twofold from 0.10 g/kWh at HER 0 to 0.21 g/kWh at HER 20, driven by peak temperature elevation. These findings establish hydrogen-enriched methanol–diesel dual-fuel combustion as a viable pathway toward high-efficiency, low-carbon CI engine operation for heavy-duty transport applications. Full article

Containerized mobile power supplies (CMPS), a critical energy replenishment carrier for all-electric ships, have caused severe economic losses via frequent fire and explosion accidents during temporary port storage in recent years. Existing literature focuses on battery thermal runaway under laboratory conditions and maritime transport risk analysis, but its conclusions are not directly applicable to port temporary storage. Port storage, featuring full-charge quiescent placement and high turnover, differs significantly from maritime transport, while its high-temperature and humid environment is distinct from laboratory settings. Furthermore, no system safety-based risk assessment framework exists, failing to deliver targeted mitigation strategies for practical operations. To address these issues, fault tree analysis (FTA), Bayesian network (BN), and attack–defense game theory were combined to build a systematic safety risk assessment framework. FTA clarified the hazard factors’ correlation mechanism; based on FTA, BN conducted a quantitative evaluation. Extended from BN results, attack–defense game theory identified key risk evolution paths and formulated targeted prevention and control measures. The main conclusions are as follows: Combined with similar accident features and port storage scenario attributes, internal correlations between hazard-inducing factors were clarified via FTA. Based on expert evaluations and BN calculation, the target port’s fire accident occurrence probability was determined as 2.41%, with two core root nodes identified via sensitivity analysis. Two critical risk evolution paths corresponding to IE1 (thermal runaway initiation) and IE2 (failure of protection and emergency response systems) were identified via game theory and traversal method, with occurrence probabilities of 1.50% and 1.77%, respectively. Targeted prevention and control measures adapted to the port storage scenario were proposed based on path triggering mechanisms. These findings provide theoretical support for port enterprises to improve CMPS fire prevention and emergency response capabilities, elevate port safety management levels, and promote the safe development of the all-electric vessel shipping industry. Full article

This study proposes a Behavior-Calibrated Endogenous Choice 2SFCA (BCEC-2SFCA) framework for assessing spatial accessibility to tertiary hospitals. Using large-scale taxi trajectory data from Ningbo, China, we empirically calibrate the Huff model parameters (α =1.1758, β =2.9608) based on observed hospital choices and construct travel time and distance matrices from observed trips. Unlike existing Huff-based FCA approaches that assume parameter values, BCEC-2SFCA jointly estimates the attractiveness elasticity and distance-decay coefficient directly from local healthcare travel behavior and integrates these calibrated probabilities into a 2SFCA structure where hospital catchments are endogenously generated rather than exogenously imposed. Compared with conventional Gaussian 2SFCA, the BCEC-2SFCA model produces a continuously varying and behaviorally plausible accessibility surface and better replicates the relative order of hospital attractiveness (ρ = 0.527, p < 0.05), although its RMSE is slightly higher (0.02700 vs. 0.02211) while MAPE is clearly lower (32.17% vs. 42.12%). Robustness checks using all 22 hospitals confirm stable estimates, and subgroup analyses show consistent advantages across hospital scales. The framework is specifically designed for high-order medical services with strong inter-facility competition—such as tertiary hospitals—and its applicability to proximity-based services is limited. Full article

As offshore wind energy advances into deeper waters, the dynamic response and safety assessment of tension leg platform (TLP) wind turbines under complex marine conditions have become focal research points. This study investigates a 15 MW TLP wind turbine, acquiring data on motion responses, mooring tensions, and tower-base loads through time-domain analysis, with extreme value estimation conducted using the mean up-crossing rate method. The results indicate that under normal operating conditions, second-order wave forces significantly influence extreme response estimation. At an exceedance probability of 0.01, the second-order sum-frequency force increases the extreme tower base shear by 4.28% and the bending moment by 10.11% compared to the first-order-only case, while the difference-frequency force has a minor effect. Different wind-wave incidence angles cause distinct variations in turbine motion, with head-on incidence exciting the largest wave-frequency responses and lateral incidence producing relatively weaker excitation effects. Furthermore, the coupling effect between incident direction and second-order wave forces further amplifies extreme response risks. Therefore, it is essential to fully assess the prevailing wind-wave directions in the target sea area and consider the effects of second-order wave forces, especially the sum-frequency component, to ensure the long-term safe operation of TLP wind turbines under complex sea conditions. Full article

Technological advancements have revolutionized photogrammetry, with the implementation of unmanned aerial vehicles for capturing images from different angles and the ease of obtaining sensor position information at the time of capture. This study evaluates the accuracy of direct georeferencing via Networked Transport of Radio Technical Commission for Maritime Services Via Internet Protocol, in the orthomosaic as a photogrammetric product in a large urban area with steep and highly variable topography, comparing it with the coordinates of nine checkpoints obtained with GNSS equipment connected to the National Active Geodetic Network, managed by the National Institute of Statistics and Geography of Mexico. An orthomosaic of the historic center of Zacatecas was obtained with a resolution of 2.70 cm/pixel. The orthomosaic coordinates, compared to those of the GNSS equipment, show a root mean square error (RMSE) of 0.78 m in the horizontal coordinates and an RMSE of 1.22 m in the vertical coordinates. Previous studies prove the efficiency of the Continuously Operating Reference Station module and network with other aircraft; this study determines that this is true for large areas with high coverage and quality in the internet network, but with rugged topography, the results are not accurate. Full article

To optimize the freight distribution structure of ports and reduce carbon emissions from freight transportation, this paper develops a bi-level programming model for freight traffic shifting between roadway and waterway networks that incorporates carbon emissions. First, a complex freight network based on the roadway–water transport system is constructed, comprising roadway networks, inland waterway networks, maritime networks, and transshipment nodes. A traffic impedance model is then formulated within this complex network framework, integrating the roadway BPR function, the M/M/1 queuing model for lock passage time on inland waterways, and the M/M/c queuing model for port cargo handling into the impedance function. This allows micro-level congestion effects to be combined with macro-level traffic assignment. Next, a bi-level programming model for freight traffic shifting in the roadway–water network system is established, with carbon emissions incorporated. The NSGA-II algorithm is employed to determine the optimal carbon subsidy level, based on which the traffic distribution in the complex freight network is analyzed. Finally, the proposed model is applied to the roadway–waterway bimodal network in the Hangzhou Bay port area of Cixi. The results indicate that without subsidies, the waterway transport share is only 1.74%. The optimal subsidy efficiency frontier is identified at CNY 350,000/day, where the waterway share increases to 22.7% and carbon emissions decrease by 33.27 tons/day. The subsidy strategy evolves through three stages: first, prioritizing maritime shipping; second, jointly promoting inland and maritime shipping; and finally, shifting focus to infrastructure investment once subsidies reach saturation. This study offers a quantitative analytical tool for designing differentiated carbon subsidy policies to facilitate the road-to-waterway modal shift under fiscal constraints. Full article

Given the increasing concern around greenhouse gas emissions and the decline in the availability of fossil fuels, there is increasing global demand to develop alternate fuels for maritime transportation that are sustainable and which have lower greenhouse gas emissions. Methanol is one such alternative fuel that has garnered considerable attention given its potential to be produced by more sustainable processes and its more favorable greenhouse gas emission profile in comparison with current fossil fuels. Understanding the physical and chemical properties of methanol under a range of conditions is essential for its development as a marine fuel. In this study, we seek to define physical and chemical properties of different methanol samples to simulate real-world storage conditions as these data are lacking in the literature. Several methanol samples were evaluated: nearly pure methanol; International Organization for Standardization (ISO) marine methanol (MM) grades A, B, and C; and methanol plus higher alcohols. We first evaluated all methanol samples for impurities, acetic acid content, density, and distillation range. We then characterized the effects of water absorption and found that methanol can easily absorb unacceptable water content from humid air within hours, necessitating storage conditions that prevent this process. In eight-week aging experiments at 20 °C and 40 °C in ambient air, we did not observe significant oxidation for any of the methanol samples; however, we did observe increases in acid number. We assessed the impact of contamination of methanol with water, marine gas oil (MGO), and an MGO–biodiesel mixture on density, viscosity, distillation range, and lubricity. Finally, we show that MGO contamination of methanol results in a slight increase in sooting tendency. In aggregate, our results provide an in-depth analysis of physical and chemical properties of methanol as well as the impacts of storage conditions and impurities on the properties of fuel methanol. 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

The transition towards alternative marine fuels introduces new safety challenges related to onboard storage, distribution, and fuel management, due to the markedly different physical and chemical properties of methane, methanol, ammonia, and hydrogen. While numerous studies address the risks of individual fuels, there is a lack of structured and comparable risk-assessment methodologies to support early-stage fuel selection and preliminary system design under a unified framework. This study introduces the Methodology to Alternative-fuels Hazardous Identification, a hybrid framework that integrates HAZOP-based deviation analysis with HAZID-style risk classification to enable a consistent qualitative–quantitative comparison of alternative marine fuel systems. The methodology is applied to representative storage and distribution architectures for methane, methanol, ammonia, compressed hydrogen, and liquefied hydrogen, allowing the identification of dominant risk drivers and system-level vulnerabilities across fuel options. The results reveal distinct fuel-specific risk profiles. Methane and methanol are mainly associated with moderate risks linked to operational temperature deviations and system controllability. Ammonia exhibits the most severe risk profile due to the high consequences of toxic releases, particularly under pressure-related failures. Compressed hydrogen is dominated by high-risk scenarios driven by extreme storage pressures, while liquefied hydrogen presents a mixed profile governed by the interaction between cryogenic temperature control and pressure regulation. By providing a comparative and scalable risk-assessment framework, the Methodology to Alternative-fuels Hazardous Identification (MAHI) supports informed decision-making in early design phases and complements existing regulatory safety analyses, contributing to a safer energy transition in maritime transport. Full article

Capsizing, sinking, and flooding accidents occurring in the coastal waters of the Republic of Korea constitute a persistent marine safety concern, accounting for approximately 17% of total fatalities associated with marine accidents. Previous statistical analyses of accident causation have identified key contributing factors such as adverse weather conditions, improper cargo loading, and deficiencies in vessel maintenance; however, the complex interdependencies among these factors have not been sufficiently quantified. To address this limitation, this study proposes a fuzzy Bayesian network (FBN) model to systematically evaluate and quantify the risk factors associated with capsizing, sinking, and flooding accidents. A total of 164 adjudicated marine accident cases that occurred in Korean coastal waters over a 10-year period (2015–2024) were analyzed (data collection cutoff: 31 December 2024) to estimate prior probabilities for six major causal categories. Conditional probability tables (CPTs) were derived through a structured Delphi survey conducted with marine safety experts possessing more than 10 years of professional experience. To mitigate the subjectivity inherent in expert judgment, triangular fuzzy numbers (TFNs) and centroid-based defuzzification were applied. Sensitivity analysis identified sea state (SI = 0.0155) and cargo loading condition (SI = 0.0125) as the two most influential factors affecting the probability of capsizing. Scenario analysis further revealed that when adverse weather conditions and improper cargo loading occur simultaneously, the probability of capsizing increases to 39.3%, representing a 5.3 percentage point increase compared to the baseline. In addition, the model demonstrated a close agreement with observed accident outcome distributions, with a Kullback–Leibler (KL) divergence of 0.038, indicating differences within 1.3 percentage points across all outcome categories. The findings of this study provide practical implications for targeted marine safety interventions and the prioritization of regulatory measures in the coastal waters of the Republic of Korea. Full article

Existing research on iron smelting sites from the Han to Song Dynasties in southeastern Guangxi has focused on metallurgical technology analysis, but geographic information system-based analysis remains limited. To address this gap, this study examines spatial distribution, clustering patterns, and natural controls of iron smelting sites and clarifies the coupling relationship between spatial patterns and the evolution of bloomery iron smelting and cast iron smelting technology. This study examines 38 iron smelting sites using a geographic database that integrates kernel density estimation, Thiessen polygons, and geographic detectors to reveal spatial patterns and driving factors. Results show that: (1) two smelting technologies existed in the region (bloomery iron and cast iron); (2) sites exhibit a three-centre cluster, with the highest density in Pingnan County; (3) lithology was the primary controlling factor, followed by contour density, relief, elevation, and soil properties; (4) shaft furnaces existed in favourable geotechnical conditions and transport access; small-scale furnaces are controlled by ore availability, with additional cultural and safety influences. This study reveals the spatial heterogeneity and key factors of iron smelting sites in southeastern Guangxi, providing quantitative support for Lingnan metallurgical archaeology, human–environment relations, and dissemination of Maritime Silk Road technology. Full article

Accurately predicting traffic crash severity is critical for intelligent transportation systems, where outcomes emerge from the interaction of infrastructure, environment, traffic control, and human behavior. However, existing approaches face three key challenges: severe class imbalance, computational inefficiency, and limited support for system-level risk understanding. To address these issues, this study proposes a unified and system-aware framework integrating Conditional Tabular Generative Adversarial Network (CTGAN), Tabular Prior-data Fitted Network (TabPFN), and eXplainable Artificial Intelligence (XAI) methods for data augmentation, efficient prediction, and interpretable analysis. CTGAN enhances rare but critical crash states while preserving feature dependencies; TabPFN enables accurate multi-class prediction with limited dataset-specific tuning; and XAI methods quantify the influence of key factors and their interactions. Experiments on a real-world crash dataset from Boston show that the proposed framework achieves competitive predictive performance with less reliance on dataset-specific hyperparameter tuning, while also providing complementary interpretability results from multiple perspectives. The results further reveal that crash severity is jointly shaped by visibility, traffic control, roadside features, and temporal dynamics, highlighting the interconnected nature of risk within the transportation system. By integrating predictive modeling with complementary interpretability analysis, the framework provides a systems-oriented basis for examining how environmental, infrastructural, and temporal conditions jointly relate to crash severity in the studied urban crash data, while offering a methodological reference for broader safety applications that require further validation. Full article

Ship collision risk is a critical issue in maritime traffic safety regulation, as it directly affects the safety, efficiency, and sustainability of maritime transportation. It depends not only on the current encounter geometry among ships, but is also closely related to the ship trajectory distribution structure and the traffic state. Existing studies have mostly identified collision risk based on collision avoidance parameters. Although such methods can characterize explicit collision risks, they remain insufficient in identifying the additional risks induced by trajectory densification, uncovering the potential risks reflected by frequent trajectory intersection and change, and representing the structural collision risks of regional traffic. To address these limitations, this study proposes a trajectory analysis-based regional collision risk model within the framework of the radial distribution function. First, the mapping relationships between collision risk and three aspects, namely trajectory density, trajectory conflict, and trajectory abruptness, are established, which are respectively characterized by trajectory density and aggregation, trajectory intersections and time differences, and trajectory alterations and fluctuations. Then, the ship traffic system is transformed into a particle system, and two-dimensional radial distribution feature planes for the above three aspects are constructed to identify the risk level of a region from different dimensions. Finally, a three-dimensional fusion space is further developed to achieve a comprehensive quantification of collision risk in a specified water area. Experiments were conducted using one week of daytime and nighttime Automatic Identification System (AIS) data from the Bohai Strait. The proposed model showed a strong temporal correlation with AIS record-based high-risk patterns (R = 0.866, p = 0.01), and, compared with a regional collision risk model based on traditional collision avoidance parameters, exhibited 20–50% higher sensitivity in identifying additional and potential risks caused by dense, intersecting, and abrupt trajectory patterns. The proposed model can provide methodological support for maritime authorities in collision risk monitoring of key waters, precise allocation of regulatory resources, and proactive safety regulation, thereby contributing to safer and more sustainable maritime transportation. Full article