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 quickly identify the location of the leakage point and the leak aperture in the coal mine, a model was proposed for identifying and locating the leak aperture by using the pressure and flow signals generated when the water supply pipeline leaked. Modal energy entropy and genetic algorithm combined with envelope entropy were used to optimize the parameters of variational mode decomposition (VMD), and then VMD was used to denoise the pressure signal. Convolutional neural network (CNN) was used to extract the deep feature sequence of pressure and flow signal, and the long short-term memory network (LSTM) was used to extract the time sequence of deep feature sequence to identify and locate the leak aperture. The experimental results show that compared with Kalman filter, mean value filter and low-pass filter, the variational modal decomposition with optimized parameters has higher root-mean-square error (RMSE), mean absolute error (MAE), signal-to-noise ratio (SNR) and normalized cross correlation (NCC), which indicates that it can effectively reduce noise components and retain effective signals. Compared with LSTM, the MAE, mean absolute percentage error (MAPE) and RMSE of CNN-LSTM in leak location decrease by 65.97%, 61.22% and 59.11%. In the identification of leak aperture, MAE decreases by 12.04%, MAPE decreases by 22.45%, and RMSE decreases by 3.29%, which proves that CNN-LSTM can make full use of the spatial and temporal characteristics of pipeline pressure and flow signals to identify the leak location and aperture, and its detection effect is more accurate and stable than LSTM.

In the context of achieving carbon peak and carbon neutrality in transportation, high-precision, fine-grained, and highly feasible real-time prediction methods for motor vehicle energy consumption have become key components in reducing carbon emissions. Addressing the issue of limited universality in traditional regression-based vehicle energy consumption models, a prediction model based on the radial basis function neural network (RBFNN) has been developed. Firstly, the influencing factors of vehicle energy consumption were analyzed, and the influence factor matrix was normalized using the Min-Max standardization method. Then, the grey wolf optimization (GWO) algorithm was employed to optimize the training of the centers of the hidden layer, the width of the Gaussian function, and the weights connecting the hidden layer to the output layer in the RBFNN algorithm. Finally, a comprehensive analysis of the model's prediction accuracy was conducted through horizontal model comparisons and real-world vehicle measurements. The test results demonstrate that the RBFNN algorithm improves prediction accuracy by approximately 12% compared to traditional regression models, achieving an overall accuracy of over 90%. This makes it highly effective in accurately predicting the energy consumption of urban motor vehicles.

In order to explore the effect of dry-wet cycles in acidic environment on the physical and mechanical properties of limestone, and to evaluate the long-term stability of limestone rock mass in this environment, the limestone of the Jinfo Mountain of the Nanchuan District in Chongqing was selected as research subject. The limestone specimens were exposed to dry-wet cycles under neutral and acidic environments. The specimens were treated through mass loss test, hygroscopic property test, uniaxial compression test and tensile test. The results show that under the condition of the same pH of the soaking solution, with the increase of the times of dry-wet cycles, the mass loss rate and saturation water absorption rate of specimens increase; the tensile strength, uniaxial compressive strength and elastic modulus gradually decrease; with the same times of dry-wet cycles, the lower the pH of the soaking solution leads to the more serious the loss of physical and mechanical properties. Based on the experimental results, the damage theory, Weibull distribution, Lemaitre strain equivalence hypothesis and Mohr-Coulomb (M-C) strength criterion, the damage constitutive model of limestone by using a quadratic function to characterize the nonlinear features of the compaction stage of stress-strain curve was established and validated.

To clarify the mechanism of water injection damage in low-permeability reservoirs of the Longdong Oilfield, the Chang 3 reservoir was selected as the research subject. A comprehensive analysis method was developed, starting from the intrinsic factors of the reservoir to the external engineering factors, to analyze the mechanism of reservoir water injection damage. X-ray diffraction (XRD), cast thin sections, and scanning electron microscopy (SEM) were used to analyze the reservoir’s rock physical properties and pore structure. Experimental methods combining visual microfluidics with nuclear magnetic resonance(NMR) were employed to analyze the damage patterns of externally injected water on low-permeability reservoirs. The results show that the intrinsic factors causing blockage in the Chang 3 reservoir are related to its low porosity and low permeability, with pore throat diameters all less than${20\mu }\mathrm{m}$, leading to poor reservoir properties and high liquid flow resistance in the reservoir. The composition of clay minerals mainly includes kaolinite and illite, which are velocity-sensitive minerals prone to fine particle migration and reservoir blockage. The external engineering factors causing blockage are the incompatibility of injected water with formation water, resulting in the formation of scale particle. These scale particles and clay particles can undergo a blockage-breakthrough process at the pore throat passages, causing fluctuating increases in injection pressure. Moreover, the injected water can carry scale and clay particles into the deep parts of the reservoir, where they accumulate and exacerbate blockage, significantly reducing the sweep efficiency of water flooding. The research results have clarified the pattern of water injection damage in the Chang 3 reservoir, providing theoretical guidance for water flooding development in oil fields.

In response to the issues of parameter homogenization and insufficient rationality in the support design of the mining roadway in the Lingtai mining area, it is highly significant to conduct classification research on the mining roadway and propose differentiated support strategies. Firstly, based on a systematic analysis of the geological conditions of the surrounding rock and the matching relationship with the support system in the mining roadway of the Lingtai mining area, six classification indicators were determined. Secondly, a mining roadway classification method based on mixed data clustering was proposed, extracting the informational characteristics of numerical and categorical indicators and introducing a penalty competition mechanism to dynamically optimize the number of clusters during the classification process. Subsequently, the proposed method was applied to classify 37 segments of the Lingtai mining area's mining roadways into four categories, validating the feasibility of the method. Finally, a field test was conducted in the D segment of the 2502 transport roadway in Shaozhai Coal Mine. The results indicate that the maximum convergence of the roof and floor of the test roadway segment is 109 mm, and the maximum convergence of the two sides is 113 mm, with a small range of surrounding rock damage. The dynamic classification and differentiated support method for mining roadways can achieve stable control of the surrounding rock in the roadways.

In order to study the reasonable sealing length of bedding gas drainage boreholes, based on the gas-air dual gas and the negative pressure attenuation effect of boreholes, a three-dimensional borehole drainage model was established by finite element simulation software to monitor and analyzed the gas pressure of coal seam in the sealing section. Through the field test of 20915 haulage roadway, the gas drainage effects of different sealing lengths were investigated. The results show that under the negative pressure attenuation effect, the negative pressure of borehole drainage is exponential function distribution, and the reduction of gas pressure in coal seams at different positions of borehole is different, and the closer to the sealing position, the smaller the reduction of gas pressure. The peak gas pressure in the sealing section is proportional to the sealing length. According to the field test, the gas drainage concentration decreases to about${10}\%$after 130 days when the sealing distance is${10}\mathrm{\;m}$. The sealing length of${20}\mathrm{\;m}$maintains at about${30}\%$, and the extraction concentration of the borehole with sealing length of${30}\mathrm{\;m}$maintains at above${60}\%$after 130 days.

In order to study the stability of the tunnel structure under the airport runway when the aircraft is running, the numerical calculation model of the runway, soil layer and tunnel structure co-deformation was constructed by establishing the six-degree-of-freedom dynamic equation of the “five-point contact” aircraft. The influence of the aircraft type, pavement type and buried depth on the stability of the tunnel structure was analyzed. The results show that the more concentrated the distribution position of the main landing gear wheel is, the greater the influence on the stability of the tunnel structure under different aircraft loads. The attenuation effect of stress diffusion on the flexible tunnel surface is obviously better than that on the rigid tunnel surface, and the deformation degree of the arch top is more significant than other locations. With the increase of the buried depth of the tunnel structure, the disturbance effect of aircraft load on the tunnel structure shows a decreasing trend. When the buried depth of the tunnel structure exceeds${64}\mathrm{\;m}$, the tunnel structure no longer bears the disturbance effect of aircraft load.

To investigate the deformation mechanism of underground electrical conduits in soft soil considering the softening effect under traffic loads, a USDFLD subroutine was developed based on the dynamic modulus attenuation model. This subroutine was imported into ABAQUS software to establish a three-dimensional finite element model of electrical conduits buried in soft soil foundations. The finite element method was used to analyze the dynamic response of underground electrical conduits under traffic loads. The effects of different traffic load magnitudes (${50}\%$full load,100% full load,200% overload,300% overload) and burial depths (800,850,900,950mm)on the mechanical properties of electrical conduits were studied. The results show that the softening effect of soft soil has a significant impact on the dynamic response of electrical conduits under traffic loads. As the traffic load magnitude increases, the settlement of the electrical conduit gradually increases, and the strain at the bottom of the conduit shifts from symmetric to asymmetric distribution, with an increase in the strain concentration area. Increasing the burial depth of the electrical conduit can significantly reduce the impact of traffic loads on the conduit. When the burial depth increases from${800}\mathrm{\;{mm}}$to${950}\mathrm{\;{mm}}$, the vertical displacement decreases by${39}\%$. The research results provide a scientific basis for the design and construction of power pipes in soft soil areas, and help optimize the depth of pipe embedding and cope with the influence of different traffic loads.

Given the prevalent issue of aging assets within power grid companies and the subpar management of aging equipment, a decision-making approach was introduced for optimizing the decommissioning of aging assets, taking into account the influence of transmission and distribution tariffs. Firstly, in accordance with the policy framework for reforming transmission and distribution tariffs, an accounting model for transmission and distribution tariffs for provincial power grids operating at varying voltage levels was developed. Secondly, the decommissioning planning model for over-age assets was established with the optimization objective of achieving the highest return throughout the investment cycle. The decision variables of time and scale for decommissioning were utilized in this model. Through a process of rolling optimization and feedback correction, the model ensured that the scale of decommissioning for overage assets in each stage aligns with the desired value, thereby meeting the premise of system reliability. Finally, the efficacy of the model and its ability to enhance investment efficiency were demonstrated through the examination of the${110}\mathrm{{kV}}$voltage level grid in a specific province.