Yang Wang

Erosion-corrosion in refining and chemical plant pipelines remains a persistent integrity concern, particularly in straight sections located downstream of elbows, which are rarely prioritized in inspection programs that typically focus on elbows and tees despite their well-known vulnerability. In these downstream regions, developing flow structures can sustain wall impingement and liquid film formation, leading to progressive material loss that is often underestimated in practice. This work examines a representative industrial pipeline through a combined approach based on computational fluid dynamics (CFD) simulations and controlled experimental validation to resolve the hydrodynamic behavior in the straight pipe section downstream of an elbow under different operating conditions. The objective is to identify the governing parameters controlling liquid film development and to support predictive assessment of erosion-corrosion risk in overlooked pipeline regions. The analysis shows that liquid film length is primarily controlled by flow velocity, water volume fraction, and the length of the upstream vertical section. When the upstream vertical pipe length is below 1 m, the flow remains highly unstable after the elbow, promoting intensified wall interaction and increased erosion susceptibility. For larger upstream lengths, the liquid film length exhibits an approximately linear increase with water volume fraction. Its dependence on flow velocity is non-monotonic, increasing at lower velocities, reaching a maximum at 6 m/s, and decreasing thereafter. Building on these trends, a predictive correlation is developed for liquid film length as a function of water volume fraction, flow velocity, and pipe length. The resulting model provides a quantitative basis for identifying erosion-corrosion risk in straight pipe sections and supports more targeted inspection and integrity management strategies in industrial pipeline systems.

In the fabrication of microelectronic devices, polyimide (PI) is often patterned through plasma etching. However, the relationship between its etching behavior and the precursor processing technology has not been systematically revealed. In this paper, PMDA-ODA type PI was selected as the research object. For the first time, the effects of two film-forming processes, freeze drying (FD) and thermal drying (TD), on the microstructure of PI films and their O2 plasma etching behavior were systematically compared. By means of scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR), and water contact angle testing, the intrinsic differences between the two types of films and the performance evolution rules during the etching process were clarified. The results show that the surface of the FD-PI film exhibits discrete pore structure, while the TD-PI film is characterized by a dense and non-porous structure. This structural difference leads to more significant surface morphology evolution and chemical composition changes in FD-PI during the etching process. After plasma treatment, polar oxygen-containing groups such as O=C-O were successfully introduced onto the surfaces of both types of films. The content of these groups showed regular changes with the etching time, and the wettability of the films processed by the two different technologies was characterized by a dynamic water contact angle measuring instrument. This study reveals the internal mechanism by which the drying process affects the plasma etching behavior of PI films by regulating their initial microstructure, providing important theoretical basis and process guidance for the precision processing of high-performance PI films for advanced electronic devices.

Polyethylene (PE) pipes are widely used in various gas transmission applications due to their excellent flexibility and other advantages. As high-molecular-weight non-metallic polymeric materials, they inevitably undergo degradation and aging during service under the action of environmental factors such as temperature and ultraviolet (UV) radiation, which impairs their service performance. To investigate the effects of environmental factors on the aging and degradation of PE pipes, a reliable method for predicting their service life was developed in this study. Taking PE80 DN110 pipes as the research subject, an accelerated aging method was adopted to simulate the combined effects of thermo-oxidative and photo-oxidative aging on the pipes. By using Fourier Transform infrared spectroscopy (FTIR) to test and track the carbonyl index (CI), the aging mechanism is explored. Oxidation Induction Time (OIT) and Melt Mass Flow Rate (MFR) tests were used to characterize changes in the pipes’ properties, thermal stability, and melt flow behavior after aging. The Arrhenius equation was used to create a lifetime prediction model under four different temperature conditions. This study estimates the service life of PE pipes under combined photo-oxidative and thermo-oxidative aging conditions. According to research, products produced during the thermo-oxidative and photo-oxidative aging processes of polyethylene pipes can promote secondary reactions and photo-oxygen reactions. The aging rate is 1.87 times higher than that of thermo-oxidative aging alone. The MFR decreased under combined thermo-oxidative and photo-oxidative aging compared with single thermo-oxidative aging, demonstrating impaired melt flowability of polyethylene pipes caused by aging and degradation. Based on the Arrhenius equation, a multi-factor lifetime prediction model for polyethylene pipes was created.

Gas polyethylene (PE) pipes have an become essential component of the urban gas pipeline network due to their long service life and corrosion resistance. To prevent safety incidents, regular monitoring of gas pipelines is crucial. Traditional inspection methods face significant challenges, including low efficiency, high costs, and limited applicability. Machine vision-based inspection methods have emerged as a key solution to these issues. Despite this, the method also encounters the problem of scarcity of defect samples and uneven data distribution in gas pipeline defect detection. For this reason, an improved Deep Convolutional Generative Adversarial Network (DCGAN) is proposed. By integrating the Minibatch Discrimination (MD), Spectral Normalization (SN), Self-Attention Mechanism (SAM) and Two-Timescale Update Rule (TTUR), the proposed approach overcomes the original DCGAN’s limitations, including mode collapse, low resolution of generated images, and unstable training, the data augmentation of defective images inside the pipeline is realized. Experimental results demonstrate the superiority of the improved algorithm in terms of image generation quality and diversity, while the ablation study validates the positive impact of the improvement in each part. Additionally, the relationship between the number of augmented images and classification accuracy, showing that classifier performance improved across all scenarios when generated defect images were included. The findings indicate that the images produced by the improved model significantly enhance defect detection accuracy and hold considerable potential for practical application.

In this paper, a method based on image recognition was proposed to detect the defects of polyethylene (PE) gas pipeline, especially the deformation due to the indentation. First, the pipeline -detection VGG (PD-VGG) model was established based on the convolutional neural network (CNN), and appropriate model parameters were optimized through model training. The defect recognition rate of the improved model can reach 94.76%. Following, the weighted average graying algorithm was used to separate the defects characterized by deformation. Then, an improved gamma correction algorithm was applied to achieve image enhancement, and the interference of impurities adhered on intersurface of pipeline was also removed by using multilayer filters. The edge detection of the defect image was completed by using the Canny operator, and following the screening between the target contour and the interference contour by using top-contour. Finally, the algorithm for minimum outer rectangle algorithm was used to fit the defect contour, and the eigenvalues of deformation defects were extracted. The results indicate that the above defect detection method can better extract the deformation contour of the dented pipeline. The high agreement with the experimental results provides a basis for the research of effectively recognizing whether the pipeline has undergone ductile failure only through profile detection of defects.

After around 50 years of development, the key substance known as polyethylene has been extremely influential in a variety of industries. This paper investigates how polyethylene materials have been used in the domains of water, packaging, and medicine to advance contemporary society in order to comprehend the physical and chemical alterations that polyethylene undergoes after being subjected to long-term environmental variables (e.g., temperature, light, pressure, microbiological factors, etc.). For the safe operation of polyethylene materials, it has always been of the utmost importance to evaluate polyethylene’s service life effectively. This paper reviews some of the most common literature journals on the influence of environmental factors on the degradation process of polyethylene materials and describes methods for predicting the lifetime of degradable polyethylene materials using accelerated aging tests. The Arrhenius equation, the Ozawa–Flynn–Wall (OFW) method, the Friedman method, the Coats–Redfern method, the Kissinger method and Kissinger–Akahira–Sunose (KAS) method, Augis and Bennett’s method, and Advanced Isoconversional methods are all discussed, as well as the future development of polyethylene.