Search Results (1,935)

In coastal hot and humid regions, the steel pin of AC porcelain insulators often suffers from severe electrochemical corrosion due to surface contamination and moisture, leading to insulator string breakage. Contrary to the common belief that AC corrosion is negligible, this study reveals the significant role of the DC component in leakage currents and the synergy of this DC component with localized high current densities in accelerating corrosion, based on field investigations and experiments. Using a simulation model based on the Suwarno equivalent circuit, it is shown that non-linear contamination causes highly non-sinusoidal leakage currents, with total harmonic distortion up to 40% and a DC component of approximately 22%. To mitigate this, a conductive silicone rubber coating is proposed to block moisture and distribute leakage current evenly, keeping surface current density below the critical threshold of 100 A/m². Simulations indicate that a 2 mm thick coating with conductivity around 10⁻⁴ S/m effectively reduces current density to a safe level. Accelerated corrosion tests confirm that this conductive coating significantly suppresses pitting corrosion caused by high current densities, outperforming traditional insulating coatings. This study presents a practical and effective approach for protecting AC insulators in harsh environments, contributing to improved transmission line reliability in high-temperature and high-humidity regions. Full article

Polyimide composites represent a class of advanced materials with remarkable mechanical robustness and thermal stability, making them highly suitable for applications in extreme environments. Their unique ability to maintain performance under high temperatures and corrosive conditions, combined with a favorable strength-to-weight ratio, positions them as critical components in aerospace, electronics, and automotive systems. Several leading aerospace and electronics corporations have made significant investments in incorporating polyimide composites into their products, indicating the material’s transformative potential. This review paper provides an overview of mechanical and thermal behaviors of polyimide composites, summarizing recent developments and research trends. It examines the influence of various reinforcements, processing techniques, and composite architectures on material performance under mechanical loading and thermal stress. The paper synthesizes findings from experimental studies and modeling efforts to highlight the critical factors affecting strength, durability, and thermal stability. Discussion and recommendations regarding applications in aerospace, electronics, and other high-temperature environments are provided, emphasizing the challenges and opportunities presented by these advanced materials. This review adopts a broad scope to reflect the interdisciplinary nature of polyimide research. Due to gaps in literature, this work aims to provide a foundational overview that supports future, more specialized investigations. Full article

To quantitatively analyze the macroscopic diffusion process of chloride ions in existing concrete members, the Peridynamic Differential Operator (PDDO) was introduced to formulate a discrete format for Fick’s second law, and a simulation model was established and validated. Subsequently, the influence of specific or randomly distributed defects in the concrete is reflected by adjusting the coefficients in the model’s global matrix. Moreover, the complex geometry of concrete members is captured by employing a point set-based spatial discretization approach. The model also accommodates for the complex corrosion conditions encountered in practice by imposing different boundary conditions. These features allowed for the simulation and validation of chloride ion diffusion experiments on concrete under natural environmental conditions. The study further analyzed how factors such as defects, diffusion coefficients, boundary conditions, and the geometric shape of members influence the macroscopic diffusion process. The findings indicate that the numerical model based on the PDDO can effectively quantify the macroscopic diffusion of chloride ions in existing concrete members. It provides fundamental data for the durability maintenance of concrete infrastructures and potentially reduces their carbon footprint by preventing unnecessary rehabilitation or reconstruction. Full article

Environmental factors significantly affect the fatigue performance of weathering steel welded components in high-altitude, low-temperature corrosive environments. This study conducted multi-factor-coupled constant-amplitude fatigue tests on Q500qENH weathering steel V-groove welded joints and built an equivalent finite element model using test data to explore key influencing factors under multi-condition coupling. Results show that stress level most significantly affects fatigue performance, followed by corrosion duration, then ambient temperature, with influences decreasing in turn. Analyzing 18-day cyclic immersion corrosion morphology predicts 21-year outdoor corrosion in plateau regions, providing a reliable method for long-term exposure prediction. Finite element simulations confirm that low temperatures improve slightly corroded specimens’ fatigue performance by 20%, but damage accumulates before optimal service. This study offers key parameters for safe design of high-altitude weathering steel welded components. Full article

In this paper, the interfacial bonding properties between (110)_NiTi and (200)_TiN interfaces, as well as the adsorption capacity of Cl⁻ on the surfaces of (110)_NiTi and (200)_TiN, were investigated using the first-principles computational method based on density functional theory (DFT). Four types of interfacial models between (110)_NiTi and (200)_TiN were developed. It was found that the interfacial bonding energies of the four interface models are greater than zero, indicating stable interface bonding between (110)_NiTi and (200)_TiN. For comparison, model III (N of (200)_TiN is located at the bridge size between Ti and Ni in (110)_NiTi) has the largest Wad value of 9.773 J/m², which is attributed to stronger N-Ti bonding at the interface. Based on interface model III, an interfacial model of Cl⁻ at three different adsorption locations (top, bridge, and hole) on the (110)_NiTi and (200)_TiN surfaces, respectively, was constructed. The results reveal that the adsorption energies of Cl⁻ on the surface of (110)_NiTi are significantly less than those of the Cl⁻ on the surface of (200)_TiN. This suggests that (110)_NiTi is more likely to react with Cl⁻. Hence, the introduction of a TiN layer on the surface of NiTi alloy can effectively improve its corrosion resistance. Full article

Hastelloy X exhibits outstanding thermal fatigue resistance, making it a promising material for repairing 50CrVA landing gear via directed energy deposition (DED). However, the substantial differences in composition and thermophysical properties between 50CrVA and Hastelloy X pose challenges by affecting interfacial microstructure and surface quality. This study investigates the effect of DED process parameters (laser power p, powder feed rate f, scanning speed v, and overlap rate) on the dilution ratio (η), microscopic morphology, surface flatness (ζ), and porosity of Hastelloy X claddings on a 50CrVA substrate. An optimization methodology integrating thermal–flow coupled simulation models and orthogonal experiments is developed to fabricate high-quality claddings. Furthermore, the corrosion–wear performance of the claddings is evaluated. The results indicate that the η of a single track increases with higher p or lower f, while it first increases and then decreases with the increase in v. Ablation marks tend to occur at excessive p or insufficient f, while low v causes surface ripples. The ζ of a single layer initially improves and subsequently deteriorates with increasing overlap rate. Porosity is significantly influenced by p and f. The optimal p, f, v, and overlap rate are 1600 W, 2.4 g/min, 240 mm/min, and 55%, respectively. The wear resistance of the cladding is nearly identical to that of the substrate, while corrosion resistance is significantly improved. This work provides a theoretical foundation for high-performance repair of 50CrVA landing gear in aircraft. Full article

Calcium phosphate coatings are known for their osteoconductive prowess. In this work, calcium phosphate coatings were studied on a model biodegradable magnesium alloy of AZ31, primarily to provide improved corrosion protection and, more importantly, to confer in vitro cytocompatibility to the AZ31 alloy. Correspondingly, an aqueous-based approach was developed to deposit Sr²⁺-substituted calcium phosphates on micro-arc oxidized AZ31. Micro-arc oxidation was used mainly as a pretreatment technique due to improved homogeneity and adhesion strength in comparison to the coatings formed by the traditionally used alkaline and acidic pretreatment. Calcium phosphate coatings with up to 11.5 mol. % Sr were formed on micro-arc oxidized AZ31 substrates. Despite observation of greater than the intended 10 mol. % Sr to the calcium phosphate coatings as measured within the measurement error, biphasic mixtures of dicalcium phosphate dihydrate and poorly crystalline hydroxyapatite were formed. Micro-arc oxidation treatment, nevertheless, provided a slight improvement in corrosion protection compared to uncoated AZ31. However, much-improved corrosion protection was provided by the calcium phosphate coatings prepared either with or without Sr²⁺. The calcium phosphate coatings prepared with Sr²⁺ were also observed to support improved MC3T3-E1 murine pre-osteoblast cell proliferation compared to the calcium phosphate coated substrates prepared without Sr²⁺. Full article

Thin liquid film corrosion is a critical failure mechanism for the atmospheric environment and industrial infrastructure. This review systematically examines relevant methods and recent advances in characterizing and simulating this phenomenon. Various measurement methods for liquid film thickness, composition, and conductivity are investigated, with particular focus on the advantages of non-contact optical technology and X-ray fluorescence (XRF) in in situ monitoring and analysis. For corrosion simulation, the finite element method (FEM), cellular automaton (CA), and molecular dynamics (MD) are widely used. Their combination has synergistic potential in revealing essential corrosion mechanisms and establishing prediction models across scales. Full article

Asset maintenance management is critical in industries such as petrochemicals and oil and gas (O&G), where complex, interdependent systems heighten failure risks. Maintenance costs represent a significant portion of operational expenditures, emphasizing the need for effective risk-based strategies. A considerable gap exists in integrating uncertainty modelling into both criticality assessment and maintenance planning. Existing approaches often neglect combining expert-driven assessments with optimization models, limiting their applicability in real-world scenarios where cost-effective and risk-informed decision-making is crucial. Maintenance inefficiencies due to suboptimal asset selection result in substantial financial and safety-related consequences in asset-intensive industries. This study presents a framework integrating Reliability-Centered Maintenance (RCM) principles with fuzzy logic and decision-support methodologies to optimise maintenance portfolios for offshore O&G assets, particularly focusing on corrosion management. The framework evaluates asset criticality through comprehensive FMEA, employing MCDM and fuzzy logic to enhance maintenance planning and extend asset lifespan. A case study on offshore asset corrosion management demonstrates the framework’s effectiveness, selecting 60% of highly critical assets for maintenance, compared to 10% by current industry practices. This highlights the potential risk reduction and prevention of critical failures that might otherwise go unnoticed, providing actionable insights for asset integrity managers in the O&G sector. Full article

In order to ensure the earthquake safety of existing buildings, retrofitting applications come to the fore in terms of being fast and cost-effective. Among these applications, fiber-reinforced polymer (FRP) composites are widely preferred thanks to their advantages such as high strength, corrosion resistance, applicability without changing the cross-section and easy assembly. This study presents a data augmentation, modeling, and comparison-based approach to predict the fire resistance (FR) of FRP-strengthened reinforced concrete beams. The aim of this study was to explore the role of data augmentation in enhancing prediction accuracy and to find out which augmentation method provides the best prediction performance. The study utilizes an experimental dataset taken from the existing literature. The dataset contains inputs such as varying geometric dimensions and FRP-strengthening levels. Since the original dataset used in the study consisted of 49 rows, the data size was increased using augmentation methods to enhance accuracy in model training. In this study, Gaussian noise, Regression Mixup, SMOGN, Residual-based, Polynomial + Noise, PCA-based, Adversarial-like, Quantile-based, Feature Mixup, and Conditional Sampling data augmentation methods were applied to the original dataset. Using each of them, individual augmented datasets were generated. Each augmented dataset was firstly trained using eXtreme Gradient Boosting (XGBoost) with 10-fold cross-validation. After selecting the best-performing augmentation method (Adversarial-like) based on XGBoost results, the best-performing augmented dataset was later evaluated in HyperNetExplorer, a more advanced NAS tool that can find the best performing hyperparameter optimized ANN for the dataset. ANNs achieving R² = 0.99, MSE = 22.6 on the holdout set were discovered in this stage. This whole process is unique for the FR prediction of structural elements in terms of the data augmentation and training pipeline introduced in this study. Full article

In this study, polysaccharide xanthan gum (XG), used in the oil and gas industry as a thickening agent, was evaluated as an active anti-corrosion component against sweet corrosion at high temperatures and pressures in a saline environment for N80 carbon steel in the oil and gas industry. The evaluation involved measuring weight loss and conducting electrochemical assessments at 5 bar CO₂ partial pressure, different temperatures (e.g., 30 °C and 90 °C), and immersion times (up to 72 h). The electrochemical results indicated that XG effectively mitigated CO₂ corrosion at both low and high temperatures, demonstrating inhibition efficiencies of 70.10% at 30 °C and 61.41% at 90 °C using 1.0 g L⁻¹ of XG, after 24 h. The good inhibition efficiency observed even at high temperatures is likely due to the denaturation process that XG undergoes at high temperatures, where a rigid double-stranded helical structure transitions into two single-stranded, more flexible, worm-like macromolecular conformations. This increases the number and mobility of XG macromolecules available for adsorption on the metal surface. EIS measurements have shown that XG was capable of protecting the metal surface even after prolonged exposure. Potentiodynamic measurements indicated that the inhibitive action of XG is of a mixed type. The Temkin adsorption isotherm model provided a good fit for the observed data, and the calculated parameters suggested that the adsorption of XG primarily occurred through physical adsorption processes, with a contribution from chemical processes. The associated activation energy and the heat of adsorption further supported the physical nature of XG’s adsorption. FTIR analysis was employed to elucidate the interaction between the XG and the N80 carbon steel surface, while SEM-EDS analysis provided visual confirmation of the XG’s impact on the metal surface. Full article

In response to the challenges of metal corrosion detection and anti-corrosion coating performance evaluation in the marine environment of Zhoushan, this study proposes an improved object detection model, YOLO v5-EfficientViT-NWD-CCA, to enhance the recognition accuracy and detection efficiency of corrosion images on marine structures. Based on YOLO v5, the model incorporates the EfficientViT backbone network, NWD (Normalized Wasserstein Distance) loss function, and CCA (Criss-Cross Attention) attention mechanism, outperforming comparative models across multiple key metrics. Experimental results show that the proposed model increases precision from 0.73 to 0.76 (approximately 4% improvement) and raises the True Positive rate from 0.66 to 0.70 (approximately 6% improvement) according to the confusion matrix, demonstrating more stable overall detection performance. Building on this, the study combines the model’s detection results to conduct a quantitative analysis of the corrosion area of eight types of anti-corrosion coatings in two typical marine environments—tidal zones and fully immersed zones—across different exposure periods (24, 60, and 96 months). The results indicate that the tidal zone presents a harsher corrosion environment, with corrosion severity significantly increasing over time. Fusion-bonded epoxy coatings, powder epoxy coatings, and fluorocarbon coatings exhibit good corrosion resistance, whereas chlorinated rubber coatings and conventional epoxy coatings perform poorly. This study not only achieves intelligent identification and precise quantification of corrosion areas but also provides a scientific basis for the selection and evaluation of anti-corrosion coatings in different marine environments. Full article

This study investigates the corrosion resistance of EN AW 6060 aluminum alloy powder-coated samples, with and without pre-anodizing treatment. The samples were exposed to a 3.5% NaCl solution, which is known for its strong corrosive effects, and their corrosion behavior was evaluated using two electrochemical techniques: Potentiodynamic Polarization and Electrochemical Impedance Spectroscopy (EIS). The aim was to assess the effectiveness of powder coatings in enhancing corrosion resistance and to examine the role of surface preparation and prior treatments. Polarization tests provided corrosion current densities and corrosion rates, while EIS data were analyzed using equivalent electrical circuits to evaluate the integrity of the protective layers. The results show that powder coatings significantly improves corrosion resistance compared to uncoated aluminum and the combination of pre-anodizing followed by painting offers the highest protection. These findings confirm the improved performance achieved through multilayer surface treatments and support the application of powder coatings acting as a durable barrier against environmental factors. Full article

Urban drainage pipelines are essential components of smart city infrastructure, supporting the safe and sustainable operation of underground systems. However, internal corrosion in pipelines poses significant risks to structural stability and public safety. In this study, we propose an enhanced semantic segmentation framework based on High-Resolution Network Version 2 (HRNetV2) to accurately identify corroded regions in Traditional closed-circuit television (CCTV) images. The proposed method integrates a Convolutional Block Attention Module (CBAM) to strengthen the feature representation of corrosion patterns and introduces a Lightweight Pyramid Pooling Module (LitePPM) to improve multi-scale context modeling. By preserving high-resolution details through HRNetV2’s parallel architecture, the model achieves precise and robust segmentation performance. Experiments on a real-world corrosion dataset show that our approach attains a mean Intersection over Union ($mIoU$) of 95.92 $\pm$ 0.03%, $Recall$ of 97.01 $\pm$ 0.02%, and an overall $Accuracy$ of 98.54%. These results demonstrate the method’s effectiveness in supporting intelligent infrastructure inspection and provide technical insights for advancing automated maintenance systems in smart cities. Full article

Coastal regions are among the areas most affected by climate change, facing rising sea levels, frequent flooding, and accelerated erosion that place renewable energy infrastructures under serious threat. Solar farms, which are often built along shorelines to maximize sunlight, are particularly vulnerable to salt-induced corrosion, storm surges, and wind damage. These challenges call for monitoring solutions that are not only accurate but also scalable and privacy-preserving. To address this need, Q-MobiGraphNet, a quantum-inspired multimodal classification framework, is proposed for federated coastal vulnerability analysis and solar infrastructure assessment. The framework integrates IoT sensor telemetry, UAV imagery, and geospatial metadata through a Multimodal Feature Harmonization Suite (MFHS), which reduces heterogeneity and ensures consistency across diverse data sources. A quantum sinusoidal encoding layer enriches feature representations, while lightweight MobileNet-based convolution and graph convolutional reasoning capture both local patterns and structural dependencies. For interpretability, the Q-SHAPE module extends Shapley value analysis with quantum-weighted sampling, and a Hybrid Jellyfish–Sailfish Optimization (HJFSO) strategy enables efficient hyperparameter tuning in federated environments. Extensive experiments on datasets from Norwegian coastal solar farms show that Q-MobiGraphNet achieves 98.6% accuracy, and 97.2% F1-score, and 90.8% Prediction Agreement Consistency (PAC), outperforming state-of-the-art multimodal fusion models. With only 16.2 M parameters and an inference time of 46 ms, the framework is lightweight enough for real-time deployment. By combining accuracy, interpretability, and fairness across distributed clients, Q-MobiGraphNet offers actionable insights to enhance the resilience of coastal renewable energy systems. Full article