Based on the actual welding process of rectifier sample structure, the numerical simulation of high temperature vacuum nickel-based alloy brazing process was carried out, and the flow model of melting-wetting filling weld of nickel-based alloy filler metal in high temperature brazing vacuum furnace was established. The numerical calculation was based on the volume of fluid(VOF)method. Considering the factors such as surface tension, gravity, and latent heat of phase change, the laminar flow model was used to solve the flow behavior of the solder with time. The distribution of solid and liquid solder and the distribution of temperature field were calculated. The phenomenon of braze material loss and weld bead formation on the surface of the base material, along with a good filling effect of the braze material inside the weld seam, verifies the rationality of the arrangement of solder and temperature control in this brazing process. An empirical formula for the volume ratio of braze material filling the weld seam with respect to time and temperature has been provided, offering guidance for actual brazing processes.

The reservoir of Yanchang Formation in the southwest Ordos Basin is one of the typical tight sandstone reservoirs in China. The classification and evaluation of tight sandstone reservoir of Yanchang Formation in central Ordos Basin has attracted the attention of petroleum enterprises. In order to rationally and efficiently develop the Chang 8₁ tight sandstone reservoir in Ordos Basin, it is urgent to carry out reservoir classification and accurately evaluate the reservoir. Taking the Chang 8₁ tight sandstone reservoir in Wuqi area as an example, the fractal dimension of the target layer was calculated by NMR experimental data, and the micro-pore structure of the Chang 8₁ tight sandstone reservoir was quantitatively studied by combining the experimental data such as cast thin slice and scanning electron microscope. At the same time, the relationship between reservoir fractal dimension and reservoir physical properties was analyzed, and the fractal theory based on nuclear magnetic resonance experiment was used to classify and evaluate tight sandstone reservoirs. The results show that the rock type of Chang 8₁ tight sandstone reservoir in Wuqi area is mainly lithic feldspar sandstone, and the pore structure is mainly intergranular dissolved pores and feldspar dissolved pores. According to the T₂ spectral curve and fractal dimension method, reservoirs are divided into three categories. Each type of reservoir corresponds to the relevant fractal dimension, and the fractal dimension has a good correlation with the physical property parameters, which characterize the complexity of the reservoir pore structure. Combining nuclear magnetic resonance experiment with fractal dimension method to quantitatively characterize the microscopic characteristics of tight sandstone reservoir can improve the accuracy of reservoir classification.

Logging data constitutes the basis for oil and gas field development and evaluation. However, in actual mining, factors like poor wellbore stability and equipment failure give rise to the distortion or loss of logging data. A prediction model based on variational mode decomposition (VMD) was proposed to address the issues of unstable and inaccurate results in existing prediction models. The model combines convolutional neural networks (CNN), bidirectional long short term memory (Bi-LSTM), and attention mechanism to predict missing sections in well logging curves. With logging sequence data as input, the VMD algorithm was employed to decompose the sequence into a series of amplitude-modulated and frequency-modulated signal subsequences. The features were extracted by the CNN network and trained by the Bi-LSTM network. During training, the Attention mechanism was utilized to learn the importance weight of each time step dynamically. Finally, the predicted value of the logging curve was outputted. The method was applied to predict logging curves in the Biyang Block of Henan Province and compared with other common machine learning prediction models. The results show that the application effect of the CNN-BiLSTM-Att model improved based on VMD is remarkable, with an error of only the order of 10^-3 and a prediction accuracy of 92.02%. The research results provide new ideas for accurate prediction of logging curves.

The in-stage multi-cluster fracturing technology in horizontal wells is a key method for the efficient development of unconventional oil and gas. Due to the influence of reservoir heterogeneity, non-uniform perforation erosion, and fluid competition, the in-stage multiple perforation clusters cannot initiate simultaneously and propagate evenly in Mahu conglomerate Jinlong 2 well block reservoirs. The perforations with the excessive fluid intake can be plugged by injecting the ball-shaped or knot-shaped diverters. The sand carrier fluids can be dynamically distributed. In this way, the fracturing stage can be evenly stimulated and the reservoir productivity can be fully excavated. It is of great value to judge the temporary plugging effect accurately and timely. In this way, the operation scheme can be adjusted and the stimulation effect can be improved. The perforation plugging, the fracture initiation, and the fracture propagation were completely considered by integrating the three methods of diverter in-place pressurization, same displacement pressure boost, and curve superposition. The comprehensive discrimination method of the in-stage multi-cluster temporary plugging effect was generated and the supporting software was compiled to realize online monitoring and discrimination. The method demonstrates more than 85% consistent with the monitoring results of optical fiber and hawk-eye, which provides method and theoretical support for the upgrade and popularization of the temporary plugging technology.

As the oil and gas industry continues to expand into deeper layers and the development of deep geothermal resources progresses, the hardness of reservoir rocks increases, making rock breaking more difficult and raising development costs. To address the problem of difficult rock breaking in deep granite, the mechanism of electromagnetic radiation-assisted rock breaking was investigated. This method utilizes the interaction between electromagnetic waves and reservoir rocks to induce thermal stress damage or fracturing of the rock, thereby reducing rock strength and improving breaking efficiency. Firstly, based on the mineral composition of deep granite, a heterogeneous core model with random distribution of minerals was established. Secondly, a numerical model of electrothermal-solid-damage coupling of granite damaged by electromagnetic radiation was established. Finally, the electromagnetic field, temperature field, stress and damage distribution of granite under electromagnetic radiation were calculated by sequential coupling method. Due to the selective heating property of electromagnetic waves, the electromagnetic power loss density of biotite is 2~3 orders of magnitude higher than that of quartz and feldspar, resulting in different electromagnetic heating degrees and forming local hot spots near biotite. Based on the difference of temperature and thermal expansion coefficient of different minerals, a non-uniform stress distribution is formed. Quartz and biotite are strained, while feldspar is pressured. After 3 kW electromagnetic radiation for 5 min, the damage volume of granite is 42%, and the tensile damage of quartz is the main damage. The damage of granite under electromagnetic radiation is significant, the strength of rock is reduced, and the rock breaking of deep granite is promoted.

As the service life of China's oil and gas pipelines increase, the pipelines are inevitably subjected to defects such as corrosion and denting due to external factors. It becomes imperative that the impact of these defects, including denting and corrosion, on the ultimate bearing capacity of the pipelines be analyzed. A simulation was conducted on the safety of an X80 pipeline with dents and corrosion defects. By changing factors such as dent length, width, depth, and material type, the influence of a single dent area under the action of spherical and ellipsoidal pressure heads on the ultimate bearing capacity of the pipeline was analyzed. At the same time, the influence of various factors such as the size and spacing of dents and dents on pipeline safety was studied when composite defects such as dents and corrosion coexist. The results show that the length of dents, corrosion length, and corrosion depth are important factors affecting the ultimate bearing capacity of composite defects, and the damage to pipelines is greater when dents act in the vicinity of the corrosion center point.

In order to study the influence mechanism of energetic particles on the combustion and explosion reaction of solid-liquid mixed fuel, a 20 L spherical cloud combustion and explosion characteristics test system was used to study the combustion and explosion characteristics of solid-liquid fuel-air dispersion systems with different mass fractions of energetic substances. Under low concentration conditions, the explosion pressure, maximum pressure rise rate, reaction time and explosion lower limit of different fuels were measured. Based on the combination of solid-liquid dispersed particles, the impact of energetic particles on the combustion and explosion characteristics of solid-liquid mixed fuel was analyzed. The results show that in the 1,3,5-trinitroperhy-dro-1,3,5-triazine(RDX)/aluminum powder mixed fuel system, the explosion pressure and maximum pressure rise rate first increase and then decrease with the increase of RDX mass fraction, with the maximum values being 1 516.17 kPa and 116.17 kPa/ms respectively. For RDX/ aluminum powder/nitromethane mixed fuel system, the addition of RDX causes the explosion pressure to continuously decrease, reaching 427.99 kPa. When the RDX mass fraction is low, RDX inhibits the combustion explosion of the mixed fuel. At the same time, it was found that the change pattern of mixed fuel reaction time is completely opposite to the change pattern of maximum pressure rise rate.

In order to explore the dust deposition characteristics on the surface of photovoltaic(PV) modules. For the dust accumulation problem of ground-mounted photovoltaic, the deposition characteristics of dust particles with different particle sizes were investigated from two factors, namely, tilt angle and wind speed, by using CFD numerical simulation method. The results show that as the tilt angle of the PV module increases, the deposition rate of dust on the surface of the PV module gradually decreases, in addition, it is found that when the PV module is facing the wind, the rate of dust deposition increases with the increase of wind speed, and the maximum particle size of dust deposition will become larger with the increase of wind speed. At the same time, with the increase of particle size, the deposition of dust on the surface of the PV module is the first to increase and then decrease. The study is of great significance in solving the problem of dust accumulation in photovoltaic power plants.

The offshore environment is complex and volatile, and the combined wind and wave loads can generate large vibrations on the floating wind turbine platform and tower top, posing a serious threat to the structural safety of the wind turbine system. To cope with this challenge, a tuned mass damper (TMD) was installed in the nacelle of the barge floating wind turbine to form a hybrid mass damper (HMD) using active driving force. The H∞ algorithm was used for the drive force control. The effects of no control, passive control, and H∞ control were compared through simulation. The results show that the H∞ control can effectively reduce the longitudinal angle of the platform and the longitudinal displacement of the top of the tower, with obvious vibration suppression effects.

In order to smooth out the output fluctuation of wind power generation system, a hybrid energy storage dual-layer fuzzy control strategy based on wind power prediction was constructed by adjusting the control strategy of hybrid energy storage system (HESS) to meet the fluctuation limit of grid connection. Firstly, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) was used to decompose the original wind power data. Secondly, the improved Adam algorithm and Transformer model were combined to predict each component, and the prediction results are superimposed as the final prediction result. Finally, based on the predicted wind power fluctuation state and state of charge (SOC) of the hybrid energy storage system, the dual-layer fuzzy control strategy was adopted to adjust the hybrid energy storage system to ensure that the overcharge and overdischarge of the hybrid energy storage system are reduced under the premise of smooth grid connection of wind power. The results demonstrate that the proposed control strategy achieves lower fluctuation indices in wind power output, ensuring reliable grid connection. Moreover, it maintains the SOC of the HESS within an optimal range, leading to an overall enhancement in the system's comprehensive performance.