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.

Lightning is the main cause of active distribution line fault. It is of great significance to study the lightning risk assessment of active distribution network. The distribution line with distributed photovoltaic system in Nanjing area was taken as the research object. The calculation model of lightning overvoltage on distribution lines with distributed photovoltaic system was established, and the interaction between photovoltaic side and distribution line side during lightning strike was analyzed. The electrical geometric model on both sides was constructed. The trip rates of the photovoltaic side and the line side were calculated, and the risk was evaluated according to the calculation results. The results show that when lightning strikes the nearest tower on the photovoltaic side, the lightning trip-out rate on the photovoltaic side increases from 27.52 times/(100 km·a) to 29.63 times/(100 km·a), and the lightning risk is higher. When the photovoltaic side is struck by lightning, the tripping rate of the adjacent three towers affected by the lightning intrusion wave is doubled, and the risk level is also higher.

In order to address the challenges posed by multifaceted risk factors in air traffic control operations, a comprehensive analysis of unsafe operational incident reports was performed to extract risk-related information and identify underlying patterns. The latent Dirichlet allocation (LDA) model was utilized to uncover key risk topics and associated keywords, and the evolutionary relationships among different risk themes were systematically analyzed. A semantic network for the civil aviation air traffic control domain was constructed using the bidirectional encoder representation from Transformers(BERT) model to examine the interconnections and potential dependencies among risk topics. This network provides a theoretical foundation for quantifying the association between keywords. The findings indicate that the proposed approach enhances the digital representation of safety risks in air traffic control operations. It is concluded that the results offer valuable insights for advancing risk assessment and mitigation strategies in civil aviation air traffic control systems. The relevant research results can better mine air traffic control unsafe information and lay a foundation for accurately perceiving air traffic control operations risks.

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.

Studying the characteristics of airport traffic flow fluctuation range is fundamental for efficient traffic management and control. Mastering these characteristics plays a crucial role in maintaining the stability and effectiveness of overall airport operations. Considering the irreversibility of time and the cumulative impact of traffic congestion, which occurs when airport traffic exceeds facility capacity within certain time intervals, a method for constructing an adaptive crossing network was proposed. From the perspective of complex network topology, both the overall characteristics of the network and the centrality of nodes were analyzed. The integrated centrality of nodes was calculated using the independent weighting coefficient method, enabling the identification of key time nodes that are core hubs of strong fluctuations within the network. The results show that the adaptive crossing network, mapped based on the traffic data from Beijing Daxing International Airport, exhibits characteristics of complexity and order, featuring scale-free properties, assortativity, and a distinct community structure. The time period from 21:20 to 22:25 (nodes 257~269) ranks highly across various centrality measures, indicating a significant fluctuation impact range, and thus, these nodes are identified as core hub nodes within the network. The integrated centrality synthesizes various topological centrality features of the network, and through quantitative analysis, effectively characterizes the strong fluctuation nodes within the network. This method provides a theoretical basis and practical reference for the optimization of airport traffic flow management and the study of abnormal fluctuations, offering a new perspective for enhancing airport operational efficiency and safety.

The natural orifice intervention using continuum robots faces challenges such as tortuous and narrow intervention paths, as well as compressive forces exerted by soft tissues in the orifice. To address the issue in the delivery process where existing planning methods struggle to balance multiple control objectives, resulting in difficulty in reaching deeper positions, an autonomous planning scheme based on residual reinforcement learning was proposed. The method enables the autonomous delivery of continuum robots through natural orifices. A feedback deviation model between the delivery posture of the continuum robot and the spatial state of the natural orifice was established to control the posture target during the delivery process. Simultaneously, a Markov model of the overall motion process of the continuum robot was constructed to train the reinforcement learning algorithm. A residual strategy, generated by combining posture feedback control with reinforcement learning control, was used to output the optimal actions for the continuum robot's delivery process. Experiments conducted in a simulated bronchial orifice show that the proposed method converges over 60% faster than existing methods and can plan smooth, collision-free trajectories for the continuum robot's intervention through the orifice, outperforming existing methods in several key metrics.

Vertical joint steel anchor ring grouting connection (referred to as vertical seam tight splicing connection) shear wall system uses steel anchor ring connection for vertical joints, followed by secondary grouting connection. Based on actual projects of the enterprise, the feasibility of vertical joint tight fitting connection has been verified through experimental research, seismic performance analysis, and design and construction practice.

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.

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.

The operation of the Three Gorges Reservoir(TGR) generated a high amplitude of hydro-fluctuation belt(HFB). The preservation and restoration of the HFB had become a major scientific issue after water storage. The classification of bank slopes is the basis for carrying out the protection and restoration of HFB. Taking four typical drinking water sources of the TGR as the research objects, firstly, based on GF-2 remote sensing images covering the study area, and on the basis of radiometric calibration, orthoscopic correction, atmospheric correction, etc., combined with the samples of different bank slope types in the HFB obtained by UAV shooting and visual interpretation, and an object-oriented method for identifying bank slope types in the HFBa was constructed. Secondly, combined with random forest, support vector machine and neural network methods, the classification of bank slope types of typical water sources was carried out, and the classification effect of different machine learning methods was compared to realize the accurate identification of bank slope types in the HFB of typical water sources. Finally, the influence of pixel oriented and object oriented strategies on the classification accuracy of the bank slope in the fall zone was analyzed. The results show that the classification of bank slopes based on multiresolution segmentation-object-oriented classification is a convenient, cost-effective method, and has high accuracy. It can be used for classification of bank slope types in the large-scale HFB of the TGR. This method can solve the problems of internal spectral heterogeneity and increased homogeneity between objects in high-resolution remote sensing images, effectively improving the accuracy of slope classification.The study was of great significance in promoting ecological protection, restoration, and management of the HFB in the TGR, and maintaining important ecological security barriers in the Yangtze River Basin.