Abstract This paper aims at providing a state-of-the-art review on the applications of particle methods in hydrodynamics-related problems in ocean and coastal engineering. The problems are placed into three categories according to their physical characteristics, namely, wave hydrodynamics and corresponding mass (air, oil, etc.) transport, wave-structure interaction, and wave-current-sediment interaction. For the first category, particle-based simulations of wave generation, propagation, breaking, as well as the associated turbulence production and dissipation, air entrainment, and mass transport, are reviewed. For wave-structure interaction, extensive structural types are considered that include fixed and moving (floating) structures, rigid and deformable structures, impermeable and porous structures, etc. For the third category, the latest advances of particle methods in wave/current interaction with sediments, i.e., sediment transport and coastal morphological changes, are outlined. This article also reviews the latest developments of particle methods with respect to enhancement of numerical stability, accuracy, efficiency and consistency in order to handle the multi-physics and multi-scale problems emerging from coastal and ocean engineering practices. Finally, the future perspectives of extending particle methods to a wider range of ocean and coastal engineering applications are highlighted.
This study investigates whether English proficiency moderates core Technology Acceptance Model (TAM) pathways in the context of AI-enhanced English MOOCs for vocational students. Drawing on an extended TAM that links Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Behavioral Intention (BI), and Perceived Learning Outcomes (PLO), we surveyed 516 learners from a provincial AI-powered MOOC. Confirmatory factor analysis confirmed strong measurement properties (all factor loadings > 0.74, AVE > 0.57, CR > 0.80). Structural analysis revealed robust direct effects: PEOU → PU (β = 0.756), PU → BI (β = 0.696), and BI → PLO (β = 0.814). Hierarchical regression showed no significant moderating by English proficiency on any TAM path, though a small positive direct effect on BI was observed (β = 0.064, p = 0.042). Results suggest that well-designed AI personalization can mitigate language-related barriers, allowing core TAM mechanisms to operate consistently across proficiency levels. The findings highlight the potential of adaptive AI tools to foster equitable engagement in vocational language learning. Future research should employ multi-item or objective proficiency measures and incorporate actual usage data to further validate these insights.
Hyperspectral image (HSI) has more spectral information than conventional images, which helps to distinguish targets in a complex scene more accurately. However, HSI typically has a low spatial resolution, which limits their application scenarios. To achieve high-resolution HSI, we propose a spectral and spatial multiscale coupling fusion model (SSMSFuse) for hyperspectral and multispectral image (MSI). SSMSFuse couples the spatial information of MSI and the spectral information of HSI at multiscales by means of a two-branch network structure, thus obtaining the fused images with high spatial and spectral resolution. SSMSFuse consists of two branches, namely the spatial embedding network (Spa-Net) and the spectral embedding network (Spe-Net). Spa-Net is constructed using a multiscale convolutional neural network to better mine multilevel spatial features from MSI. Spe-Net is constructed using self-attention, which can model the long-distance spectral dependencies of HSI to better extract spectral information from HSI. Finally, to achieve interactive coupling of dual-branch information, we designed a spatial–spectral guidance fusion block to fuse features at different scales to avoid loss of spatial and spectral details. Experiments are carried out on four public datasets, and the results show that the proposed method can effectively improve the objective indicators of the fusion results, such as the peak signal to noise ratio, which is increased by 1.36%, and the root mean square error, which is increased by 9.72% on the CAVE dataset, and satisfactory subjective results are also obtained.
As a candidate cladding material for fourth-generation nuclear energy systems, the microstructure and properties of oxide dispersion strengthened 316L (ODS-316L) steel are significantly affected by heat treatment (HT) processes. This paper highlights the influence mechanism of HT temperature on the microstructural evolution and corrosion resistance of ODS-316L alloys prepared by laser powder bed fusion (LPBF) technology. The results show that HT can significantly regulate the microstructure of ODS-316L alloys. After heat treatment, the ODS-316L alloy consists of a single austenitic phase, indicating no phase transformation occurs before and after heat treatment. With the increase of HT temperature, the fish-scale molten pool morphology characteristic of the as-fabricated ODS-316L alloy disappears, and recrystallized grains coarsen. When the HT temperature reaches 1150 °C, the grain size reaches a maximum of 43.1 μm, the proportion of low-angle grain boundaries (LAGBs) is the highest at 76.7 %, and the Kernel Average Misorientation (KAM) value drops to a minimum of 0.28°. At the HT temperature of 1150 °C, the alloy exhibits excellent corrosion resistance: the self-corrosion potential (Ecorr) reaches a maximum of −0.255 V, the self-corrosion current density (Icorr) decreases to a minimum of 7.797 × 10−7 A/cm2, the pitting potential (Ep) reaches a maximum of 0.632 V, and the passivation interval reaches a maximum of 0.659 V. Moreover, two types of nano-oxide particles, Y–Si–O and Si–O, are uniformly distributed in the 316L matrix heat-treated at 1150 °C, which helps to reduce the self-corrosion current density, thereby improving its corrosion resistance. Furthermore, the correlation between nano-oxides and corrosion resistance is elaborate. This study provides a theoretical basis and reference value for further optimizing the microstructure and corrosion resistance of nuclear-grade ODS-316L alloy components.
Spotted scat (Scatophagus argus), an economically significant marine fish species, is widely distributed along the coasts of Guangxi, Taiwan and Guangdong Province in China. It exhibits sexual growth dimorphism, with females exhibiting a faster growth rate and larger size than males. The critical metabolic organ liver plays an important role in regulating body growth and reproduction processes, which that are influenced by estrogens. In this study, the molecular mechanisms of 17-estradiol (E2) regulates body growth and reproduction in livers were investigated by transcriptome sequencing, after fed 2-year-old spotted scat with E2-containing baits for 30 days. Transcriptome analysis identified 218, 1949, 548, and 1718 differentially expressed genes (DEGs) (FDR < 0.05 and |log2(FC)| ≥ 1) in livers of spotted scat across the Ctrl-ML vs. Ctrl-FL, E2-ML vs. E2-FL, E2-ML vs. Ctrl-ML, and E2-FL vs. Ctrl-FL groups, respectively. The amino sugar and nucleotide sugar metabolism pathway was significantly influenced in females by E2. While fatty acid metabolism, arginine and proline metabolism were significantly enriched in males. Several genes metabolic (acsl5, gpx1b, and nots), growth and reproduction-related genes (igfs, vtgs, erα, and zps) were responded to E2 in a gender-specific manner. Thus, the livers of females appeared to be more sensitive to E2 feeding than males, and the responsive mechanisms of spotted scat livers to E2 were gender-specific. These findings will provide a foundation for understanding the molecular regulation of estrogen in fish growth and reproduction within the liver, and will also offer theoretical evidence for the artificial cultivation of spotted scat.
Introduction: The study presents an integrated system comprising a central platform and four wave-energy converters, with a focus on investigating their coupled motions induced by ocean waves. The interaction between the buoys and the central platform is achieved through the implementation of spring components. The power take-off system is simulated by incorporating damping coefficients and stiffness into these spring components, enabling a detailed analysis of the energy conversion of such system.Methods: Numerical simulations based on the continuity equation and the Reynolds-Averaged Navier-Stokes (RANS) equations, coupled with the realizable k−ε turbulence model, are conducted. The two-phase flow model employs the Volume of Fluid (VOF) method to accurately capture free surface elevations. Additionally, frequency-domain predictions, based on the linearized velocity potential flow theory, are provided for a single central platform and buoy for comparative purposes.Results: Detailed results regarding the effects of wave frequency and the damping coefficient of the power take-off system are presented.Discussion: The results reveal that while both the platform’s motion and the relative motions between buoys and the platform are suppressed, the absolute motion of buoys varies depending on their respective locations within the system and ocean waves. This variation is deeply influenced by the interaction between incident, reflected and diffracted waves within the system.
Marine diesel engines work in an environment with multiple excitation sources. Effective feature extraction and fault diagnosis of diesel engine vibration signals have become a hot research topic. Time-domain synchronous averaging (TSA) can effectively handle vibration signals. However, the key phase signal required for TSA is difficult to obtain. During signal processing, it can result in the loss of information on fault features. In addition, frequency multiplication signal waveforms are mixed. To address this problem, a multi-scale time-domain averaging decomposition (MTAD) method is proposed and combined with signal-to-image conversion and a convolutional neural network (CNN), to perform fault diagnosis on a marine diesel engine. Firstly, the vibration signals are decomposed by MTAD. The MTAD method does not require the acquisition of the key phase signal and can effectively overcome signal aliasing. Secondly, the decomposed signal components are converted into 2-D images by signal-to-image conversion. Finally, the 2-D images are input into the CNN for adaptive feature extraction and fault diagnosis. Through experiments, it is verified that the proposed method has certain noise immunity and superiority in marine diesel engine fault diagnosis.
Aimed at the problem of low efficiency of ship pipeline design, an optimization method of pipeline layout is proposed. An optimization mathematical model is established by comprehensively considering the engineering background of safety, economy, coordination and operability, and the defects of ant colony optimization algorithm in dealing with mixed pipeline layout conditions are improved. A spatial state transition strategy for optimizing feasible solution search, a pheromone diffusion mechanism for improving pheromone inspiration effect and accelerating algorithm convergence are proposed, and a multi-ant colony co-evolution mechanism is designed for mixed pipeline layout conditions. Based on the secondary development technology, the application of this method in the third-party design software is realized, and verified by a nuclear primary pipeline layout project. The results show that the pheromone Gaussian diffusion multi ant colony optimization (PG-MACO) algorithm has a better performance and layout effect than the traditional ant colony algorithm. The routing efficiency is improved by 58.38%, the convergence algebra is shortened by 43.24%, the pipeline length is shortened by 33.88%, and the number of pipeline bends is reduced by 41.67%, which verifies the effectiveness and engineering practicability of the proposed method.
Engineering (General). Civil engineering (General), Chemical engineering
Abstract The hydropower sector is currently experiencing several technological developments. New technologies and sustainable practices are emerging to make hydropower more flexible and eco-friendly. Novel materials have also been recently developed to increase performance, durability, and reliability; however, no systematic discussions can be found in the literature. Therefore, in this paper, novel materials for hydropower applications are presented, and their performance, advantages, and limitations are discussed. For example, composites can reduce the weight of steel equipment by 50% to 80%, polymers and superhydrophobic materials can reduce head losses by 4% to 20%, and novel bearing materials can reduce bearing wear by 6%. These improvements determine higher efficiencies, longer life span, waste reduction, and maintenance needs, although the initial cost of some materials is not yet competitive with respect to the costs of traditional materials. The novel materials are described here based on the following categories: novel materials for turbines, dams and waterways, bearings, seals, and ocean hydropower.
Ngoc-Thanh Nguyen, Rogardt Heldal, Keila Lima
et al.
The ocean is vital for humankind but may cause catastrophes when unhealthy. Although there have been efforts to build ocean monitoring systems, the understanding of the underwater environment is limited due to the cost and challenges of obtaining real-time marine data. One potential solution is to build stationary ocean observation systems based on wireless communication due to its affordable cost. In this study, we divide these systems into three components: 1) underwater data acquisition; 2) network communication; and 3) data management. We investigate the engineering challenges associated with each component, the causes, and how they relate. The literature has not discussed the technical issues of building stationary smart ocean monitoring systems entirely based on wireless communication yet. This article fills that research gap by conducting semi-structured interviews with 17 experts knowledgeable about underwater sensors, underwater acoustic communication, offshore network communication, and underwater data usage. The identified challenges are compared with the literature to assess whether our findings are novel or are a confirmation of what have been already found in prior publications. The Internet of Things (IoT) used in smart city platforms is quite advanced, but the Internet of Underwater Things (IoUT) employed in smart ocean monitoring systems has several unresolved issues; although IoT is viewed as a foundation for IoUT. Therefore, we compare fundamental differences between the technologies used in the smart city and the smart ocean domains, explaining why some of our identified challenges are unique in the marine context.
The goal of achieving carbon neutrality in the next 30-40 years is approaching worldwide consensus and requires coordinated efforts to combat the increasing threat of climate change. Two main sets of actions have been proposed to address this grand goal. One is to reduce anthropogenic CO2 emissions to the atmosphere, and the other is to increase carbon sinks or negative emissions, i.e., removing CO2 from the atmosphere. Here we advocate eco-engineering approaches for ocean negative carbon emission (ONCE), aiming to enhance carbon sinks in the marine environment. An international program is being established to promote coordinated efforts in developing ONCE-relevant strategies and methodologies, taking into consideration ecological/biogeochemical processes and mechanisms related to different forms of carbon (inorganic/organic, biotic/abiotic, particulate/dissolved) for sequestration. We focus on marine ecosystem-based approaches and pay special attention to mechanisms that require transformative research, including those elucidating interactions between the biological pump (BP), the microbial carbon pump (MCP), and microbially induced carbonate precipitation (MICP). Eutrophic estuaries, hypoxic and anoxic waters, coral reef ecosystems, as well as aquaculture areas are particularly considered in the context of efforts to increase their capacity as carbon sinks. ONCE approaches are thus expected to be beneficial for both carbon sequestration and alleviation of environmental stresses.
Chang-Yang Hsieh, Shih-Yen Huang, Yu-Ren Chu
et al.
The presence of a second phase in the Mg–8Al–4Ca (at. %) alloy plays a significant role on both its corrosion behavior and the chemical conversion coating processes. Using scanning Kelvin probe force microscopy (SKPFM), a lower Volta-potential of the second phase present on the surface has been measured. The β-Al-Ca phase has a higher electrochemical activity than the α-Mg matrix and may act as the micro-galvanic anode in a local electrochemical corrosion process. Transmission electron microscopy (TEM) examinations reveal that the β-Al-Ca phase is more susceptible to corrosion than the α-Mg matrix in an aqueous solution, and its higher activity and higher corrosion rate accelerate the hydrogen evolution rate on the α-Mg matrix in the cerium (Ce) conversion coating process. It's also been discovered that by immersing the bare Mg–Al–Ca alloy in deionized (DI) water, the β-Al-Ca phase, exposed on the surface, can be dissolved and converted in situ into aluminum hydroxide (Al(OH)3), and the Ce conversion coating can be deposited via replacement reactions in the subsequent conversion coating process. A thicker Ce coating with smaller blisters has then been produced on the DI-treated Ce-coated Mg specimen; it indeed improves the corrosion resistance.