To improve the accuracy of airworthiness assessment for aviation lithium batteries, this paper evaluated the feasibility of equivalent substitution of propane mixed gas in the explosion containment test of thermal runaway gases of aviation lithium batteries. Based on airworthiness standards, the explosion characteristics of standard volume fraction propane and in-situ thermal runaway gases from lithium batteries at different states of charge (SOC) were experimentally compared and analyzed. Ternary lithium batteries were employed, and a dedicated experimental platform was constructed to systematically measure the explosion temperature, maximum explosion overpressure, and pressure rise rate of the thermal runaway gases under different SOCs. The results indicate that the explosion limit range of the lithium battery thermal runaway gases significantly widens with increasing SOC, with a maximum explosion overpressure of 0.519 8 MPa and an explosion power index of 1.093 9. For propane within the standard volume fraction range (3.85% to 4.25%), the maximum explosion overpressure reached 0.822 5 MPa, with an explosion power index of 1.501 7, its explosion potential being higher than that of thermal runaway gases under most SOC conditions. Directly adopting the standard propane concentration for explosion containment verification may lead to over-testing, resulting in an excessively high safety margin. Propane can serve as a preliminary assessment medium, but for equivalent substitution, the test concentration needs to be optimized in combination with the actual SOC state.

To address the challenge of locating the fire source in high-rise building fires, a full-scale indoor fire test platform was constructed and a series of tests were conducted to investigate the feasibility of inferring fire locations from the temperature field on the fire-unexposed surface of window glass in high-rise building fires. By varying the fire location and heat release rate, the temperature field on fire-unexposed surface and fire environment parameters were obtained, and the characteristics of fire-unexposed surface temperature field under different scenarios were analyzed. The results show that window glass regions at higher elevations and on the fire side exhibit significantly higher temperature rise rates under different fire location conditions. At 480 s after ignition, the temperature non-uniformity coefficient of fire-unexposed surface under different fire location conditions is not less than 33.52%, and reductions in the distance between fire location and window lead to a marked increase in the temperature non-uniformity coefficient on the fire-unexposed surface. With increasing heat release rate, the coefficient of variation of the increase in temperature rise rates across different glass regions on the fire-unexposed surface generally exceeds 10%, and this disparity becomes more pronounced with increasing heat release rate. When the normal distance between the fire location and the window decreases, window glass at higher elevations and on the fire side exhibits a greater increase in temperature rise rate. When the radial distance between the fire location and the window decreases, the increase in temperature rise rate at higher elevations is significantly greater than that on the fire side.

In order to address the safety and environmental risks associated with aqueous ammonia leakage accidents in the chemical industry, based on the rich pore structure and physicochemical properties of porous materials, AC and zeolite were selected as the basic raw materials for detergents. The powder detergents AC@AlCl₃ and Zeolite@AlCl₃ were prepared by impregnating with an AlCl₃ solution of varying mass fractions. Using aqueous ammonia as the target contaminant, systematic experiments were conducted involving both individual decontamination and combined powder-liquid decontamination, employing the aforementioned modified powdered decontaminants along with various surfactants. By evaluating indicators such as decontaminant dosage, volume fraction of volatile gas, and the morphology of decontamination products, the effectiveness of individual versus combined decontamination was compared, and the practical performance of the powder-liquid synergistic decontamination strategy was comprehensively assessed. The results demonstrate a significant enhancement in the decontamination capacity of the modified powders, with decontamination efficiencies for aqueous ammonia exceeding 70%. Among the samples, AC@20%AlCl₃, combined with a 3% NaHCO₃ powder solution, forms a more complete gel-like solid during the decontamination process, and the decontamination efficiency reaches 80%. This approach successfully realizes the goals of high efficiency, cost-effectiveness, and safety in aqueous ammonia decontamination.

To address the current issues of low intelligence level in coal mine working faces and insufficient research on the performance monitoring of shaft structures, a digital twin-based performance monitoring method for vertical shafts is proposed. Firstly, a five-dimensional framework for the digital twin of vertical shafts is proposed based on the operational mechanism and performance monitoring requirements of the shafts. Secondly, a digital twin of the shaft is established by combining virtual-real mapping technology with a finite element surrogate model for grid dimensionality reduction. The structural performance of the vertical shaft is predicted online through artificial neural network technology, where the predicted data is the real-time prediction of shaft structure performance data obtained during the shaft operation process using a shaft structure performance prediction model. The prediction model for the structural performance of the vertical shaft adopts the RBF surrogate model, and the Unity3D virtual engine platform is built to integrate the above functions and achieve online prediction of the structure performance of the vertical shaft. The results indicate that during the operation, by simulating 120 sets of stress and strain data under different working conditions, the average coefficient of determination between predicted and simulated values is 0.995 5, indicating a high correlation between the predicted strain and simulated strain, thus verifying the feasibility of the digital twin framework for vertical shafts. This provides an effective reference for the digital improvement of vertical shafts.

This article proposes a nuclear environment monitoring method based on ionizing radiation response characteristics in visual images. Through experimental analysis of the response characteristics of ionizing radiation to visual images, including statistical parameters such as mean, variance, skewness, and kurtosis of pixel values, the relationship between feature data and radiation dose rate was quantified, and the high-precision linear correlation of the fitting was verified. A two-dimensional wavelet packet decomposition is used to analyze high-frequency components in video images, and an algorithm is proposed to extract radiation response signals from complex backgrounds, achieving accurate monitoring of gamma ray radiation dose rates in nuclear environments. The experimental results indicate that within the gamma ray radiation dose rate range of 51.61 Gy/h to 479.24 Gy/h, there is a significant linear relationship between the number of response events and the dose rate. The correlation coefficients of the fitted curves are 0.998 9 and 0.999 3, respectively. In terms of pixel response characteristics, the mean and variance significantly increase with increasing dose rate, while skewness and kurtosis show an exponential downward trend. In addition, the experiment verified the influence of setting the pixel value threshold on radiation dose rate measurement. When the pixel value threshold is 130, the linearity of the fitting results is optimal. By using two-dimensional wavelet packet decomposition, the statistical analysis of high-frequency components in the image has further improved the accuracy of dose rate characterization, especially achieving maximum linearity in the diagonal components. This method can efficiently extract radiation response information in complex environments, achieve precise monitoring of gamma ray dose rates in nuclear environments, and provide technical support for emergency response to nuclear leaks and assessment of radioactive areas.

To address the challenge of accurately predicting the fatigue crack initiation life caused by surface pitting corrosion in buried thermal pipelines during design, operation, and maintenance, this study employed the finite element method to investigate the influence of pit morphology, defect interaction, and axial loading on the maximum stress concentration factor K_t. An empirical formula for K_t was proposed, and a prediction method for fatigue crack initiation life under soil corrosion was developed. A pipeline in service in Beijing was used as a case study to verify the scientificity and effectiveness of this method. The results indicate that a 10-fold increase in pit depth leads to a 2.84-fold increase in K_t, while a 10-fold reduction in pit circumferential width results in a 4.75-fold increase. Deep and narrow defects characterized by a/c > 0.6 and b/c < 0.6 exert a stronger effect on increasing K_t and significantly shorten crack initiation life. When the defect spacing d=0, K_t reaches 1.03 times that of a single defect. The smaller the defect spacing, the stronger the interaction effects and the lower the crack initiation life. As defect spacing decreases, the fatigue crack initiation life of deep narrow pits is reduced to 0.12 times the original life, whereas shallow wide pits are more sensitive to spacing, with their crack initiation life reduced to 0.83 times the original life. Under conditions of low soil resistivity, low pH, and elevated temperature, crack initiation may occur within 20 years. The crack initiation life of shallow wide pits is more sensitive to soil parameters. Compared with internal pressure loading alone, an axial compressive load of 20 MPa significantly reduces crack initiation life by 0.74 times. Increasing axial tensile load from 20 MPa to 50 MPa results in a further life reduction of 0.82 times.

To improve the safety of aircraft landing phase, this study investigates the prediction of pilot human error during the carrier landing phase complex task environments based on CREAM. Based on the pilot operational workflows during the carrier landing phase, this work recalibrates the original Common Performance Condition (CPC) factors in CREAM, and proposes an improved approach for predicting the probability of human error in pilots based on the improved CREAM method, describing the situational environment in which the pilot is located during the carrier landing phase. It introduces an Environmental Impact Index and Effect Impact Index to characterize the influence of various task environments on pilots' cognitive functions. A simulated carrier landing assessment experiment is designed and conducted based on the actual situational environment during the carrier landing phase. By analyzing characteristic indicators such as electroencephalogram (EEG), eye-tracking, electrocardiogram (ECG), and electromyography (EMG) data, and National Aeronautics and Space Administration Task Load Index(NASA-TLX) scale data of the subjects, the load level of the subjects in different environments is assessed, and then the effect impact index of different cognitive functions is calculated to predict the probability of human error. The results demonstrate that the proposed method for predicting probability of human error in pilots can improve the accuracy of prediction results by using objective data assessment, which instead of experts' subjective evaluations. This method can predict the probability of human error in pilots under the influence of 20 different environmental factors.

To investigate the instability of rock slopes along joint surfaces in water-impounded abandoned open-pit mines under fluctuating water-level conditions, physical model tests were performed using similar materials containing joints and weak interlayers. The mix proportion of the similar materials was optimized through orthogonal experimental design to ensure that their mechanical and seepage properties corresponded to those of the in-situ rock mass. Multiple types of sensors were embedded in the model to monitor pore water pressure, earth pressure, moisture content, and displacement, enabling systematic observation of the multi-field responses of the slope during water-level fluctuations. Working conditions with different numbers of cycles and varied rates of water-level change were designed to examine the deformation characteristics and failure mechanisms of the slope. The results indicate that certain damage to the slope surface is caused by the scouring effect induced by repeated water-level fluctuations at a constant rate, although internal structural damage is limited. This suggests that overall stability is little influenced by slow, single water-level fluctuations. The rate of water-level change is shown to significantly affect slope behavior, with slope displacement increases positively correlated with the rate of water-level decline. Furthermore, the greater the outward-directed pore water pressure of the slope is, the more significantly it is influenced by the hysteresis effect, and the higher likelihood of slope instability becomes.

In order to improve the development of safety behavior of flight cadets and enhance the level of flight training, conduct an indepth exploration of the relationship and intrinsic mechanism between authentic leadership style and safety behavior of flight cadets, a theoretical model of the safety behavior of flight cadets based on the authentic leadership style theory was constructed. The questionnaire was developed by drawing on established scales and consulting experts to align it with the current training conditions of aviation schools. AMOS26.0 software was used to test the mediating effect of authentic follow and the moderating effect of basic psychological needs satisfaction, and verify the effect on all constructs, the applicability of the theoretical model of flight cadets safety behavior in different stages of learning to fly, flight level, instructor job groups and work environment. The results show that there is a positive correlation between the authentic leadership style of flight instructors and the safety behavior of flight cadets, and the authentic follow of flight cadets plays a mediating role between the two. The authentic leadership style has a positive impact on authentic follower, and the basic psychological needs satisfaction plays a moderating role in this process. Different groups between the four dimensions of stages of learning to fly, flight level, instructor job groups and work environment have no moderating effect on the model, further verifying the structural stability of the model.

To address the problem of reduced defect classification accuracy caused by noise contamination in the bend detection signals of oil and gas pipelines, this paper proposes an oil and gas elbow defect diagnosis model based on Welch power spectrum feature enhancement and multi-head attention improved dual-branch multi-scale-residual collaborative network. Firstly, the Welch method was used to convert the collected time domain signal into a feature-enhanced power spectrum, showing the energy distribution of the defect signals at different frequencies. Secondly, the multi-scale network branch composed of parallel stacked convolutional layers was responsible for extracting the multi-dimensional features of the signal power spectrum, and the multi-head attention mechanism was used to establish long-term associations between features. Simultaneously, the residual network branch captured the detail information of the signal power spectrum. Finally, the deep concatenation layer fused the features extracted by the dual-branch network to achieve defect classification. The experiment results show that in a high-noise environment, the test accuracy of the proposed model is 91.6%. Compared with the models based on Kaiser windows and flat-top windows, the classification accuracy is improved by 1%~7.9%; compared with convolutional neural network (CNN) and long short-term memory network (LSTM), the accuracy is improved by 36.9% and 10.3% respectively.