Frost damage is a key problem in the construction of alpine tunnels. A numerical model was established to consider heat conduction and convection for designing effective thermal insulation measures. Orthogonal test conditions were designed to analyze the significance of different thermophysical parameters on lining temperature and their influence on lining damage. The results show that the convection coefficient, the thermal conductivity of the insulation layer, and the thermal conductivity of the surrounding rock have significant influences. The thermal conductivity of the insulation layer has the most significant impact. As its thermal conductivity increases, the lowest lining temperature decreases, and the damage level increases. An increase in the convection coefficient decreases the minimum lining temperature and increases damage. Higher thermal conductivity of surrounding rock raises the minimum lining temperature, spreading damage from the vault to the sides. Reducing the thermal conductivity and convection coefficient of the insulation layer, while increasing the thermal conductivity of the surrounding rock, improves overall damage distribution.

In order to do a good job of emergency management in the airport sector, strengthen the construction of the emergency response system, and improve the emergency response capability, a decision support method based on hybrid reasoning was proposed for the disposal of airport emergencies. Firstly, the ontology model of airport emergencies was constructed by abstracting the emergencies and the disposal process in the airport based on the actual scenarios and official documents. Secondly, the hybrid reasoning combining rule-based reasoning and case-based reasoning was introduced for case retrieval, and case representation was performed for the constructed ontology model to construct a case database. Lastly, the retrieval results are corrected using a feature weighting algorithm for attribute trade-offs, and the attribute parameters were adjusted using a neural network-based weight parameter optimization strategy. The advantages of the Bert+LSTM combination in this task scenario were verified by comparing it with commonly used deep learning models, and the final example proves that when an emergency occurs, the model can focus on the emergency itself, refer to historical cases and disposal standards, and obtain a structured data that comprehensively describes the information and disposal measures of the emergency, which provides support for emergency disposal decision making.

Tahe Oilfield was one of the important oilfields in western China. The monitoring and development of remaining oil reserves were paid significant attention to and were regarded as one of the current challenges in oil and gas exploration and development. Well-to-surface time-frequency electromagnetic detection technology, due to its sensitivity to oil-gas-water interfaces, was increasingly applied in oilfield development. However, as time-frequency electromagnetic detection technology was introduced relatively late in oil and gas detection, the related detection mechanisms and high-resolution signal extraction and interpretation were still under research. Through the analysis of well-to-surface time-frequency electromagnetic exploration principles, a technical sequence for differential apparent resistivity, differential phase processing, and imaging analysis of well-to-surface time-frequency electromagnetic data was established, starting from the definition of regional apparent resistivity. The differential processing results of time-frequency electromagnetic data collected from the actual production well TX1 in Tahe Oilfield demonstrated that the high-density sampling provided by multi-pole transmission and multi-frequency reception of well-to-surface time-frequency electromagnetic detection technology offered high-resolution reservoir detection datasets. The new differential data processing technology could greatly enhance the effective identification capability of deep oil-gas-water interfaces. The successful trial of this method provided a promising technical means for oil-gas-water detection in oilfield development. Additionally, through comparative analysis with seismic exploration results, it was found that well-to-surface electromagnetic exploration had unique advantages in identifying complex underground structures and could provide complementary information to seismic exploration.

Wood consists of a number of early and latewood alternately, in order to predict the strength of the mechanical properties of the specimen according to the proportion of early and latewood, a composite material model with early and latewood as the basic modeling unit was established. Starting from the fine level of wood, the wood is regarded as a composite material ideally bonded by two kinds of materials with different mechanical properties of early and late wood. Taking Yunnan pine as the research object, the respective mechanical parameters of early and late wood were obtained respectively, and according to the theory of composite plywood, the composite material tensile strength model of early and late wood with smooth grain was set up, and the test is utilized to verify the results. The results show that it is feasible to consider the wood as early and latewood composite material and use the laminate theory to predict its mechanical properties. The relative errors between the theoretical values of tensile elastic modulus and tensile strength and the experimental values are within 10%, which is highly reliable, and according to this model, the tensile strength and elastic modulus can be calculated through the measurement of the volume fraction of the latewood in the material, which will provide a good basis for the subsequent early and latewood tests and the further study of the early and latewood related models. This model can provide a reference basis and theoretical foundation for the in-depth study of the subsequent early and late material test and the related model of early and late material.

In order to study the prediction and health management of PEMFCs(proton exchange membrane fuel cells) for vehicles, a method combining GWO(grey wolf optimizer) and RBF(radial basis function) neural network with relative power loss rate as a health indicator was proposed to predict the remaining useful life of vehicular PEMFCs. Firstly, by analyzing the polarization curve of the fuel cell at the initial moment, a calculation method based on the relative power loss rate as a health indicator was constructed, and its feasibility was verified using the grey correlation analysis method. Then, the RBF neural network optimized by GWO algorithm was applied to predict the remaining useful life of vehicular PEMFCs. Finally, the proposed method was validated using two datasets. The results show that compared with other methods, the GWO-RBF method proposed in this paper has the smallest average absolute percentage error and root mean square error, the largest coefficient of determination, and a relative error of less than 1%. It is concluded that the proposed method can be used to predict the remaining useful life of vehicular PEMFCs with fewer datasets and better accuracy.

Addressing challenges such as large memory footprint, high computational complexity, and insufficient real-time detection speed in road crack detection models for complex scenarios, a highly efficient and precise algorithm named FCG-YOLO was proposed. Lightweight modules and attention mechanisms were integrated, and traditional feature fusion pyramids were enhanced.The algorithm incorporates PConv into the residual calculation module of YOLOv8n to introduce the improved C2f_Faster structure, thereby reducing model parameters and computational complexity. To enhance detection accuracy, GAM(global attention mechanism) was introduced into the backbone, and the Feature Fusion Pyramid SPPF was improved to SPPFCSPC module, enhancing the model’s ability to represent and fuse features of road cracks.The impact of each module on algorithm performance was verified through ablation experiments, identifying a lightweight and accurate model configuration. Furthermore, the robustness and generalization of the algorithm were explored in practical application scenarios.FCG-YOLO demonstrates outstanding detection efficiency, achieving a detection accuracy of 90.3% mAP50 and 74.4% mAP50-95 on the validation set, with a detection speed of 345 frames per second. These results highlight its high detection efficiency and significant practical value.

The performance of supercritical multi-thermal fluid injection in heavy oil reservoirs is markedly superior to that of steam flooding. However, the underground seepage law of injecting supercritical multi-thermal fluid is not yet clear. Therefore, the “high-temperature and high-pressure steady-state method” was proposed to test the oil-water and oil-gas relative permeability curves at different temperatures. The viscosity of produced heavy oil and the contact angle between oil sand and water at different temperatures were tested, and finally, combined with the oil-water, oil-gas relative permeability and Stone-Ⅱ prediction model, the isoperms of oil phase relative permeability in different hot areas during three-phase seepage were obtained. The results show that after the action of supercritical water on heavy oil, the measured viscosity of produced heavy oil decreased by 31.03% at 50 ℃ compared to steam, and the contact angle between oil sand and water decreased from 139.5° to 100.9°, indicating that the wetting properties of the oil sand develop towards a water-wet direction. Compared with the relative permeability of oil phase, the relative permeability of water phase is very small, and the characteristic value of oil-water relative permeability curve changes gradually and then suddenly at supercritical temperature. The relative permeability of oil-gas increases gradually with the increase of temperature. In the isoperms of oil phase relative permeability, the area of the oil flow zone expanded as temperatures rose, and under supercritical conditions, the flow zone area grew to 54.59%, highlighting a significant enhancement in oil-phase flow capacity. The research results of this paper can provide theoretical basis for the seepage mechanism and numerical simulation of supercritical multi-thermal fluid injected in heavy oil reservoirs.

In order to investigate the heat and mass transfer of liquid droplet impinging on a heated surface with different wettability, VOF(volume of fluid) numerical simulation method was used to analyzed the mechanisms of wall wettability and surface temperature on droplet morphology and heat transfer characteristics. The results show that the hydrophilic wall is favorable for droplet spreading, while the hydrophobic wall is favorable for droplet rebound. With the increase of contact angle, the maximum spreading factor of droplets decreases, the time to reach the maximum spreading factor is shortened, and the average heat flux of the wall surface decreases. Surface temperature has less influence on the droplet spreading stage, with the increase of surface temperature, the droplet phase transition rate accelerates, the average heat flux of the wall surface increases, and the Leidenfrost phenomenon occurs when the surface temperature exceeds the critical temperature.

Aiming at the problems of poor disturbance immunity and long dynamic response time of the grid-connected inverter based on VSG(virtual synchronous generator) control, an improved fuzzy adaptive control strategy for VSG was proposed. First, a small-signal model of VSG was established to analyze the effects of virtual inertia and damping coefficient on the dynamic response of the system. Determine the value range of the two parameters and use the sparrow search algorithm to optimize the initial values of inertia and damping in the adaptive strategy. Next, the angular frequency change curve of the system after perturbation was analyzed to refine the design fuzzy rules. Finally, a stand-alone VSG model was built in MATLAB/Simulink to compare the different control strategies. The results show that the fuzzy adaptive strategy proposed in this paper not only improves the response speed of the system, but also has a strong anti-disturbance ability when the command power and load power change suddenly. The effectiveness of this paper's strategy is proved.

The Kezirto pluton is located in the alkaline intrusive rock belt on the northern margin of the Tarim Basin. The lithology is alkaline granite. The petrogeochemical characteristics were studied in detail. The U-Pb isotope age of the pluton was measured by LA-ICP-MS method, which provides a basis for the study of the characteristics, age, genesis and formation environment of the pluton. At the same time, the nature of late Paleozoic magmatic activities and their tectonic settings in the area were studied, which can better guide the exploration work in the area and have important theoretical and practical significance. The Kezirto granite pluton was divided into two parts, north and south. The total rare earth element content is relatively high. The ∑REE of the northern pluton was 261.723×10^-6~834.783×10^-6, and the ∑REE of the southern pluton is 422.174×10^-6~575.86×10^-6. HFSE(high field strength elements) are obviously enriched relative to LILE(large ion lithophile elements), and elements such as Ba, Sr, P, and Ti are obviously depleted, with obvious negative Eu anomalies. The chondrite-normalized rare earth element diagram shows a slightly right-inclined seagull-like shape, and light rare earth elements are slightly enriched compared to heavy rare earth elements. The Kezirto pluton is A1-type granite. The LA-ICP-MS U-Pb isotope ages of the north and south plutons are (273.8±2.9) Ma and (274.8±1.8) Ma, which are contemporaneous plutons. The formation of the Kezirto pluton is due to the underplating of mantle-derived magma, which leads to the remelting of ancient rocks in the lower crust. After that, it undergoes fractional crystallization. During the formation process, it is contaminated by the crust to different degrees. The pluton has experienced the tectonic environment of deep mantle plume and intracontinental rift.