The occurrence of natural gas leaks in buried gas pipelines is a serious safety event that can have significant economic and environmental impacts. For large-diameter high-pressure gas transmission pipelines, the computational fluid dynamics (CFD) method was used to establish a three-dimensional numerical model that included a${1.4}\mathrm{\;m}$diameter pipeline and the surrounding soil, to study the leakage characteristics of high-pressure gas through a pre-set leak hole in the soil. The CFD model considered the soil as a porous medium material, used the Redlich-Kwong equation of state to describe the temperature-pressure effects of high-pressure gas, and combined species transport and turbulence models to study the impact of leak hole diameter and internal pipeline pressure on leakage rate and temperature distribution. The results show that the leakage rate increases with the increase of hole diameter and pressure. When the leak hole diameter varies from 10 to${50}\mathrm{\;{mm}}$, the leakage rate increases by${77.78}\%$. Ambient temperature can cause the soil temperature field distribution to take different forms. When the ambient temperature is low, the temperature-pressure effect produced by the leakage of high-temperature gas inside the pipeline will be weakened. When the ambient temperature is close to the temperature of the gas inside the pipeline, a detectable temperature change area is produced in the buried range of 0.7 to 1.2 m above the leak hole. The research results help to understand the leakage characteristics and temperature change patterns of buried large-diameter high-pressure gas transmission pipelines, providing a theoretical basis for the layout of pipeline leak monitoring optical cables.

In order to address the issue of coordinate base inconsistency in the fusion display of road building information modeling (BIM) models and tilted reality models within existing large-scale 3D geographic information system (GIS) platforms, a high-precision matching method for geographic coordinates between road BIM models and tilted reality models was proposed. Taking into account the distribution characteristics of road bands and the requirements for road maintenance, the model was initially segmented. Subsequently, a spatial distance-weighted least-squares coordinate matching parameter fitting method was developed based on the distribution of characteristic points on the road pavement and asset facility model, with a focus on accurately joining edges of the road pavement in each segment. Real road data was selected for conducting experiments to validate this coordinate matching method. The method effectively resolves bias issues in matching between the road model and tilted reality model, achieving accuracy at millimeter level post-matching, thereby meeting digital maintenance needs as well as dynamic updating requirements for road traffic facilities.

Synthetic aperture radar (SAR) target recognition method based on deep networks requires a large amount of training data, and in practical applications, it is extremely difficult for SAR imaging systems to obtain sufficient and evenly distributed target data. One way to solve the small sample problem in SAR target recognition, is to use electromagnetic simulation technology to generate a large amount of SAR simulation data. However, there are still significant differences between simulated images and measured SAR images, so using simulation data directly cannot bring significant performance improvement for target recognition. A simulation data optimization method based on SAR target characteristic constraints was proposed to address the above issues. On the basis of analyzing the characteristics of SAR targets, a texture structure cycle-consistent generative adversarial network (TS-CycleGAN) based on texture structure and cycle consistency was constructed, in which the structural similarity measure was used to constrain the generation process of CycleGAN. This method can reduce the difference between simulation data and measured data, and can improve the usability of simulation data. The experimental results on the SAR SAMPLE dataset show that, compared to other simulation data optimization methods, the proposed method achieves significant improvements in image quality evaluation and classification performance.

Fault diagnosis of industrial motor bearings is crucial for equipment performance and lifespan. Traditional diagnostic methods aggregate data from multiple factories, leading to issues with data privacy and high annotation costs. To address these problems, a fault diagnosis strategy based on adaptive local collaboration (ALC) federated learning was proposed. In this approach, bearing data under different working conditions was stored across multiple clients, with a central server collaborating with each client to build a federated learning diagnostic model. An improved ResNet-18 network was used as the classifier, which was trained within the personalized federated learning framework. The ALC federated learning method enables each client to effectively integrate global and local models, extracting global information to optimize local training results. Experiments demonstrate that this method enhances fault diagnosis accuracy while protecting data privacy, showing higher fault classification precision compared to other methods, especially in multi-factory environments.

In order to select advanced technologies applicable to civil aircraft, technical characteristics from various fields were integrated to develop an evaluation framework. Five key evaluation dimensions were identified: technology competitiveness, technology readiness assessment, economic impact, engineering methods, and technology standards. From practical case studies, these dimensions were derived and used as the basis for an evaluation index system. A technology application perspective was adopted, utilizing a cloud model and a reverse cloud generator to determine indicator weights. This approach incorporated technical standards from different industries, airworthiness standards, and the entire life cycle of civil aircraft to create comprehensive evaluation guidelines. The results show that this approach effectively compares advanced technologies across different industries, differentiates similar technologies at various levels, and eliminates those that offer no benefit or are unsuitable for civil aircraft. This evaluation approach successfully selects advanced technologies with a high degree of compatibility with civil aircraft.

At present, China's offshore floating photovoltaic is in its exploratory stage, which ensures the dynamic stability of floating photovoltaic foundation under varying environmental loads becoming a key research priority. In order to more effectively solve the above-mentioned difficulties and the shortage of land resources in the photovoltaic industry, a floating photovoltaic PE floating block foundation structure design, hydrodynamic calculation and optimization were completed through numerical simulation calculation based on the environmental conditions of a sea area in Rushan City, Weihai. The results indicate that wave height, wave period, and wave incidence angle have varying degrees of influence on the motion response and internal force values of the structure. Based on the analysis, practical applications of the new floating foundation in specific projects are guided, showing promising results that verify the feasibility and stability of this foundation structure's application.

Given the practical application background of installing underground pipelines in coal mine tunnels and the actual environmental conditions underground, a jointed tunnel pipeline installation robot was designed. The detailed design of the robotic arm structure was completed, along with its 3D modeling. The kinematic model of the robot was established, and MATLAB was employed to verify the forward and inverse kinematics of the robotic arm. Based on the established kinematic model, a trajectory planning algorithm for the Cartesian space of the robotic arm was designed, and MATLAB and ADAMS software were used to verify the robotic arm through simulation experiments. The simulation demonstrates that the structural design of the robotic arm is reasonable, and the trajectory planning scheme for the robotic arm is feasible.

As a critical unconventional oil and gas resource within the global energy framework, heavy oil has garnered significant attention for its development efficiency. Although steam flooding technology has improved the efficiency of heavy oil production, the phenomenon of steam breakthrough negatively impacts thermal efficiency and reservoir development. Traditional prediction methods have shown inadequate precision and delayed response when dealing with long-term oilfield time series data. Data from 13 steam flooding well groups in the Shengli oilfield heavy oil block were utilized. An innovative approach was adopted, using the instantaneous temperature ratio between production and injection wells as an indicator of steam breakthrough time. Pearson correlation coefficient analysis was employed to select key factors related to steam breakthrough time. Based on these factors, a deep learning model built on the Transformer architecture was developed, achieving accurate predictions of the instantaneous temperature ratio. The predictions closely aligned with oilfield observation data, demonstrating higher prediction accuracy and stability compared to traditional long short-term memory (LSTM) models. The research results not only provide a new perspective for the precise prediction of steam breakthrough time in heavy oil reservoirs but also further validate the extensive potential of deep learning technology in oilfield development applications, supporting the construction of intelligent oilfield management and decision support systems.

In order to accurately calculate the hydrodynamic parameters of the slope rill at any point during the erosion process, and to avoid errors caused by using the average flow rate to calculate the hydrodynamic parameters in the traditional method. Based on the variability and complexity of the development process of slope rills, as well as the characteristics of water sand two-phase flow, the Euler-Euler two-phase flow model was used to calculate and analyze the morphological evolution characteristics and erosion mechanisms of slope rills at different stages of expansion erosion. The results show that the Euler-Euler two-phase flow model can accurately describe the morphology evolution process of slope rill in expanded erosion. Based on the morphology evolution characteristics of slope rill at different stages of expanded erosion, the expanded erosion of slope rill is divided into the period when the rill sidewall is slightly spreading and eroding (the early stage), the period when the expanded erosion become severe with a significant increase in the number and area of amalgamated arcs (the middle stage), and the period when the expanded erosion basically ceased and the rill morphology stabilized (the late stage). The influential factors of slope gradient, initial flow rate, and preset rill width on the Darcy-Weisbach resistance coefficient, Reynolds number, and real-time flow rate are significant. Optimal characterization parameters for different stages of slope rill development, such as erosion arc length and hydraulic radius, are proposed, aiding in determining the specific period of slope rill development and predicting the development trend of rill morphology through changes in these parameters. The research results provide a theoretical basis for soil erosion control measures and are of great significance for soil and water conservation.

With the increasing number and aging of in-service oil and gas pipelines in China, issues such as corrosion, aging, and geometric deformation have become increasingly apparent. Due to the limitations of manual inspections, such as complex spatial environments and narrow pipe diameters, the use of pipeline robots for inspection and maintenance has emerged as a dominant trend in both domestic and international research. To address the risks of pipeline failure after prolonged service, understanding the latest advancements in pipeline robotics is crucial for setting forward-looking development goals and minimizing redundant research efforts. A comprehensive review of recent developments in in-service pipeline robotics was provided, these robots were divides into two main types based on their movement mechanisms and working environments: internal (passive and active) and external pipeline robots. By examining specific examples of each type, their overall performance were compared and highlighted key considerations for field applicability. Furthermore, the future directions were explored for pipeline robotics, emphasizing the importance of multi-parameter integration in ensuring the safe operation of oil and gas pipelines in the future.