In the natural gas pipeline transportation system, the series elbows is particularly susceptible to erosion due to its special structure and the sand-producing characteristics of natural gas. The erosion behavior of the series elbows is affected by multiple factors, among which the length of the connecting pipe between the two elbows is a variable that cannot be ignored. For this reason, the computational fluid dynamics(CFD)-discrete phase model(DPM) numerical simulation method was used to study the erosion behavior of the series elbows at different spacings of sand-containing natural gas. The results show that when two elbows are installed in series, the corrosion morphology and rate of the second elbow are greatly affected by the distance between the two elbows. With the increase of the length of the middle section of the series elbow, the corrosion morphology of the first elbow is V-shaped, but the corrosion morphology of the second elbow gradually changes from a triangle to a V-shape. In addition, due to the influence of gravity on the migration trajectory of sand particles, the area with the most serious erosion and wear of the second elbow is 5° to 8° behind the upstream elbow. By analyzing the multi-angle section flow field of the second elbow, it is found that the airflow generates a more complex secondary flow at the second elbow after passing through the middle section. Therefore, the maximum erosion rate of the second elbow decreases first and then increases with the increase of the length of the middle section of the series elbows. The research results can provide certain theoretical guidance and basis for optimizing the engineering design and erosion prevention of the double elbow system.

In order to comprehensively analyze and evaluate the monitoring capabilities of the integrated multi-satellite retrievals for global precipitation measurement (GPM) final run (IMERG-F) satellite retrieval product for daily and sub-daily scale precipitation, as well as various intensity rainfalls, under complex mountainous conditions in southwest China, ground-based dense rain gauge data was used to investigate these aspects. The results show that within the daily and sub-daily scales, the critical success index (CSI) of IMERG for short-term accumulated precipitation primarily ranges from 0.2 to 0.6, with the correlation coefficient fluctuating between 0.25 and 0.5. The daily scale precipitation detection accuracy is the highest, with better performance in summer months compared to winter. For different intensity rainfall events, IMERG exhibits a high probability of detection (POD) for light rainfall, while the false alarm rate (FAR) is relatively low. However, there is an underestimation phenomenon for moderate to heavy rainfall. The elevation difference significantly impacts the stability of IMERG products, but there is no direct linear relationship with the elevation itself. Compared to areas with significant topographic variations, IMERG-F demonstrates higher reliability in detecting weak rainfall events in areas with less topographic variation. It is concluded that the application of IMERG products in southwest China should consider the limitations imposed by seasonal and topographic characteristics.

Structural design is an important part of the architectural engineering design stage, which must ensure that the building is safe, reliable, economical, and durable. Artificial intelligence can replace structural designers with a lot of training and repetitive operations to find the optimal design results and improve design efficiency. In order to comprehensively understand the relevant research and application hotspots of artificial intelligence in structural design, the current research status of artificial intelligence in the three stages of scheme design, preliminary design and construction drawing design was summarized from the perspective of the entire structural design process. Through reviewing literatures, it is found that artificial intelligence methods such as expert systems, decision trees, annealing algorithms, genetic algorithms, neural networks, and linear regression have been widely used in the field of building structure design, which has brought new development directions and approaches. At present, artificial intelligence methods are more widely used in the design of aboveground structures, but less in underground structures (foundations, basements, etc.), and their application in underground structures needs to be strengthened. In addition, the quantitative translation technology of normative provisions is relatively mature, but the qualitative translation technology of normative provisions still needs to be broken, and it is necessary to strengthen the research on rule-based or machine learning-based natural language processing.

A systematic study was conducted on the issue of gate assignment, with the goal of minimizing the number of remote gate assignments and the idle time of near gates. A multi-objective mathematical model was proposed to address the multi-objective and multi-constraint characteristics of the problem. The model was designed to minimize the number of remote gate assignments and the idle time of near gates while taking into account parameters such as actual flight arrival and departure times, aircraft types, and the interrelationships among gates. The gate assignment process was optimized using the deep reinforcement learning method, specifically the deep deterministic policy gradient(DDPG) algorithm. To enhance the optimization ability and performance of the algorithm, an improved DDPG algorithm was developed by incorporating prioritized experience replay and multi-strategy exploration mechanisms. Comparative experiments were conducted, and the results show that the improved algorithm significantly reduces the number of remote gate assignments and optimized time utilization. The algorithm also achieves faster convergence and stronger global optimization capabilities, confirming its effectiveness.

In order to improve the security performance of civil aviation security personnel, the relationship between security personnel's hazard perception ability and security performance under different factors was explored. Based on the fuzzy signal detection theory(FSDT), the discriminability and judgment criterion were used as indicators to measure the hazard perception ability of security personnel. A mixed experimental design of 2 (experience: novice, veteran) ×2 (time pressure: no time limit, time limit) ×2 (probability of occurrence of contraband: high, low) was employed to examine the impact of experience, time pressure, and probability of contraband on the hazard perception ability of security personnel. The results indicate that experience and time pressure significantly impact security performance, while the main effect of contraband probability on security performance is not significant. Experience has a notable effect on fuzzy discriminability and fuzzy judgment criterion, with veterans demonstrating higher overall hazard perception ability than novices. Additionally, a time limit reduces the discriminating power of security personnel under low contraband probability but improves fuzzy judgment criterion standards under high contraband probability. Attaching importance to security skills training, setting reasonable search time and conveyor speed can effectively improve security personnel's hazard perception ability and ensure aviation safety.

To address the high cost and low accuracy of manual inspection for steel surface defects, as well as the excessive computational resource requirements caused by complex traditional target detection models,a lightweight defect detection algorithm named YOLOv8n-MDC was proposed by integrating MobileNetv3 with YOLOv8.Firstly, based on YOLOv8n, the original intersection over union(IoU)-based bounding box loss function was replaced with weighted IoU(WIoU), enhancing model robustness through a non-monotonic focusing mechanism. Secondly, the backbone feature extraction network of YOLOv8n was substituted with MobileNetv3, utilizing its lightweight architecture to reduce network complexity and redundant computational overhead. Finally, during the feature fusion stage, depthwise separable convolution (DWConv) and C3Ghost modules replaced the original components, further minimizing model parameters and accelerating detection speed. Evaluated on the NEU-DET steel surface defect dataset, the YOLOv8n-MDC achieves an mAP of 81.3%, representing a 5% improvement over the baseline YOLOv8n, while its parameter count and computational complexity are reduced to 1.02 M and 2.1 GFLOPs (33.9% and 25.9% of the original model, respectively), meeting industrial requirements. This lightweight algorithm significantly reduces computational complexity and resource consumption while enhancing detection accuracy, offering an optimized solution for industrial steel surface defect inspection.

As a key link between carbon source and carbon sink in carbon capture, utilization and storage(CCUS) technology, CO₂ pipeline transportation will play an important role in the process of carbon neutralization in the future. For the pipeline water hammer condition, the pressure oscillation may exceed the pressure in the pipe and be lower than the inlet pressure of the pump. At present, the water hammer and control theory of supercritical CO₂ pipeline is not mature. A mathematical model based on the law of conservation of mass, momentum and energy was established to describe the one-dimensional gas flow in the pipeline. The characteristic line method was used to solve the model, and the MATLAB programming was used to calculate. The simulation results were compared with the simulation results of the gas transmission system model proposed by Kiuchi and the simulation results of the commercial software OLGA. The results show that the simulation results are generally consistent with the simulation results of the gas transmission system model. Compared with OLGA software, the maximum relative errors of pressure and flow are 0.02% and 2.32%, respectively, which meet the requirements of engineering calculation accuracy. For the fast transient process of pipeline parameter change caused by pipeline compressor start and stop, valve emergency switch and rapid change of flow in a short time, the rapid change value is set to simulate. The established model can calculate the parameter change of each node with high accuracy, which can provide theoretical support and technical support for the localization of supercritical CO₂ pipeline transportation process simulation software.

Highway maintenance operations occupy existing road infrastructure and have a significant impact on vehicle traffic safety and efficiency. In order to analyze the traffic flow characteristics during maintenance operations on a four-lane highway when the inner lane is closed, a traffic flow modeling and simulation method based on an improved cellular automaton was proposed. According to the Highway Maintenance Safety Operation Regulations (JTG H30—2015), maintenance operation control zones were established, dividing the mainline highway facilities into six traffic scenarios: warning zone, upstream transition zone, buffer zone, work zone, downstream transition zone, and termination zone. By introducing a longitudinal safety distance model and optimizing the lateral lane-changing safety condition determination rule, the NaSch car-following model and symmetric two-lane cellular automata (STCA) lane-changing model were improved. The model was calibrated using field data, and the traffic flow operating conditions in the maintenance work zone were simulated using MATLAB. The results show that when the traffic flow density reaches 1 550 pcu/h per lane, setting the merging start point 1 000 meters downstream of the warning zone, with a speed limit of 60 km/h and an upstream transition zone length of 160 meters, the traffic flow safety and efficiency indices in the maintenance zone are optimized.

To investigate the psychological load characteristics of drivers on interchange ramp curves, an naturalistic driving test was conducted with 38 participants in a high-density interchange group in Chongqing. The PhysioLAB physiological monitoring system was used to collect electrocardiogram (ECG) data from drivers navigating the ramp curves. Three typical ramps were taken as research objects, a factor analysis model was established based on heart rate (HR), heart rate increment (HRI), and heart rate variability[root mean square of the difference between adjacent normal cardiac cycles(RMSSD) and standard deviation of RR intervals for all sinus heartbeats(SDNN)] to analyze the variations in psychological load and influencing factors for drivers on different ramp curves. The results indicate that the average heart rate for drivers on various ramp scenarios is found to range from 60 to 120 beats per minute, with heart rate increments between 3% and 15%. Psychological load on right-turn ramps is more dispersed, while on left-turn and circular ramps, it is more concentrated, with higher load observed on circular ramps. Differences in psychological load are noted among drivers of different styles and genders. Left-turn ramps impact female drivers more, and right-turn ramps impact aggressive drivers more. Psychological load decreases with increased familiarity and increases with larger turning angles. More ramp lanes lead to more vehicle weaving, increasing psychological load. A negative correlation is observed between psychological load and the radius of right-turn ramps, with no linear relationship for left-turn and circular ramps. Driving on consecutive circular ramps increases psychological load.

The smart skin of an aircraft is realized by integrating distributed sensors, actuators, and controllers into the composite skin, thereby enabling it to monitor its own state and detect damages. The physical field inversion algorithm plays a key role in the signal processing of the smart skin. However, due to factors such as the low sensor density, traditional inversion algorithms exhibit limited accuracy. In order to enhance the monitoring precision of the smart skin, a back propagation(BP) neural network-improved grey wolf optimizer(IGWO) inversion algorithm, which combined a BP neural network with an IGWO-optimized Kriging model, was proposed. A prototype of the smart skin was subsequently fabricated, and wind tunnel tests were conducted to validate the proposed algorithm. The results demonstrate that the BP-IGWO inversion algorithm achieves higher accuracy and superior detail representation compared to traditional inversion algorithms, and can better monitor the state of smart skin.