The ball valve is an essential piece of equipment in an oil and gas pipeline. The sand particles transported through the pipeline can cause erosion and wear to the ball valve, thus causing it to fail, leading to serious safety hazards. In this paper, the self-designed erosion experiment method was combined with computational fluid dynamics (CFD), while the Euler-Lagrange method was also introduced to optimize the Oka erosion model and Ford particle-wall rebound model. The erosion mechanism and characteristics of the ball valve sealing surface in gas-solid two-phase flow were simulated, while the erosion condition of the specimen was analyzed and compared when exposed to different factors, such as different particle velocities, impact angle, particle size, and specimen materials. The experimental data conformed well to the CFD erosion simulation data, verifying the accuracy of the CFD simulation analysis. The results indicated that the worn surface was caused by various wear mechanisms, while a “stagnation zone” was identified at the center of the specimen. The maximum erosion area, which was U-shaped, was also located at the center. The erosion rate increased in conjunction with an increase in the particle velocity and size, both of which failed to affect the erosion pattern. The erosion rate initially increased, after which it decreased with the impact angle, reaching the maximum value at an impact angle of 30°. This paper summarizes the erosion failure mechanism and characteristics in gas–solid two-phase flow and provides both technical support and a theoretical basis for the on-site maintenance of essential vulnerable parts in the pipeline, such as ball valves.

Two metal-free organic luminophores with ultrastrong dipole moments of 16.1 (CPBT) and 22.7 (CTBT) Debye were developed for constructing near-infrared mechanoresponsive luminescent materials. Amorphous CTBT showed a remarkable emission turn-on peak at 822 nm due to disturbance in the dipole–dipole interactions in the solid state upon mechanical grinding.

Girth weld defects in long-distance oil and gas pipelines are one of the main causes of pipeline leakage failure and serious accidents. Magnetic flux leakage (MFL) is one of the most widely used inline inspection methods for long-distance pipelines. However, it is impossible to determine the type of girth weld defect via traditional manual analysis due to the complexity of the MFL signal. Therefore, an automatic image classification method based on deep convolutional neural networks was proposed to effectively classify girth weld defects via MFL signals. Firstly, the image data set of girth welds MFL signal was established with the radiographic testing results as labels. Then, the deep convolutional generative adversarial network (DCGAN) data enhancement algorithm was proposed to enhance the data set, and the residual network (ResNet-50) was proposed to address the challenge presented by the automatic classification of the image sets. The data set after data enhancement was randomly selected to train and test the improved residual network (ResNet-50), with the ten validation results exhibiting an accuracy of over 80%. The results indicated that the improved network model displayed a strong generalization ability and robustness and could achieve a more accurate MFL image classification of the pipeline girth welds.

Pipelines that cross complex geological terrains are inevitably threatened by natural hazards, among which landslide attracts extensive attention when pipelines cross mountainous areas. The landslides are typically associated with ground movements that would induce additional stress on the pipeline. Such stress state of pipelines under landslide interference seriously damage structural integrity of the pipeline. Up to the date, limited research has been done on the combined landslide hazard and pipeline stress state analysis. In this paper, a multi-parameter integrated monitoring system was developed for the pipeline stress-strain state and landslide deformation monitoring. Also, data-driven models for the pipeline additional stress prediction was established. The developed predictive models include individual and ensemble-based machine learning approaches. The implementation procedure of the predictive models integrates the field data measured by the monitoring system, with k-fold cross validation used for the generalization performance evaluation. The obtained results indicate that the XGBoost model has the highest performance in the prediction of the additional stress. Besides, the significance of the input variables is determined through sensitivity analyses by using feature importance criteria. Thus, the integrated monitoring system together with the XGBoost prediction method is beneficial to modeling the additional stress in oil and gas pipelines, which will further contribute to pipeline geohazards monitoring management.

Automatic detection of safety helmet wearing is significant in ensuring safe production. However, the accuracy of safety helmet detection can be challenged by various factors, such as complex environments, poor lighting conditions and small-sized targets. This paper presents a novel and efficient deep learning framework named High-Resolution You Only Look Once (HR-YOLO) for safety helmet wearing detection. The proposed framework synthesizes safety helmet wearing information from the features of helmet objects and human pose. HR-YOLO can use features from two branches to make the bounding box of suppression predictions more accurate for small targets. Then, to further improve the iterative efficiency and accuracy of the model, we design an optimized residual network structure by using Optimized Powered Stochastic Gradient Descent (OP-SGD). Moreover, a Laplace-Aware Attention Model (LAAM) is designed to make the YOLOv5 decoder pay more attention to the feature information from human pose and suppress interference from irrelevant features, which enhances network representation. Finally, non-maximum suppression voting (PA-NMS voting) is proposed to improve detection accuracy for occluded targets, using pose information to constrain the confidence of bounding boxes and select optimal bounding boxes through a modified voting process. Experimental results demonstrate that the presented safety helmet detection network outperforms other approaches and has practical value in application scenarios. Compared with the other algorithms, the proposed algorithm improves the precision, recall and mAP by 7.27%, 5.46% and 7.3%, on average, respectively.

One-dimensional (1D) porous Ni_xS_y nanostructures have been successfully fabricated by two-step method consisting of solvothermal and subsequent annealing process. The suitable heat treatment temperature and reaction time play crucial roles in the final structure, morphology, as well as performance. The uniform and perfect porous Ni_xS_y nanostructures obtained at 310°C exhibit outstanding microwave absorption performances. A minimum reflection loss of −35.6 dB is achieved at 8.5 GHz, and the effective absorption bandwidth almost covers 14.5 GHz with the absorber thickness range of 2.0–5.0 mm. It can be supposed that this porous structure with rough surface which is favor for increasing the microwave multiple reflection and scattering, contributes a high-performance electromagnetic absorption.