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The Kuqa Foreland Basin is an important hydrocarbon-producing basin in western China. The Dabei area is an important zone for hydrocarbon accumulation. High fluid overpressures in the Lower Cretaceous Bashijiqike Formation are related to multi-genetic processes. However, the formation and evolution of pressure remain unclear, hindering the further development of oil and gas migration and accumulation. In this study, the overpressure distribution is described based on a drill stem test and mud density data. The formation and quantification of multi-genetic overpressure were evaluated based on well-logging data and basin simulation technology (Ansys Workbench). The coupling evolution of multi-genetic overpressure was examined based on the basin simulation technique. Finally, the influence of overpressure on hydrocarbon accumulation was explored. The results showed that the residual pressure of the Bashijiqike Formation in the Dabei area ranged from 40 to 60 MPa. The main causes of pressure in the Bashijiqike Formation in the Dabei area were disequilibrium compaction overpressure (2–6 MPa, contribution of 8–15%), tectonic compression overpressure (10 MPa, contribution of 30%), and fracture transfer overpressure (15–20 MPa, contribution of 8–15%). With respect to the evolution process of multiple pressures in the Bashijiqike Formation in the Dabei region, at 0–23.3 Ma, the overpressure due to disequilibrium compaction was <10 MPa and increased slowly to 18 MPa at 2.48–23.3 Ma. At 2.48 Ma, the tectonic compression was enhanced, and the residual pressure reached ~50 MPa. At 1.75–2.48 Ma, fracture activity was enhanced, leading to the generation of fracture transfer overpressure. Under these conditions, the residual pressure exceeded 60 MPa. Finally, the Bashijiqike Formation in the Dabei area is a favorable area for vertical and lateral migration of oil and gas. This study is of great significance to the formation and evolution of multi-origin overpressure in the same basin type and its influence on oil and gas accumulation.

Traditional methods for simulating high-energy explosions in confined spaces, such as those based on the JWL equation, often fail to accurately capture the interaction between blast waves and combustion. To address this, we propose an enhanced Pressure Bubble Method (PBM) that dynamically integrates combustion energy release. Numerical simulations of a TNT explosion in a confined tank were conducted using FLUENT. The results demonstrate that the modified model significantly improves prediction accuracy for overpressure distribution and blast wave propagation compared to experimental data. The key novelty lies in the combustion-energy-integrated PBM, which provides a more precise and efficient simulation tool for confined explosions, especially when detailed explosive parameters are unavailable.

To calculate the dynamic peak overpressure of the shock wave of a shell-packed charge and assess their blasting power, this paper constructed a function relationship between terminal velocity, loading ratio, test point position, and equivalent charge mass based on the test result and the dynamic scattering characteristics of explosive products. This model quantifies the effect of terminal ballistic parameters on the dynamic peak overpressure of shock waves for the first time, and the error between the model calculation result and the experimental result is less than 12%. In addition, by reconstructing the dynamic peak overpressure space-time field of shock waves and analyzing the influence of terminal ballistic parameters on dynamic peak overpressure of shock wave, it was found that the height had the biggest influence on the dynamic peak overpressure of shock wave with the influence coefficient of -11896 Pa/m, followed by the fall angle with the influence coefficient of 37.4 Pa/°, and then the terminal velocity with the influence coefficient of 16.6 Pa/(m/s).

In this paper, the effect and action low of thermite on the explosion performance of HMX-based thermobaric explosive were studied. A series of near-ground explosion were tested for the explosives containing Fe 2 O 3 and CuO. The results showed that thermobaric explosive containing thermite had a significant thermal effect, the peak temper ature was about 15% higher than that of TNT, and the distribution of the fireball morphology and temperature was asymmetrical. Compared with CuO, Fe 2 O 3 could significantly increase incident wave ’s peak overpressure and duration of barotropic action, and Fe 2 O 3 had greater advantages than CuO in promoting thermite reaction to increase fireball temperature and duration of thermal effect. The results provide reference and guidance for the formulation design of thermobaric explosive.

Combined with the image explosion source method and LAMBR (LAMB revisied) model, a fast calculation method of wall load of implosion in cylindrical vessels with single explosion source was proposed. The verification results show that the maximum relative errors of the predicted and simulated values of overpressure peak and specific impulse on the structural wall are −13.66% and −17.84% respectively. The predicted overpressure and specific impulse time curves are in good agreement with that obtained by simulation, which can reflect the multimodality of the load at the measuring point under the action of implosion and verify the effectiveness of the method.

Understanding the dynamic characteristics of deep-sea explosions is essential to improve the survivability and combat capability of deep-sea equipment. In this paper, by considering the practical underwater conditions, we investigated the mechanical effects of the deep-sea 1-kg-trinitrotoluene (TNT) explosion with charge depths ranging from 1 to 10 km through numerical simulation and dimensional analysis. The shock wave overpressure, the positive overpressure pulse, the bubble pulse, and the energy distribution for various depth explosions were analyzed systematically. The simulation results showed that the charge depth was negligible for the peak overpressure of the shock wave. However, the positive overpressure pulse, the shock wave energy, the maximum bubble radius, the bubble energy, and the bubble period decrease significantly with increasing the charge depth. Then, the dimensional analysis for deep-sea TNT explosion was performed to reveal the key dimensionless parameters, from which the scaling laws of the shock wave overpressure and the overpressure pulse were obtained. By fitting the simulation results, the dimensionless equations were proposed, providing an effective method for predicting the peak overpressure and the positive overpressure pulse of shock wave for underwater TNT explosion over a wide range of water depths.

Air overpressure is one of the most undesirable destructive effects induced by blasting operation. Hence, a precise prediction of AOp has vital importance to minimize or reduce the environmental effects. This paper presents the development of two artificial intelligence techniques, namely artificial neural network (ANN) and ANN based on genetic algorithm (GA) for prediction of AOp. For this purpose, a database was compiled from 97 blasting events in a granite quarry in Penang, Malaysia. The values of maximum charge per delay and the distance from the blast-face were set as model inputs to predict AOp. To verify the quality and reliability of the ANN and GA-ANN models, several statistical functions, i.e., root means square error (RMSE), coefficient of determination ( R 2 ) and variance account for (VAF) were calculated. Based on the obtained results, the GA-ANN model is found to be better than ANN model in estimating AOp induced by blasting. Considering only testing datasets, values of 0.965, 0.857, 0.77 and 0.82 for R 2 , 96.380, 84.257, 70.07 and 78.06 for VAF, and 0.049, 0.117, 8.62 and 6.54 for RMSE were obtained for GA-ANN, ANN, USBM and MLR models, respectively, which prove superiority of the GA-ANN in AOp prediction. It can be concluded that GA-ANN model can perform better compared to other implemented models in predicting AOp.

To estimate the turbulence influence of the branch tubes on the rising rate of gasoline-air mixture explosion overpressure, turbulence factor α was introduced to modify the formula of the peak rise rate of explosion overpressure. Then, the experimental data of peak explosion overpressure and overpressure rise rate under the different numbers of branches were obtained. Finally, the experimental data and laminar flame velocity were put into the overpressure rise rate correction formula, and the turbulence influence factors under different branches were calculated. The results show that increasing the number of branches will improve the turbulence degree of the explosion process. When the number of branching pipes is 1, the turbulence impact factor is about 2.0 times that in the case of no branch tube, while when the number of branch tubes is more than 1, the turbulence impact factor fluctuates about 3.5 times.

Air-blast overpressure (AOp) is one of the undesirable effects caused by blasting operations in open-pit mines. This side effect of blasting can seriously undermine surrounding residential structures and living quality. To control and mitigate this situation, this study using artificial neural networks to predict AOp implemented at Deo Nai open-pit coal mine, Vietnam. A total of 146 events of blasting were recorded, of which 80% (118 observations) was used for training and 20% (28 observations) was used for testing. A resampling technique, namely tenfold cross-validation, was performed with three repeats to increase the accuracy of the predictive models. In this paper, three different types of neural networks were developed to predict AOp including multilayer perceptron neural network (MLP neural nets), Bayesian regularized neural networks (BRNN) and hybrid neural fuzzy inference system (HYFIS). Each type was tested with ten model configurations to discover the best performing ones based on comparing standard metrics, including root-mean-square error (RMSE), coefficient of determination ( R 2 ), and a simple ranking method. Eight parameters were considered for these models, including charge per delay, burden, spacing, length of stemming, powder factor, air humidity, and monitoring distance. The results indicated that MLP neural nets model with RMSE = 2.319, R 2  = 0.961 on testing datasets and a total ranking of 12 yielded the most accurate prediction over BRNN and HYFIS models.

Due to differences in charging technology, test methods, etc. the same explosive can obtain different explosive parameters in different tests. The difference in performance of explosives with different parameters is the focus of engineer’s design. This paper uses the TNT equivalence (TNT e ) to compare the parameters of three CL-20 based explosives reported in the previous literature. In this paper, TNT e is attained from Peak overpressure (OP) and impulse (IM), calculated by AUTODYN software and is compared with the TNT e calculated by Cooper Method, Hydrodynamic Work, heat of detonation method. The results show that the difference of TNT e among different parameters can reach 30%. The TNT e calculated by heat of detonation method is too low to be recommended. The average value of TNT e calculated by other methods is 1.69±0.09. More accurate TNT e needs to be used with caution, including data sources and limitations.