Search Results (620)

The anisotropic mechanical properties of selective laser melting (SLM)-processed Ti-6Al-4V (TC4) alloy hinder its deployment in polar marine equipment. This study systematically probes the relationships between laser scanning strategies (unidirectional vs. 67°-rotated scanning between layers), notch orientation (governing loading direction), and cryogenic impact energy of SLM-TC4. Charpy impact tests from −60 °C to 20 °C were performed on V-notched specimens fabricated with distinct scanning strategies and notch orientations (top/side surfaces). The analysis of impact energy data and macro/micro-fractography demonstrates that impact energy declines markedly with decreasing temperature, showing a 25–35% reduction at −60 °C versus 20 °C while exhibiting enhanced data consistency under cryogenic conditions. Notably, specimens fabricated with 67°-rotated scanning between layers achieve higher impact toughness than unidirectionally scanned equivalents. Moreover, for identical scanning strategies, side-notched specimens consistently outperform top-notched specimens, evidencing superior interfacial bonding strength between deposited layers relative to bonding within individual layers. Within individual layers, toughness normal to the laser scan path exceeds that parallel to the path. However, controlling ductile-to-brittle transition behavior and precluding brittle failure are imperative for SLM-TC4 components in polar cryogenic service. This work delivers essential quantitative benchmarks and experimental validation for optimizing SLM processing in critical polar vessel components. Full article

Addressing IMO 2020 compliance, this study investigates marine fuel oil production from hydrotreated residues, focusing on mitigating excessive total sediment potential (TSP) caused by over-hydrotreatment. This study systematically investigates the impact of blending ratios of Fluid Catalytic Cracking (FCC) slurry oil with Residue Desulfurization (RDS) heavy oil on TSP, colloidal stability, and asphaltene structure evolution. Techniques such as XRD, SEM, and XPS were employed to analyze the structural changes in asphaltenes during the TSP exceeding process. The results indicate that as the FCC slurry oil blending ratio increases, TSP in the blended oil initially rises and then decreases. The peak TSP value of 0.41% occurs at a 10% FCC slurry oil blending ratio, primarily due to high-saturation hydrocarbons in RDS heavy oil disrupting the colloidal stability of asphaltenes in FCC slurry oil. When the blending ratio reaches 25%, TSP significantly decreases to 0.09%, attributed to the solubilizing effect of high aromatic compounds in the FCC slurry oil on the asphaltenes. The ω(Asp)/ω(Res) ratio mirrors the TSP trend, and the colloidal solubilizing capacity of asphaltenes increases with the blending ratio. Asphaltenes in RDS heavy oil exhibit a spherical structure, whereas those in FCC slurry oil show a layered structure. The precipitated asphaltenes in the blends primarily result from the aggregation of asphaltenes in FCC slurry oil, with heteroatoms (N, S, O) mainly originating from RDS heavy oil asphaltenes. During the early stage of blending, TSP formation is dominated by FCC slurry oil asphaltenes, but increasing the aromatic content in the system can significantly reduce TSP. This work provides theoretical and technical support for optimizing marine fuel blending processes in petrochemical enterprises. Full article

The estimation of accurate and precise Satellite-Derived Bathymetries (SDBs) is important in marine and coastal applications for a better understanding of the ecosystems and science-based decision-making. Despite the advancements in related Machine Learning (ML) studies, quantifying the anticipated bias per pixel in the SDBs remains a significant challenge. This study aims to address this knowledge gap by developing a spatially explicit uncertainty index of a ML-derived SDB, capable of providing a quantifiable anticipation for biases of 0.5, 1, and 2 m. In addition, we explore the usage of this index for model optimization via the exclusion of training points of high or moderate uncertainty via a six-fold iteration loop. The developed methodology is applied across the national coastal extent of Belize in Central America (~7017 km²) and utilizes remote sensing data from the European Space Agency’s twin satellite system Sentinel-2 and Planet’s NICFI PlanetScope. In total, 876 Sentinel-2 images, nine NICFI six-month basemaps and 28 monthly PlanetScope mosaics are processed in this study. The training dataset is based on NASA’s system Ice, Cloud and Elevation Satellite (ICESat-2), while the validation data are in situ measurements collected with scientific equipment (e.g., multibeam sonar) and were provided by the National Oceanography Centre, UK. According to our results, the presented approach is able to provide a pixel-based (i.e., spatially explicit) uncertainty index for a specific prediction bias and integrate it to refine the SDB. It should be noted that the efficiency of the optimization of the SDBs as well as the correlations of the proposed uncertainty index with the absolute prediction error and the true depth are low. Nevertheless, spatially explicit uncertainty information produced by a ML-related SDB provides substantial insight to advance coastal ecosystem monitoring thanks to its capability to showcase the difficulty of the model to provide a prediction. Such spatially explicit uncertainty products can also aid the communication of coastal aquatic products with decision makers and provide potential improvements in SDB modeling. Full article

To reduce welding deformation during the automated welding of thick-walled pipes in shipbuilding and thereby improve welding quality, a segmented multi-layer multi-pass welding sequence optimization and process improvement strategy is proposed. Firstly, based on a welding model for thick-walled pipes, a multi-layer multi-pass welding trajectory equation is established. A double-ellipsoidal moving heat source is adopted to design a circular multi-layer multi-pass double-ellipsoidal heat source model. Secondly, three circular pipe workpieces with different wall thicknesses are selected, and four segmented welding sequences are simulated using welding finite element analysis (FEA). Finally, based on the optimal segmented welding sequence, the welding process is improved, and optimal welding process parameters are determined based on deformation and residual stress analysis. The results of the segmented multi-layer multi-pass welding sequence optimization show that the skip-symmetric welding method yields the best results for thick-walled circular pipes. Compared to other welding sequences, it reduces welding deformation by an average of 6.50% and welding stress by an average of 5.37%. In addition, process improvement tests under the optimal welding sequence indicate that the best welding quality is achieved under the following conditions: for 10 mm thick pipes—200 A current, 24 V voltage, and 11.5 mm/s welding speed; for 15 mm thick pipes—215 A, 24.6 V, and 10 mm/s; and for 20 mm thick pipes—225 A, 25 V, and 11 mm/s. Full article

Microplastic pollution has become a critical environmental issue, affecting terrestrial, freshwater, and marine ecosystems. These pollutants, originating from plastic degradation and primary sources, can act as carriers for harmful substances such as heavy metals and organic contaminants. While mitigation efforts are still in development, biological systems, particularly Black Soldier Fly Larvae (BSFL), have shown promise in organic waste management and pollutant bioaccumulation. Recent research explores the potential of BSFL to interact with and degrade microplastic particles, although the mechanisms remain underexplored. The role of microbial communities in facilitating microplastic degradation is of growing interest, as well as the impact of microplastic ingestion on the larvae’s efficiency in organic waste breakdown. However, experimental inconsistencies and environmental variations continue to delay progress, underscoring the need for further study to optimize bioremediation strategies and assess long-term ecological effects. This systematic review aims to explore the interactions between microplastics and BSFL, focusing on their potential as a bioremediation agent. It investigates the larvae’s ability to reduce microplastic pollution through bioaccumulation and degradation processes. Full article

This study investigates the microstructure evolution and mechanical properties of DSS2209 duplex stainless steel fabricated via cold metal transfer wire arc additive manufacturing (CMT-WAAM). The as-deposited thin-wall components exhibit significant microstructural heterogeneity along the build height due to thermal history variations. Optical microscopy, SEM-EDS, and EBSD analyses reveal distinct phase distributions: the bottom region features elongated blocky austenite with Widmanstätten austenite (WA) due to rapid substrate-induced cooling; the middle region shows equiaxed blocky austenite with reduced grain boundary austenite (GBA) and WA, attributed to interlayer thermal cycling promoting recrystallization and grain refinement (average austenite grain size: 4.16 μm); and the top region displays coarse blocky austenite from slower cooling. Secondary austenite (γ₂) forms in interlayer remelted zones with Cr depletion, impacting pitting resistance. Mechanical testing demonstrates anisotropy; horizontal specimens exhibit higher strength (UTS: 610 MPa, YS: 408 MPa) due to layer-uniform microstructures, while vertical specimens show greater ductility (elongation) facilitated by columnar grains aligned with the build direction. Hardness ranges uniformly between 225–239 HV. The study correlates process-induced thermal gradients (e.g., cooling rates, interlayer cycling) with microstructural features (recrystallization fraction, grain size, phase morphology) and performance, providing insights for optimizing WAAM of large-scale duplex stainless steel components like marine propellers. Full article

Given the demand for sustainable food solutions in China and the underutilization of bovine by-products, this study aimed to optimize the marinating process of bovine liver, heart, and rumen while evaluating their storage stability. An orthogonal experimental design was employed to systematically optimize the marinating agent ratio and incorporate natural antioxidants to inhibit lipid oxidation and microbial spoilage. Results demonstrated that the optimized marinating formula, which included 0.3 g/kg rosemary extract, exhibited optimal antioxidant and antimicrobial effects. This strategy not only slowed product pH decline but also improved product yield and texture, and significantly reduced thiobarbituric acid reactive substances (TBARS) values and carbonyl content (p < 0.05), while maintaining favorable sensory scores and extending shelf life. The study indicates that targeted marinating technology holds potential for transforming bovine by-products into high-value-added food products, offering innovative solutions to address both economic and environmental challenges and establishing a technical foundation for efficient by-product utilization and industrial upgrading. Full article

T-bar penetrometers have been widely used to measure strength parameters of marine clay in laboratory and in situ tests. However, using the deep resistance factor derived from full-flow conditions to evaluate the undrained shear strength of shallow clay layers may lead to significant underestimation. Furthermore, the deep resistance factor is inherently influenced by the strain-softening behavior of clay rather than maintaining the constant value (typically 10.5) as conventionally assumed in practice. To address this issue, large-deformation finite element (LDFE) simulations incorporating an advanced exponential strain-softening constitutive model were performed to replicate the full T-bar penetration process—from shallow embedment to deeper depths below the mudline. A series of parametric studies were conducted to examine the influence of key parameters on the resistance factor and the associated failure mechanisms during penetration. Based on numerical results, empirical formulas were derived to predict critical penetration depths for both trapped cavity formation and full-flow mechanism initiation. For penetration depths shallower than the full-flow depth, an expression for the softening correction factor was developed to calibrate the shallow resistance factor. Finally, combined with global optimization algorithms, a computer-aided back-analysis procedure was established to estimate strain-softening parameters using resistance-penetration curves from initial penetration tests in marine clay. The reliability of the back-analysis procedure was validated through extensive comparisons with a series of numerical simulation results. This procedure can be applied to the interpretation of T-bar in situ test results in soft marine clay, enabling the evaluation of its strain-softening behavior. Full article

Hydrate-based CO₂ sequestration is considered one of the most promising methods in the field of carbon capture, utilization, and storage. The abundant fractured environments in marine sediments provide an ideal setting for the sequestration of CO₂ hydrate. Investigating the kinetics and morphological characteristics of CO₂ hydrate formation within fractures is a critical prerequisite for achieving efficient and safe CO₂ sequestration using hydrate technology in subsea environments. Based on the aforementioned considerations, the kinetic experiments on the formation, dissociation, and reformation of CO₂ hydrates were conducted using a high-pressure visualization experimental system in this study. The kinetic behaviors and morphological characteristics of CO₂ hydrates within sandstone fractures were comprehensively investigated. Particular emphasis was placed on analyzing the effects of fracture width, type, and surface roughness on the processes of hydrate formation, dissociation, and reformation. The experimental results indicate the following: (1) At a formation pressure of 2.9 MPa, the 10 mm width fracture exhibited the shortest induction time, the longest formation duration, and the highest hydrate yield (approximately 0.52 mol) compared to the other two fracture widths. The formed CO₂ hydrates exhibited a smooth, thin-walled morphology. (2) In X-type fractures, the formation of CO₂ hydrates was characterized by concurrent induction and dissolution processes. Compared to I-type fractures, the hydrate formation process in X-type fractures exhibited shorter formation durations and generally lower hydrate yields. (3) An increase in fracture roughness enhances the number of nucleation sites for the formation of hydrates. In both fracture types (I-type and X-type), the induction time for CO₂ hydrate formation was nearly negligible. However, a significant difference in the trend of formation duration was observed under varying roughness conditions. (4) Hydrate dissociation follows a diffusion-controlled mechanism, progressing from the fracture walls towards the interior. The maximum gas production was achieved in the 10 mm-width fracture, reaching 0.24 mol, indicating optimal heat and mass transfer conditions under this configuration. (5) During the reformation process, the induction time was significantly shortened due to the “memory effect.” However, the hydrate yield after the reformation process remained consistently lower than that of the first formation, which is primarily attributed to the high solubility of CO₂ in the aqueous phase. Full article

Polytetrafluoroethylene (PTFE) is one of the alternative materials suitable for seawater-lubricated bearings, favored for its excellent corrosion resistance and good self-lubricating properties. As marine equipment develops towards higher load, higher reliability, and longer service life, more stringent requirements are imposed on the wear resistance of bearing materials. However, traditional PTFE materials struggle to meet the performance requirements for long-term stable operation in modern marine environments. To improve the wear resistance of PTFE, this study used alumina (Al₂O₃) particles with three different particle sizes (50 nm, 3 μm, and 80 μm) as fillers and prepared Al₂O₃/PTFE composites via the cold pressing and sintering process. Tribological performance tests were conducted using a ball-on-disk reciprocating friction and wear tester, with Cr12 steel balls as counterparts, under an artificial seawater lubrication environment, applying a normal load of 10 N for 40 min. The microstructure and wear scar morphology were characterized by scanning electron microscopy (SEM), and mechanical properties were measured using a Shore hardness tester. A systematic study was carried out on the microstructure, mechanical properties, friction coefficient, wear rate, and limiting PV value of the composites. The results show that the particle size of Al₂O₃ particles significantly affects the mechanical properties, friction coefficient, wear rate, and limiting PV value of the composites. The 50 nm Al₂O₃/PTFE formed a uniformly spread friction film and transfer film during the friction process, which has better friction and wear reduction performance and load bearing capacity. The 80 μm Al₂O₃ group exhibited poor friction properties despite higher hardness. The nanoscale Al₂O₃ filler was superior in improving the wear resistance, stabilizing the coefficient of friction, and prolonging the service life of the material, and demonstrated good seawater lubrication bearing suitability. This study provides theoretical support and an experimental basis for the design optimization and engineering application of PTFE-based composites in harsh marine environments. Full article

With the increased power density of internal combustion engines (ICE) and growing demands for lightweight design, the connecting rod big-end bearings are subjected to significant alternating loads. Consequently, the interference–fit interfaces become susceptible to fretting damage, which can markedly shorten engine service life and impair reliability. In the present study, the effects of the big end manufacturing process, bolt preload, and bearing bush interference fit are considered to develop a coupled lubrication–dynamic model of the connecting rod big-end bearing. This model investigates the fretting damage issue in the bearing bush of a marine diesel engine’s connecting rod big end. The results indicate that the relatively low stiffness of the big end is the primary cause of bearing bush fretting damage. Interference fit markedly affects fretting wear on the bush back, whereas the influence of bolt preload is secondary; nevertheless, a decrease in either parameter enlarges the fretting distance. Based on these findings, an optimized design scheme is proposed. Full article

Shallow foundations are widely used in both terrestrial and marine environments, supporting critical structures such as buildings, offshore wind turbines, subsea platforms, and infrastructure in coastal zones, including piers, seawalls, and coastal defense systems. Accurately determining the soil bearing capacity for shallow foundations presents a significant challenge, as it necessitates considerable resources in terms of materials and testing equipment, as well as a substantial amount of time to perform the necessary evaluations. Consequently, our research was designed to approximate the forecasting of soil bearing capacity for shallow foundations using machine learning algorithms. In our research, four ensemble machine learning algorithms were employed for the prediction process, benefiting from previous experimental tests. Those four models were AdaBoost, Extreme Gradient Boosting (XGBoost), Gradient Boosting Regression Trees (GBRTs), and Light Gradient Boosting Machine (LightGBM). To enhance the model’s efficacy and identify the optimal hyperparameters, grid search was conducted in conjunction with k-fold cross-validation for each model. The models were evaluated using the R² value, MAE, and RMSE. After evaluation, the R² values were between 0.817 and 0.849, where the GBRT model predicted more accurately than other models in training, testing, and combined datasets. Moreover, variable importance was analyzed to check which parameter is more important. Foundation width was the most important parameter affecting the shallow foundation bearing capacity. The findings obtained from the refined machine learning approach were compared with the well-known empirical and modern machine learning equations. In the end, the study designed a web application that helps geotechnical engineers from all over the world determine the ultimate bearing capacity of shallow foundations. Full article

This review is focused on glass fibers and natural fibers, exploring their applications in vehicles and buildings and emphasizing their significance in promoting sustainability and enhancing performance across various industries. Glass fibers, or fiberglass, are lightweight, have high-strength (3000–4500 MPa) and a Young’s modulus range of 70–85 GPa, and are widely used in automotive, aerospace, construction, and marine applications due to their excellent mechanical properties, thermal conductivity of ~0.045 W/m·K, and resistance to fire and corrosion. On the other hand, natural fibers, derived from plants and animals, are increasingly recognized for their environmental benefits and potential in sustainable construction, offering advantages such as biodegradability, lower carbon footprints, and reduced energy consumption, with a sound absorption coefficient (SAC) range of 0.7–0.8 at frequencies above 2000 Hz and thermal conductivity range of 0.07–0.09 W/m·K. Notably, the integration of these materials in construction and automotive sectors reflects a growing trend towards sustainable practices, driven by the need to mitigate carbon emissions associated with traditional building materials and enhance fuel efficiency, as seen in hybrid composites achieving 44.9 dB acoustic insulation at 10,000 Hz and a thermal conductivity range of 0.05–0.06 W/m·K in applications such as the BMW i3 door panels. Natural fibers contribute to reducing reliance on fossil fuels, supporting a circular economy through the recycling of agricultural waste, while glass fibers are instrumental in creating lightweight composites for improved vehicle performance and structural integrity. However, both materials face distinct challenges. Glass fibers, while offering superior strength, are vulnerable to chemical degradation and can pose recycling difficulties due to the complex processes involved. On the other hand, natural fibers may experience moisture absorption, affecting their durability and mechanical properties, necessitating innovations to enhance their application in demanding environments. The ongoing research into optimizing the performance of both materials highlights their relevance in future sustainable engineering practices. In summary, this review underscores the growing importance of glass and natural fibers in addressing modern environmental challenges while also improving product performance. As industries increasingly prioritize sustainability, these materials are poised to play crucial roles in shaping the future of construction and transportation, driving innovations that align with ecological goals and consumer expectations. Full article

This study presents a novel environmentally-friendly process for the selective extraction and enrichment of DHA/EPA-containing phospholipids (PL-DHA/EPA) from krill oil. The methodology leverages differential crystallization behavior between phospholipids and triacylglycerols in ethanolic solutions, exploiting their distinct freezing point thresholds to achieve precise fractionation. Response surface methodology optimization identified optimal extraction parameters: liquid-to-material ratio of 6:1 (v/w), freezing temperature of −20 °C, freezing duration of 25 h, and rotary evaporation temperature of 45 °C, yielding a final product with 39.40% PL-DHA/EPA content. Principal component analysis revealed substantial overlap in confidence ellipses among extraction methodologies, indicating effective preservation of core phospholipid signatures from the parent krill oil while maintaining critical structural characteristics and molecular species distribution. Comprehensive analysis of phospholipid fractions and heatmap analysis revealed distinctive molecular profiles compared to conventional organic solvent extraction, with selective enrichment of EPA-containing phospholipids, particularly PC-EPA and PI-EPA species. The green extraction method demonstrated comparable oxidative stability to conventional approaches, with superior protection against secondary oxidation as evidenced by significantly lower anisidine values. This sustainable approach achieves effective phospholipid enrichment while substantially reducing environmental impact through elimination of halogenated solvents, addressing the critical need for environmentally conscious technologies in marine lipid processing with potential applications in nutraceutical and functional food industries. Full article

With the increasingly frequent exploitation and transportation of offshore oil, the threat of oil spill accidents to the marine ecological environment has become increasingly serious. It is urgent to develop efficient and reliable oil film monitoring technology. Based on the marine radar oil spill data, an innovative OAT-NGN hybrid strategy segmentation algorithm was proposed. By integrating the local feature learning ability of a Neural Gas Network (NGN) and the global search strategy of the Oat optimization algorithm (OAT), the proposed method effectively meets the challenges of traditional oil film segmentation methods in complex sea conditions. Firstly, the raw data of marine radar were preprocessed by using co-frequency interference and speckle noise suppression. Then, the OAT algorithm guided the updating of neural weights in the NGN on a global scale for the exploration of a more optimal solution space during the optimization process. Finally, the oil spill segmentation results were projected to the polar coordinate system through post-processing technology. The experimental results showed that this method effectively balanced the problem of false detection and missing detection. Compared with existing methods, OAT-NGN shown stronger adaptability in complex scenarios. In order to improve the segmentation performance, its innovative dynamic weight adjustment mechanism and spatial constraint design provide a new technical path. Full article