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The advances in research on the explosion load characteristics of the fuel–air mixture in vented chambers are reviewed herein. The vented explosion loads are classified into three typical types based on this comprehensive literature research. These models are the accumulation load model, attenuation load model, and interval jump load model. The characteristics of the three different typical vented explosion load models are analyzed using Fluidy-Ventex. The research results show that overpressure is largely determined by methane concentrations and vented pressure. The turbulent strength increased from the original 0.0001 J/kg to 1.73 J/kg, which was an increase of 17,300 times, after venting in the case of a 10.5 v/v methane concentration and 0.3 kPa vented pressure. When the vented pressure increased to 7.3 kPa, the turbulent strength increased to 62.2 J/kg, and the overpressure peak correspondingly increased from 69 kPa to 125 kPa. In the case of the interval jump load model, the explosion overpressure peak tends to ascend when the intensity of the fluid disturbance rises due to the venting pressure increasing at a constant initial gas concentration. When the venting pressure reaches tens of kPa, the pressure differential increases sharply on both sides of the relief port, and a large amount of combustible gas is released. Therefore, there is an insufficient amount of indoor combustible gas, severe combustion is difficult to maintain, and the explosion load mode becomes the attenuation load model.
The advances in research on the explosion load characteristics of the fuel–air mixture in vented chambers are reviewed herein. The vented explosion loads are classified into three typical types based on this comprehensive literature research. These models are the accumulation load model, attenuation load model, and interval jump load model. The characteristics of the three different typical vented explosion load models are analyzed using Fluidy-Ventex. The research results show that overpressure is largely determined by methane concentrations and vented pressure. The turbulent strength increased from the original 0.0001 J/kg to 1.73 J/kg, which was an increase of 17,300 times, after venting in the case of a 10.5 v/v methane concentration and 0.3 kPa vented pressure. When the vented pressure increased to 7.3 kPa, the turbulent strength increased to 62.2 J/kg, and the overpressure peak correspondingly increased from 69 kPa to 125 kPa. In the case of the interval jump load model, the explosion overpressure peak tends to ascend when the intensity of the fluid disturbance rises due to the venting pressure increasing at a constant initial gas concentration. When the venting pressure reaches tens of kPa, the pressure differential increases sharply on both sides of the relief port, and a large amount of combustible gas is released. Therefore, there is an insufficient amount of indoor combustible gas, severe combustion is difficult to maintain, and the explosion load mode becomes the attenuation load model.
During mine excavation, rock wall collapse can pose a safety risk to miners. Reasonably designed support equipment can prevent collapse and ensure a safe working environment. In this paper, a new half-bowl spherical rubber structure is introduced and modeled using Abaqus to study its damping ability under different impact energies. By comparing the support reaction forces and pressures of the A-S, R-S, and C-S structures, we find that the R-S structure, with a smaller number of half-bowl spheres, has superior energy absorption abilities and impact resistance. These findings support the designing and manufacturing of mining support equipment.
Risk assessment of deep shale reservoirs is very important for subsurface energy development. However, due to complex geological environments and physicochemical interactions, shale reservoir fabric parameters exhibit variability. Moreover, the actual investigation and testing information is very limited, which is a typical small-sample problem. In this paper, the heterogeneity and statistical characteristics of deep shale reservoirs are clarified by the measured mechanical parameters. A deep learning method for deep shale reservoirs with limited survey data information is proposed. The variability of deep shale reservoirs is characterized by random field theory. A variable stiffness method and stochastic analysis method are developed to evaluate the risk of deep shale reservoirs. The detailed workflow of the stochastic risk assessment framework is presented. The frequency distribution and failure risk of deep shale reservoirs are calculated and analyzed. The risk assessment of deep shale reservoirs under different model parameters is discussed. The results show that a stochastic risk assessment framework of deep shale reservoirs, using a deep learning method and random field theory, is scientifically reasonable. The scatter plots of the elasticity modulus (EM), cohesive force (CF), and Poisson ratio (PR) distribute along the 45-degree line. The different distributed variables of EM, CF, and PR have a positive correlation. The statistical properties of the measurement data and deep learning data are approximately the same. The principal stress of deep shale follows the normal distribution with significance level 0.1. Under positive copula conditions, the maximum failure probability is 5.99%. Under negative copula conditions, the maximum failure probability is 4.58%. Different copula functions under positive and negative copula conditions have different failure probabilities. For the exponential correlation structure, the minimum failure probability is 3.46%, while the maximum failure probability is 6.19%. The mean failure probability of the EM, CF, and PR of deep shale reservoirs is 4.85%. Different random field-related structures and parameters have different influences on the failure risk.
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