In order to reduce the dynamic loads on risers under harsh sea conditions and enhance their adaptability to complex marine environments, a multifunctional suspension system for deepwater riser was proposed, which included a suspension system, a hydraulic compensation system, and a measurement and control system, in order to ensure the safety of the suspended riser under severe sea conditions such as typhoons in the South China Sea. In addition, the system can also be applied to transit with a suspended riser between wells, which can significantly reduce the load of the suspension riser. The suspension system suspends a single bearing riser system, and relieves the stress concentration on the top of the riser by means of a centralizer and a hinge joint; the hydraulic compensation system adopts three working modes of differential throttling compensation, passive non-throttling compensation and throttling shielding to meet the requirements of platform avoidance and inter-well transit. The measurement and control system monitors and warns in real time, regulates and controls the telescopic movement of the hollow hydraulic cylinder, and realizes the real-time compensation of the dynamic load of the riser system. The system has undergone sea trial applications, the results show that the proposed multi-function suspension system can significantly reduce the riser top stress and dynamic load amplitude, especially in the differential throttle compensation and passive non-throttle compensation modes. At the same time, the downstream and upstream speeds of the platform are increased to 1.0 knots, which significantly improves the operation efficiency. The system helps to overcome the challenge of traditional suspension mode, provides a safe and efficient solution for deepwater drilling riser operations, and reduces the disaster risk of suspension riser under extreme sea conditions such as typhoons.

To mitigate dust dispersion during electric shovel excavation and loading operations, an externally mounted high-pressure air curtain closed-loop jet dust removal system was designed. This system isolated dust generated during dumping operations at the shovel front from the surrounding environment, creating a low-disturbance zone. When only the airflow system was activated, dust was drawn into the negative-pressure dust collection port through a predetermined closed-loop path via the combined action of jet outlets and the collection port. Subsequent activation of the spray system intensified droplet fragmentation under high-velocity jet impact, enabling thorough mixing of mist particles with dust-laden airflow before settlement. Numerical simulations were conducted to analyze the shovel's external flow field characteristics and droplet distribution during system operation. Four jet deflection angles were evaluated to determine optimal flow field distribution. Simulation results show that the optimal closed-loop entrainment is achieved when the jet velocity is 30 m/s and the deflection angle is 30°. Spray activation enhances droplet fragmentation through high-speed airflow impact, significantly improving dust wetting coverage in front of the shovel body. Airflow field simulations were confirmed through smoke visualization tests showing high similarity to observed flow patterns. Dust concentration measurements after spray activation demonstrated the best dust control effect around the shovel when the jet deflection is 30°, verifying the simulation accuracy of the external flow field and establishing optimal operational parameters.

In-service pipelines are subject to complex stresses, and their performance degradation over time constitutes a dynamic, time-varying stochastic process. To address the challenge that traditional deterministic functions struggle to accurately capture its inherent randomness, a dynamic analysis method for failure probability of in-service pipelines based on a dual stochastic process was proposed. The degradation of pipeline performance was simulated using an inverse Gaussian stochastic process, while the variation of internal pressure loads within the pipeline was described by an equal-interval stationary binomial rectangular wave process probability model. A dual stochastic process probability model for pipeline bearing capacity and internal pressure load was then constructed. Based on statistical parameters and performance degradation data from a specific pipeline's service period, inverse Gaussian distribution was used to fit the performance degradation models at six distinct time points, enabling dynamic failure probability prediction. The results show that the pipeline's service life is predicted to be 16 and 14 years using degradation data from 2 and 4 years, respectively. When utilizing degradation data from 6, 8, and 10 years, the predicted service lives are 12, 11, and 10 years, respectively. Sensitivity analysis indicates that wall thickness, yield strength, pipe diameter, and operating pressure have the most significant impacts on the pipeline's failure probability, followed by the initial depth of defects. In contrast, the initial length of defects, depth corrosion rate, and length corrosion rate have relatively minor effects.

To investigate the influence of ammonia admixture on methane explosion propagation characteristics in tunneling tunnels, the explosive behavior of methane/ammonia/air mixtures was systematically examined under varying ammonia blending ratios, initial pressures, and initial temperatures. The explosion overpressure evolution and flame propagation characteristics of the mixtures under varying ammonia blending ratios, initial pressures, and initial temperatures. Results indicate that the incorporation of ammonia exhibits a dual effect on the explosion behavior: initial suppression followed by enhancement. The explosion overpressure is observed to decrease first and then increase with rising ammonia content, while the flame front position and propagation velocity progressively increase with higher ammonia ratios. Regarding the influence of initial pressure, the peak explosion overpressure, flame propagation velocity, and flame position all show linear increasing trends with elevated initial pressure. The influence of initial temperature on explosion characteristics is more complex. Within the range of 300-700 K, the explosion overpressure increases with temperature, and the flame propagation trajectory show a significant positive correlation with temperature. However, when the temperature reaches 900 K, flame propagation is markedly suppressed and manifested by significant decreases in both the flame front position and velocity. The study reveals that the ammonia blending ratio exerts a non-monotonic influence on the methane explosion process, while initial pressure and temperature regulate explosion intensity and flame propagation behavior in linear and nonlinear ways, respectively.

Existing methods for predicting temperature from subway carriage and tunnel fires mainly rely on physical models and empirical methods that are valid only under narrowly-defined environmental conditions. To solve this issue, this study adopts an artificial-intelligence-based approach. A GA-BPNN network model is constructed by optimizing BPNN using a GA. The GA is employed to global optimize BPNN's weights and thresholds, after which the model is trained to predict the temperature distribution of both the subway carriage and the tunnel, thereby achieving intelligent inversion of the fire temperature field. The results show that, for subway carriage temperature prediction, GA-BPNN model yields a mean absolute error (MAE) of 8.17, a root mean square error (RMSE) of 9.76, and a coefficient of determination (R²) of 0.99. For tunnel temperature prediction, MAE is 3.95, RMSE is 5.63, and R² reaches 0.98. By comparing the results with those of the traditional BPNN, it is found that the GA-BPNN model outperforms the conventional BPNN in both prediction accuracy and generalization capability.

In order to promote the optimization and upgrading of metal dust explosion-proof technology and establishment of a safety protection and control system, this study systematically investigated the reaction kinetics of aluminum powder with excess water under varying particle sizes and stacking masses, based on the self-developed visualization experimental platform for the reaction between stacked metal dust and water. Through quantitative characterization of the effects of particle size and stacking mass on key reaction parameters-including the maximum temperature, total gas production, maximum hydrogen concentration, maximum pressure, and total reaction time-the kinetic mechanism of the aluminum-water reaction was revealed. The results demonstrate that at a fixed particle size, all characteristic parameters exhibit positive correlations with increasing stacking mass. At a fixed stacking mass, the maximum temperature follows a V-shaped trend (decreasing then increasing) with increasing particle size, while gas production, hydrogen concentration, and pressure show inverted U-shaped trends (increasing then decreasing). Total reaction time increases monotonically with increasing particle sizes. The primary reaction products are Al(OH)₃ and H₂, and the reaction process comprises three distinct stages: slow hydrogen evolution, violent reaction, and attenuation and termination.

To address the high-risk characteristics and cascading propagation effects of accident consequences of major hazard installations in chemical parks, the framework of the safety risk prevention and control system was constructed and optimized. The advantages and disadvantages of safety risk analysis methods for major hazard installations and the evolution analysis methods of domino accident chains were systematically reviewed. The definition, classification, and efficacy evaluation indicators and models of safety barriers in chemical parks were summarized. A new classification pattern for safety barriers in chemical parks was proposed, encompassing physical facilities, automated technologies, operational procedures, and organizational management. Key assessment indicators for safety barriers in chemical parks were identified, namely effectiveness and reliability, and future research directions were outlined. The results show that the proposed system reveals the intrinsic mechanism of dynamic closed-loop control among safety risk analysis, domino accident chain evolution, and safety barriers for major hazard installations in chemical parks, thereby effectively overcoming the static limitations of traditional approaches. The new classification pattern and key assessment indicators provide a novel paradigm for quantifying the effectiveness of safety barriers in chemical parks.

In order to address the significant safety risks posed by frequent extreme weather to the source, grid, and load-side equipment of the new power system, a risk assessment model for urban new power systems under extreme weather conditions was proposed. First, risk factors of the urban new power system were identified based on disaster theory, and an ISM was applied to clarify the interrelationships among these risk factors. Subsequently, the topological structure of the disaster chain was mapped into a BN. The prior probabilities of each risk factor node were determined using fuzzy comprehensive evaluation and accident statistics. Sensitivity analysis and scenario analysis were employed to derive key risk nodes for urban new power system accidents and the consequences of multi-hazard coupled accidents. Finally, MC simulation was utilized to conduct operational optimization analysis on "transmission towers" from the perspective of wind resistance level design. The results indicate that the constructed BN-MC coupled model effectively quantifies and enhances the analysis of extreme weather risks in urban new power systems. Under multiple superimposed extreme weather conditions, the failure probability of photovoltaic generators reaches as high as 60%, with strong winds being the key driving factor. Furthermore, improving the wind resistance level of transmission towers significantly reduces their failure probability. At a real-time wind speed of 36 km/h, increasing the wind resistance level from 35 km/h to 40 km/h reduces the failure probability by 59.39%. This effect exhibits a nonlinear characteristic, with a greater reduction in risk probability in the low wind speed range than in the medium wind speed range.

Traditional manual methods for detecting wall hollowing suffered from strong subjectivity, low efficiency, and difficulties in large-scale application. To address these issues, this study proposed an intelligent detection method based on a fully automatic hollowing signal acquisition device and an optimized signal processing algorithm. Firstly, a fully automatic hollowing signal acquisition device capable of stable operation on building walls was designed to achieve standardized tapping and high-precision acoustic signal acquisition. Secondly, VMD and EEMD optimized by Bayesian Optimization (BO) were employed to denoise the original signals, thereby enhancing the features of hollowing signals. Then, MSC and MFCC features of the signals were extracted and fused at the frame level to form an MFCC+MSC feature set. Finally, a majority voting ensemble learning model was utilized for classification, enabling high-precision hollowing detection. The results indicate that the classification accuracy of the proposed method reaches 99.31%, significantly outperforming traditional methods. These results validate the feasibility and effectiveness of combining automated devices with optimized signal processing techniques for wall hollowing detection.

To optimize the layout of the outbound transportation system and enhance system resilience, a "Sensitivity-Response" dual-dimensional evaluation index system was constructed. The comprehensive methods including set pair analysis, spatial Markov chain, and obstacle degree model were used to analyze the dynamic characteristics and driving mechanisms of vulnerability in 225 inland cities in China. The findings reveal that vulnerability demonstrates significant multi-polarization and spatial club convergence. The overall vulnerability decreases annually, evolving from bi-polarization to multi-polarization, with a reduction of absolute difference. Vulnerability exhibits significant spatial auto-correlation and spillover effects, forming a spatial pattern where "high-vulnerability is clustered along the borders, while low-vulnerability diffuses from urban agglomerations". A low-vulnerability neighborhood environment increases the probability of a city's vulnerability transferring downward by 64.5%. The obstructive effects of both freight turnover volume and total import-export trade value weaken annually. In contrast, the hindering effect of shortest inland-port road travel time shifts from a relatively stable to a consistent increase. Furthermore, city hub level and connectivity impose significant constraints on vulnerability of non-hub cities.