To enhance the support for the nuclear safety review of domestic advanced pressurized water reactor (PWR), the reactor coolant pump (RCP) shaft seizure accident for this reactor type was conducted. The impact of the loss of offsite power (LOOP) assumption and moderator temperature feedback on the accident consequences was examined. The findings are as follows. The peak pressure in the reactor coolant system and the peak temperature of the hot spot cladding, recorded during the accident process, met the accident acceptance criteria, enabling the nuclear power plant to eventually stabilize. The LOOP assumption was found to be conservative. For this accident, however, the differences arising from this assumption manifested in the later stages and did not influence key outcome parameters such as system pressure peak, minimum departure from nucleate boiling ratio (DNBR), and peak temperature of hot spot cladding, which occurred early in the accident. Therefore, the inclusion or exclusion of the LOOP assumption was deemed acceptable. The introduction of moderator temperature feedback was considered an unconservative assumption. Nevertheless, even without this feedback, the peak pressure in the reactor coolant system and the peak temperature of the hot spot cladding still met the accident acceptance criteria, maintaining a substantial margin. Additionally, incorporating moderator temperature feedback brought the calculations closer to reality. Overall, the introduction of moderator temperature feedback in this accident was also judged to be acceptable.

CO₂ flooding is one of the most effective technical means to improve the recovery of low permeability reservoirs. However, the injected CO₂ will cause asphaltene precipitation in crude oil, resulting in the decrease of reservoir permeability and the change of reservoir heterogeneity. In order to clarify the effect of asphaltene differential precipitation on the heterogeneity of low permeability reservoirs under different pressure conditions, a low permeability reservoir in Jilin Oilfield was taken as the research object. Through the CO₂ long core displacement experiment, the CO₂ displacement characteristics under different pressures were clarified. At the same time, combined with the test results of crude oil and core properties, the changes of crude oil asphaltene content and reservoir properties before and after displacement under different pressure conditions were analyzed, and the distribution law of asphaltene precipitation was further clarified. The results show that under immiscible conditions, the degree of crude oil recovery is low, only 55.11%. The asphaltene precipitation increases first and then decreases with the increase of displacement distance. CO₂ mainly displaces the large pore throat crude oil, and the asphaltene precipitation also mainly occurs in the high permeability area, thus inhibiting the reservoir heterogeneity. Under miscible conditions, the degree of crude oil recovery is high, up to 81.84%. Asphaltene precipitation increases with the increase of displacement distance. CO₂ can displace small pore throat crude oil. Asphaltene precipitation occurs in high permeability and low permeability areas, but low permeability areas are greatly affected, and reservoir heterogeneity is further strengthened.

In order to analyze the instability and failure characteristics of earthquake landslides in the Loess Plateau, the Xinbaocun landslide induced by the 8.5-magnitude Haiyuan earthquake in 1920 was taken as an example. Based on satellite remote sensing interpretation and field investigation of landslides, the original terrain of the Xinbaocun landslide before sliding was restored using the numerical simulation function of MATLAB based on the principle that the volume of the landslide body before and after sliding is equal. On this basis, the numerical simulation method was used to analyze the instability, deformation and failure characteristics of the Xinbaocun landslide under the action of earthquakes and invert the minimum horizontal ground motion of the slope instability. The landslide restoration results show that after the restoration of the Xinbaocun landslide, the slope surface shape is generally concave, and the slope distribution range is 12°~18°.The numerical simulation results show that under natural conditions, the slope deformation of the restored landslide is small, there is no obvious plastic deformation zone,the stability is good, and the overall stability coefficient is 1.355. Under the action of earthquake, when the input earthquake motion is 0.4g, the stability coefficient is 0.887, the slope is in an unstable and damaged state, and the slope body has a large area of deformation. The maximum deformation area is located near the top of the slope,and gradually decreases from the top to the foot of the slope. It is speculated that the landslide is a push-type landslide. The inversion results show that the minimum horizontal earthquake motion for slope instability is 0.36g. The research results of this paper can provide certain basic data and reference for the prevention and control of earthquake disasters in the Loess Plateau.

The interporosity flow between matrix pores and fractures is the main cause of strong attenuation and dispersion of elastic wave in the seismic frequency band. A fractional equation for interporosity flow is developed. The constitutive relationship of double-porosity medium was improved in the frequency domain, in which three factors representing the effects of fractional interporosity flow were introduced. A wave equation considering fractional interporosity flow for a double-porosity medium was developed by substituting the improved constitutive relationship into the Lagrangian equation. The wave velocity and inverse quality factor was obtained and analyzed by plotting. Comparing to the traditional interporosity flow, the fractional interporosity flow provides a more flexible and accurate description for the dispersion and attenuation of elastic waves in the seismic frequency band.

Gas storage operations can be significantly impacted by abnormal wellbore temperatures at natural gas storage sites. Accurately predicting wellbore temperatures is of paramount importance for enhancing the safety and efficiency of these operations. Based on the analysis of operational parameter correlations, a gas storage wellbore temperature prediction method was proposed using advanced spatiotemporal graph convolutional neural network (A-SGCN). Both GCN and long short-term memory (LSTM) networks were employed by A-SGCN to capture spatial and temporal dependencies, respectively. Based on this framework, an adaptive residual attention mechanism was incorporated to effectively capture the intricate relationships between spatiotemporal data, ultimately enabling accurate temperature prediction. The effectiveness of the method is validated through its application at the Huangcaoxia gas storage No.2 injection-production station. Accurate prediction of wellhead temperature at Well No.1 is achieved through the association of monitoring parameters between Well No. 1 and Well No.6.

Landslides are significant geological hazard, with most events directly related to rainfall. Consequently, rainfall monitoring is critical for landslide forecasting and prediction. A self-powered rainfall sensor based on triboelectric nanogenerator (TENG) technology was developed for landslide monitoring. The sensor consists of a transmission unit and a sensing unit, which enable rainfall measurement and self-powering through rain-induced contact electrification of nanomaterials. Experimental results indicate that the sensor maintains a measurement error of 5% within the 0~300 mL/24 h range, with a linearity of 3.4%. It is capable of being operated in environments where the temperature is below 100 ℃ and the relative humidity is under 90%. Additionally, the output of the sensor can achieve a maximum power of 203.5 nW when an external load resistance of 7.8×10⁷ Ω is applied. The performance of the sensor can meet the practical requirements for field rainfall monitoring of landslides and also show potentials for the development of related rainfall monitoring technologies.

In frastructure projects such as hydropower, harbours and transport are usually constructed by rockfill of aggregates from local materials, however, the shapes of rockfill of aggregates in different regions are quite different. In order to study the effect of aspect ratio on the K₀ consolidation characteristics of rockfill, firstly, 10 kinds of particle clusters with different aspect ratios were generated by the discrete element method, and then the same equivalent particle size was taken to generate the specimens with the same initial state in the same way, and then the K₀ consolidation test was carried out at last, and the effect of the aspect ratio of the particles on the K₀ consolidation characteristics of the heap stone materials and its micro-mechanisms were explored in detail. The results of the study show these as fouows At the macro level, the specimens with different aspect ratios of rockfill are divided by AR=0.5, when the AR is 0.5~1, the K₀ value of the specimens increases with the increase of aspect ratio, and when the AR is 0.2~0.5, the trend of the change of the K₀ value of the specimens with the increase of the aspect ratio is unknown. This suggests that the particles in the interval of aspect ratio 0.5~1 need to be focused on during engineering design. At the microscopic level, the smaller the particle aspect ratio (the longer the particles), the larger the coordination number, the smaller the average force between the particles, and the smaller the cumulative angle of rotation of the particles, which suggests that the contact between the particles of the specimens with smaller particle aspect ratios is closer, the contact force chain is more dispersed, and the particles are more resistant to rotation. This may be the reason for the differences in K₀ consolidation properties of rockfill with different aspect ratios. The research results can provide theoretical references for the evaluation of K₀ values in areas where there are particle shape differences, and provide a basis for selecting appropriate rockfill materials, ensuring the safety of the project.

A new anomaly sound detection algorithm was studied that combines attention mechanisms and domain generalization techniques to more accurately identify normal and abnormal sounds in mechanical equipment. Specifically, two neural networks were jointly trained using a sub-cluster Adacos loss function, with features modeled by a Gaussian mixture model (GMM). Anomaly scores were calculated using negative log-likelihood values, and a 90th percentile threshold was set for detection. The algorithm demonstrated strong performance across seven types of machines, including fans and bearings, achieving harmonic mean AUC(area under curve) and pAUC values of 76.69% and 87.99%, with the highest performance observed on valve data. Compared to two baseline systems, the algorithm improved AUC and pAUC by 24.08% and 20.68%, respectively. Ablation studies further confirmed the positive impact of the GMM, attention mechanism, and Scadacos loss function. When tested against eight other algorithms on the same dataset, the proposed method showed a 4.16% improvement in the harmonic mean of AUC and pAUC, highlighting its significant advantage in anomaly sound detection tasks.

In view of the position control accuracy and body chattering of small unmanned helicopter under large disturbance, an improved sliding mode controller (SMC) was proposed. Firstly, for the unknown parameters in the dynamic model of small unmanned helicopter named Align T-REX 300, three flight experiments were designed for the data acquisition, determining the comprehensive aerodynamic parameters. Then, the dual-channels control strategy with an improved SMC was designed to realized the helicopter hovering. The parameters ranges of SMC were presented by combining stability analysis, which can effectively suppress chattering. The model validation experiment shows that the aerodynamic parameters determined by the flight experiments have high fidelity. Moreover, the control simulations show that the steady state error of the step responses is less than 0.02 under the continuous large disturbance. Under the improved SMC with high control accuracy and weak tremor, the servos' control signal curves are smooth, which is more conducive for achieving the flight control of unmanned helicopter under the disturbance in actual engineering.

To improve the short-term prediction accuracy of photovoltaic power generation models with multiple input features, a photovoltaic power prediction ensemble model LGGWO-TCN-MHSA based on optimizing TCN hyperparameters was proposed. The model integrated the levy gold grey wolf optimization (LGGWO), temporal convolutional network (TCN), and multi-head self-attention mechanism (MHSA). First, the Spearman correlation coefficient method extracted the main features that significantly affect photovoltaic power, which were then fed into the TCN prediction model. Then, the proposed multi-strategy LGGWO was applied to the TCN for hyperparameter optimization, which improved the model's prediction performance. Finally, the predicted values were input into the multi-head self-attention model to further boost prediction accuracy. The experiment was verified using original Australian photovoltaic data. By comparing with six groups of models including convolutional neural networks (CNN) and long short-term memory neural networks (LSTM), the mean absolute error (MAE) and root mean square error (RMSE) of the proposed model on the test data set were reduced by 2.03%~82.0% and 10.5%~80.1%, respectively. The results show that the proposed method has high prediction accuracy and good stability.