Power plants utilizing substandard coal for electricity generation often face reduced denitrification efficiency due to high fly ash concentrations in the flue gas, which complicates achieving ultra-low emissions. The flue gas duct from the economizer outlet to the riser duct entrance in the W-shaped flame boiler at Guizhou Chayuan Power Plant was investigated. Numerical simulations were conducted to analyze flue gas flow and fly ash particle trajectories. The results show that the economizer ash hopper is minimally effective, with a collection rate of only 9.99%. Simulation results reveale fly ash deposition on the wall at the SCR riser flue bend at 1.12 kg/s, representing 11.33% of total fly ash. Based on these findings, two design schemes for positioning a denitrification ash hopper below the riser flue are proposed to enhance fly ash collection efficiency. Optimal results are obtained with a 45° angle between the inclined surface and horizontal wall, increasing fly ash collection by 8.38% with a 2.25 Pa pressure drop increase.

In view of the problem of limited pressure relief and anti-impact ability of large diameter boreholes in high stress and strong disturbance coal seams. The methods of theoretical analysis, numerical simulation and field test were adopted to study the mechanism of pressure relief and anti-impact of large diameter boreholes, and the control measures of pressure relief and anti-impact of large diameter boreholes were proposed to improve the pressure relief and anti-impact performance of high stress and strong disturbance coal seams. The results show that the pressure relief range of large diameter boreholes is directly proportional to the drilling radius, surrounding rock stress, and disturbance stress increment, and inversely proportional to the constraint force of the fracture zone and plastic zone. Increasing the pressure relief range of large diameter boreholes can increase the attenuation of dynamic stress wave energy, transfer static load energy to the deep surrounding rock, and reduce the risk of coal seam rockburst. As the disturbance coefficient increases, the range of pressure relief for large diameter boreholes and the gathering elastic energy around the boreholes show an increasing trend. The vibration acceleration of borehole increases exponentially with the increase of disturbance coefficient and shock energy. When the disturbance coefficient is greater than 2, the impact dynamic load is superimposed, and the large diameter borehole is blocked to achieve the ultimate anti-impact ability. The use of dense drilling method and repeated drilling method can increase the pressure relief range of large diameter drilling, reduce the risk of coal seam rockburst. After field test, the deformation of surrounding rock of roadway is reduced by 31.5%~67.0%, the stress value of coal body is lower than the critical warning value, and the stability of surrounding rock of roadway is significantly improved.

As a large transportation hub, airport terminals have complex structures, and the evacuation efficiency becomes extremely important when an emergency occurs. To improve the evacuation efficiency of the terminal building, an improved A* algorithm has been proposed for selecting the optimal evacuation path based on the actual distribution of personnel. Firstly, the flow of personnel in the terminal building was simulated, and data on the distribution of personnel was obtained. Then the time-varying distribution of personnel in each area was considered in the cost calculation of path selection. Finally, the A* algorithm was improved in terms of traversal methods, network weights, other factors, and congested paths were replanned to avoid congestion. The results indicate that considering the distribution of passengers in the terminal improves the evacuation paths at each node, which leads to shorter evacuation time compared to traditional A* algorithm. It also allows for the avoidance of congested paths. This study can provide theoretical and methodological support for the rapid evacuation of passengers in the terminal under emergencies.

In order to further explore the pipe-soil interaction and the failure mechanism of buried pipelines during the collapse process, the element birth and death technology and the displacement load technology were used to simulate the soil collapse process. The coupling models considering the pipe-soil nonlinear effect were constructed respectively to analyze the failure law of buried pipelines under collapse. The results show that the bottom of the pipe jacking is the control point, the axial stress is the control stress, and the junction of the non-subsidence area and the subsidence area and the center of the subsidence area are dangerous sections. Applying displacement load technology to element birth and death technology, the location of the most dangerous section transitions from the junction of non-subsidence area and subsidence area to the center of subsidence area, and the peak value of Von-Mises stress increases from 4.3 MPa to 6.09 MPa. The application of displacement load technology makes the lower part of the pipeline always in contact with the soil, and the pipeline is subjected to strong shear near the junction of the non-subsidence area and the subsidence area. The element birth and death technology is lost with the soil, and the pipe-soil state gradually changes from contact to separation, which is a weak shear effect. The Von-Mises stress peak is larger and the radial stress is smaller, which can better reflect the actual situation. According to the stress and settlement values, the maximum Von-Mises stress prediction formula is fitted, and the error is within 7%. The research results can provide reference for the safe operation of pipelines and the selection of collapse simulation methods.

To address the challenges of high-dimensional features, large computational demand, and difficulty in improving the accuracy of secondary return water temperature prediction models for heat stations, a secondary return water temperature prediction model based on the xtreme gradient boosting-artifical neural network(XGBoost-ANN) was proposed. The feature screening layer uses XGBoost algorithm to calculate the importance scores of the original data features and determine the main features that affect the secondary backwater temperature, thus reducing the complexity of the model and improving the computational efficiency. Three layers of feedforward ANN were trained by Bayesian regularization algorithm as the secondary backwater temperature prediction layer, and the initial weights and thresholds of the ANN model were optimized by grey wolf optimizer (GWO) algorithm. The weights and thresholds of the ANN model were represented by grey wolf position vector. The fitness function was introduced to evaluate the performance of each set of weights and thresholds to help the model avoid falling into local optimality at the initial stage of training, so as to improve the performance and generalization ability of the model. Experimental results demonstrate that the constructed XGBoost-GWO-ANN secondary return water temperature prediction model achieved significant improvements. Compared to the model before feature filtering, the root mean squared error(RMSE) is reduced by 26.8%, the R² is increased by 11.3%, and the model inference time is shortened by 46.1%. Furthermore, the optimization of the initial ANN weights and thresholds using the GWO algorithm improve the RMSE by 20.0% and the R² by 3.4% compared to the unoptimized ANN model. These results indicate that the accuracy and generalization ability of the proposed prediction model are effectively enhanced.

Hydrothermal management technology is conducive to solving the problems of proton exchange membrane fuel cell(PEMFC), such as large heat dissipation demand, slow cold start, and short life. The dynamic response test of a high-power fuel cell was carried out, and the correlation between temperature and humidity and effective output voltage was verified by Pearson correlation coefficient. The influence of water management and thermal management on fuel cell was analyzed by literature review, and the current hydrothermal management and modeling methods were summarized. Water management methods mainly include reaction gas humidification, internal structure design, and drainage control, but it isn’t easy to achieve accurate online water content detection and closed-loop control. On the other hand, thermal management technology is relatively mature. The water cooling method of the traditional heat engine and the temperature control strategy is used to control the water pump and fan in the thermal management subsystem so that the temperature of the fuel cell and the temperature difference of inlet and outlet cooling water are kept in a reasonable range. However, there is a strong coupling between temperature and water distribution in the stack, so the single temperature variable and water variable study can not truly reflect the influence of temperature and water content on the performance of fuel cell. In the future, it is the key to improve the performance of fuel cell, effectively improve the parasitic power of appendage, and prolong its service life by using efficient hydrothermal coupling technology and considering the influence of temperature and water content comprehensively.

Currently, mainstream gait recognition methods often rely on stacked convolutional layers to gradually expand the receptive field and integrate local features. These methods mostly use shallow networks, which have limitations in extracting global features from gait images and lack attention to temporal cycle feature information. Therefore, a deep neural network algorithm combining Transformer and 3D convolution, named 3D convolutional gait recognition network based on AdaptFormer and Spect-Conv (3D-ASgaitNet)was proposed. Firstly, the initial residual convolution layer converts the binary contour data into a floating-point encoded feature map to provide dense low-level structural features. On this basis, the spectral layer enhances the feature extraction ability through the joint processing of frequency domain and time domain, and uses the pseudo-3D residual convolution module to further extract advanced spatio-temporal features. Finally, AdaptFormer module was integrated to provide flexible feature transformation capability through lightweight down-sampling and up-sampling network structure to adapt to different data distribution and task requirements. 3D-ASgaitNet was carried out on four publicly available indoor datasets (CASIA-B, OU-MVLP) and outdoor datasets (GREW, Gait3D), and achieved recognition accuracy rates of 99.84%, 87.83%, 45.32% and 72.12%, respectively. Experimental results show that the recognition accuracy of the proposed method in CASIA-B and Gait3D data sets is close to the performance of SOTA.

The oxidation of Sb(III) occurred rapidly in aerobic and dark groundwater environments, with previous studies suggesting that co-oxidation of Fe(II) and Sb(III) may be the predominant driving mechanism. However, there is a lack of field evidence confirming environmental isotope fractionation. Therefore, 20 groups of Magunao aquifer (${\mathrm{Dx}}_{3}^{4}$ water) samples were collected from the North mine of Xikuangshan antimony mining area in Hunan Province to compare the differences in environmental isotopes (δ⁵⁶Fe, δ¹³C, and δ³⁴S) between high- and low-Sb groundwater and investigate the fractionation process of these isotopes. The results reveal that total Sb(^TSb) concentrations ranged from 5.30 μg/L to 20 700 μg/L, with a mean concentration of 3 660.61 μg/L. Additionally, Sb(V) is found to be the most dominant valence state for Sb in ${\mathrm{Dx}}_{3}^{4}$ water. The neutral-alkaline and oxygen-enriched conditions in ${\mathrm{Dx}}_{3}^{4}$ water facilitate the co-oxidation of FeS₂ and Sb₂S₃, as well as induce fractionation of δ¹⁸${\mathrm{O}}_{\mathrm{S}{\mathrm{O}}_{4}}$, δ³⁴${\mathrm{S}}_{\mathrm{S}{\mathrm{O}}_{4}}$and δ⁵⁶Fe between sediments and groundwater, resulting in the increase of $\mathrm{S}{\mathrm{O}}_{4}^{2-}$,total Fe (^TFe) and Sb(Ⅴ) contents in high Sb groundwater. Furthermore, microbial activities promote the oxidative decomposition of organic carbon, thereby enhancing the co-oxidation rate of Fe(Ⅱ) and Sb(Ⅲ). This conclusion unveils a novel mechanism for aerobic oxidation of Sb(III) in dark groundwater environments while providing a scientific foundation for advancing our understanding of the Sb geochemical cycle and preventing environmental pollution from high Sb groundwater.

The prediction of dynamic stress-strain relationship of composite geomembrane is one of the key problems involved in the long-term operation of seepage prevention projects of earth-rock dams. In order to study the effect of cyclic loading on the stress and strain of the composite geomembrane, cyclic loading and unloading tests were carried out under three different stress levels of 10%, 20% and 30%, and the creep tests under 10% to 80% stress levels were carried out to compare and analyze the creep and creep recovery characteristics of the composite geomembrane. The expression of strain under cyclic loading and accumulation condition was presented, and the constitutive model of composite geomembrane boundary surface under cyclic loading condition was constructed. The results show that the deformation of composite geomembrane increases instantaneously at the moment of loading and unloading, and then gradually slows down with the increase of time. After complete unloading, when the stress level is less than 30%, the deformation of the composite geomembrane can almost recover. When the stress level is greater than or equal to 40%, the residual deformation increases gradually with the increase of the stress level. The effect of cyclic loading times on the creep deformation of geomembrane is not a simple linear relationship. In addition, an improved three-parameter viscoelastic model was established, and it is verified that the model can reproduce the creep recovery process of composite geomembrane under different load levels. The research results can provide a reference for the long-term deformation analysis of composite geomembrane in earth-rock dams.

Aiming at the unmanned autonomous positioning task of mobile robot during the maintenance of nuclear reactor internals, an underwater geometric positioning method combining inertial measurement unit(IMU) and monocular visual distance measurement was proposed. Combining with the actual environment demand, the principle of distance measurement with monocular camera was described briefly. Aiming at the limitation of the underwater nuclear radiation environment on the use of sensors, a method based on monocular vision correction of inertial measurement unit position estimation integral error and geometric positioning was proposed. Simulation experiments show that the positioning method can effectively correct the accumulated error generated in the positioning process of the inertial measurement unit, and improve the positioning accuracy of the robot in underwater motion.