In order to explore the application of carbon fiber composite in portable weapons, the technical research on the structural properties of the launcher and the method of composite laminating was carried out. The working pressure of a certain type of launcher was obtained through experiments, and different carbon fiber composite layering models were established by using finite element numerical simulation method. Then the mechanical properties of the launcher under working load were studied. The results show that the layering method of carbon fiber has a great influence on the stiffness and damage failure of the launcher. Under the condition of the same working pressure, the same number of layers and the total thickness, the maximum deformation of the launcher structure is 2.41 mm in the layering mode Ⅰ. The maximun deformation of mode Ⅱ is 7.66 mm, and the latter is more than 3 times of the former. According to the Hashin failure criterion, there is no damage in the carbon fiber cylinder of the mode Ⅰ, except for the 6th to 12th fiber layers in the middle of the cylinder according to the mode Ⅱ, a large range of initial damage points are found in other layers. The research results can provide reference for the stucture design of the launcher and the carbon fiber composite layering scheme.

In order to avoid wellbore failure caused by abnormal annulus band pressure and resulting safety accidents, the annulus band pressure value is accurately predicted, and preventive measures are taken in advance when it exceeds the control value. An autoregressive integrated moving average-long short term memory (ARMI-LSTM) model was proposed. The model was trained to predict the annular band pressure of example wells based on actual annular band pressure time series data and feature capture data sets, and compared with a single model and recurrent neural network (RNN) model. The results show that the model has a good performance in error, fitting accuracy and overall performance after training with actual data, which can provide a reference for improving the prediction accuracy and efficiency of annular band pressure value, and is helpful to well integrity design.

In order to explore the durability of microbially induced calcite precipitation(MICP) technology to improve the durability of aeolian sandy soil materials, 0.08% polymer absorbent resin (MICP+A) and 0.37% xanthan gum (MICP+B) were used to improve the traditional MICP materials. The microstructure of different cycles of high and low temperature cycle and ultraviolet irradiation was studied by nuclear magnetic resonance technology, and the durability of mineralized sandy soil materials was investigated. The results show that the porosity of both MICP+A and MICP+B materials increases with the increase of cycling cycles. In 20 cycles of high and low temperature cycling tests and 15 cycles of ultraviolet irradiation tests, the MICP+A material shows good stability, and the porosity increment decreases by about 1.8 times and 1.1 times, respectively, compared with the conventional MICP material. Under high and low temperature cycling and ultraviolet irradiation, the crystal structure of calcium carbonate is altered and the percentage of medium-sized pores in the soil increases, causing the 2nd peak of the T₂ spectra of all three materials to be higher than the pre-test peak. The test shows that the polymer water-absorbing resin can improve the stability performance of the traditional MICP specimens, and this study provides a basic experimental basis for the engineering application of microbial mineralized geotechnical materials in the treatment of desert areas.

Aiming at the problem of redundancy in the phase space reconstruction of sample entropy algorithm, the phase space reconstruction process of sample entropy algorithm was replaced by a symbolic variable matrix, and an improved sample entropy algorithm was established. The analysis of white noise and powder noise simulation signals shows that the improved sample entropy algorithm can extract signal features effectively and has high computational efficiency. In the past, bearing clearance faults of complex compressors were studied, and the improved sample entropy algorithm was applied to extract features and compared with sample entropy. The feature extraction results of the method are highly consistent with the sample entropy algorithm, and the computational efficiency of the algorithm is much higher than that of the sample entropy algorithm.

A numerical calculation model for airfoil dynamic stall numerical simulation was established by computational fluid dynamics method, and the influence of plunging motion on airfoil unsteady aerodynamic force was analyzed. The simulation results were compared to the pitching motion wind tunnel experimental data of NACA0012 airfoil, and the results of the model under mild stall and deep stall conditions were in good agreement with the experimental values, which verified the accuracy and feasibility of the numerical calculation model. The plunging motion of NACA23012 airfoil was equivalent to the pitching motion, the lift characteristics of airfoil under the two motion modes are very close, but the moment characteristics are obviously different. With the increase of amplitude of plunging motion and inflow Mach number, the difference of moment characteristics is further expanded. With the increase of amplitude of plunging motion, the damping effect of aerodynamic moment of pitching and plunging motions is obviously enhanced, with the increase of inflow Mach number, the damping effect is reduced, and the moment divergence occurs when the Mach number is 0.85.

Compared to image instance segmentation in general scenes, instance segmentation in complex stacked scenes is affected by complex situations such as severe occlusion and stacking of similar objects, making instance segmentation more difficult. To solve the problem of garbage instance segmentation in complex stacking scenarios, an instance segmentation algorithm combining YOLOv8 and two-layer feature network strategy was proposed. Firstly, the feature data was layered in the data preprocessing part, and the two-branch feature fusion was realized through the graph convolutions network (GCN), which reduces the influence of stacking on the features of the occluded objects, thus solving the instance segmentation problem under complex stack occlusion. At the same time, in order to solve the problem that similar objects are easily confused, a soft threshold non-maximum suppression algorithm and a new intersection ratio algorithm were integrated. Finally, according to the complexity of application scenarios and data sets, the feature extraction module of the backbone network was optimized, and the multi-scale attention mechanism was introduced in the backbone network, which effectively improves the detection performance of the model. In the experiment, examples of occlusive garbage classification were used to segment the dataset. The experimental results show that this method outperforms other methods in terms of average accuracy, average accuracy when the intersection to union ratio threshold is 0.5 (AP₅₀), and average accuracy when the intersection to union ratio is 0.5~0.95 (AP_50~95). Compared with the original YOLOv8 algorithm, the detection AP₅₀is increased by 7.9% and the segmentation AP₅₀ is increased by 5.4%, which has better detection and segmentation effects.

The drainage consolidation characteristics of municipal sludge are closely related to its water occurrence form. However, there is insufficient understanding of the water transformation law of municipal sludge after chemical conditioning and consolidation. Based on the theory of soil science, the soil-water potential curves of different types of municipal sludge were tested by centrifuge method, and the water forms of municipal sludge were divided into bound water, capillary water and gravitational water according to the range of soil-water potential. On this basis, the different forms of water content in the original sludge, consolidated samples and conditioned sludge samples were compared to reveal the water transformation law of municipal sludge under the action of consolidation and chemical conditioning. The results show that the bound water content of the sludge is reduced by 70%~80% and the free water content is doubled after the sludge is modified by ionic salt. Under the consolidation pressure of 3.1 kPa, only a part of gravity water is discharged from different types of municipal sludge, and the contents of capillary water and bound water are basically unchanged. Under the consolidation pressure of 100 kPa, the gravity water is completely discharged, the capillary water is significantly reduced, and the bound water is slightly reduced.

The Permian Wutonggou Formation in Dixi area of Junggar Basin has huge potential for oil and gas exploration. Based on the latest seismic data, combined with thin section data, drilling and logging data, rock physical properties and physical parameters, using seismic forward modeling, wave impedance attributes and waveform clustering attributes, the basic characteristics of the reservoir were characterized, the seismic waveform identification method of the upper and lower sand groups in the first member of Wutonggou Formation was clarified, the thickness distribution law of the upper and lower sand groups was described, and the sedimentary facies development characteristics of the upper and lower sand groups were clarified. The results display that the sandstone of Wutonggou Formation is mainly lithic sandstone and feldspar lithic sandstone, which belongs to low porosity and low permeability reservoir. The seismic waveform characteristics of sand bodies are significantly affected by seismic resolution, sand body thickness, mudstone interlayer thickness, sand body superposition relationship and underlying lithology. The sand body distribution regular pattern based on the interpretation of the sand body waveform characteristics of the forward model is highly consistent with the average wave impedance attribute distribution regular pattern. The study area develops delta front underwater distributary channel microfacies, estuary dam microfacies, underwater tributary bay microfacies and sheet sand microfacies. There are great differences in the development characteristics of sedimentary facies between the upper and lower sand groups.

The low-load one-time construction method of PVC micro-pipe jacking was applied in China and is in its infancy. The small stiffness of the flexible PVC pipe ring leads to the redistribution of soil pressure and the generation of elastic resistance during the jacking process. The jacking force can not be accurately calculated by the existing specifications. The principle and construction method of the PVC micro-pipe jacking method were used to investigate. At the same time, the influence of vertical deformation of PVC pipe on soil arching effect and the additional frictional resistance caused by horizontal deformation were considered based on Terzaghi soil pressure. The corresponding jacking force calculation formula was derived on this basis. Furthermore, the factors that influence the jacking force of PVC pipes were analyzed through finite element numerical simulation. The results indicate that the pressure around the PVC pipe will increase due to the deformation of the pipe. The maximum error between the predicted lower limit and the measured value of the jacking force calculation model proposed is 15%. The measured value is between the predicted upper limit and the lower limit, which proves its applicability. The parametric analysis indicates that the jacking force increases by 1.6 times as the pipe diameter increases by 100 mm and the jacking force increases by about 3 times when the buried depth of the pipeline increases from 4.5 m to 6 m.

Weld defects present within pipelines constitute a considerable threat for leakage and rupture accidents. To elevate the detection precision of these defects, X-ray inspection was employed as a means to identify and locate them with greater accuracy. However, the diverse types, small sizes, and complex backgrounds of weld defects posed challenges for accurate detection. To address the limitations of current deep learning-based models, such as inadequate adaptability to complex backgrounds and lighting variations, as well as poor performance in detecting small targets, an improved faster region convolutional neural networks(Faster R-CNN) network model was investigated. This model incorporated a channel attention mechanism into the backbone network, modified the residual block structure, and employed ROI Align to replace the traditional ROI Pooling. The results show that compared to the original algorithm, the improved Faster R-CNN model achieves significant improvements in mean average precision (mAP) and F₁, with respective increases of 15.82% and 16.44%. It is concluded that this improved model can meet the high-precision requirements for weld defect detection and holds significant theoretical importance as well as promising prospects for engineering applications.