Energy Science & Engineering最新文献

In this study, a three-dimensional large-scale numerical model is established to investigate the failure characteristics of overlying strata and mechanism of strong ground pressure induced by excavation disturbance from multiple working faces. The characteristics of overlying strata fractures, heights of the caving zone and the fracture zone, and evolution of the stress field are systematically analyzed. The numerical simulation results reveal that the height of the caving zone after mining is 8.1 m, and that of the fracture zone is 27.3 m under the conditions of gently inclined thin coal seams. These findings are consistent with the theoretical results. The fracture development process can be divided into three stages: extensive development of new fractures, partial compaction of fractures, and closure of numerous fractures. In the structure of the post-mining overlying rock, four stress zones are identified as follows: two zones of stress concentration at both ends of the working face, respectively, a zone of relatively high stress at the middle of the working face with low overlying strata, and a zone of stress fully released at the middle of the working face with high overlying strata. Comprehensive analysis of the maximum vertical stress of the cross section and the stress of the working face indicates that the stress increases significantly when mining enters the gob square stage and the roof does not collapse timely.

Manual interpretation of geophysical logging data can be a tedious and time-consuming task in the case of the nonlinear behavior of well-logging signals. This study aims to enhance lithology classification of reservoir formations through advanced machine learning (ML) and deep learning (DL) techniques, introducing and comparing three novel algorithms, GrowNet, Deep-Insight, and blender, against traditional models like random forest (RF) and support vector machine (SVM). Data from the South and North Viking Graben regions, encompassing 12 lithological facies, was preprocessed through cleaning, normalization, transformation, and imputation of missing values using regression models. The data set was enhanced with physic-based features and balanced using SMOTE and NearMiss algorithms. Deep-Insight converted tabular data into images for a convolutional neural network (CNN), significantly improving classification accuracy compared to conventional models such as decision trees (DTs). GrowNet and blender models leveraged hybrid approaches for enhanced performance. These hybrid approaches successfully addressed data imbalance and enhanced model learning, outperforming classic methods. The GrowNet and blender models for lithology classification successfully increased the penalty score and accuracy compared to the FORCE 2020 competition. Additionally, introducing the class prediction error plot visualizes multiclass classification performance more effectively than using a confusion matrix. These novel models in multiclass classification contribute to the petroleum industry by providing more accurate and reliable lithology classification, thereby improving reservoir characterization and exploration efficiency.

Most of the mine roadways in China are located in layered rock mass. To study the mechanical properties of a layered rock mass, the uniaxial compression test was carried out on the layered composite rock mass composed of sand and paraffin. The results showed that the locations of the high- and low-strength rocks were independent of the strength of the layered composite rock. The main failure site was not affected by the combination mode. Failure was mainly concentrated in the low-strength rock. The strengths of low- and high-strength rocks determined the lower and upper limits of the strength of the layered rock, respectively. When the thickness of the layered high-strength composite rock was >60%, the layered composite rock strength tended to be high; conversely, layered composite rock strength lowered the rock strength, and with increasing thickness, the layered composite rock strength was significantly enhanced. From the perspective of energy conversion, the effect of the thickness of the high-strength rock mass on the strength of the layered composite rock mass was analyzed.

With the continuous development of deep oil and gas resources, the number of high-temperature and high-pressure wells is increasing, and the complexity of fracturing operations is becoming more pronounced. The safe conduct of fracturing operations is crucial for improving production efficiency. To investigate the strength and safety of fracturing strings in high-temperature and high-pressure wells, a transient calculation model of temperature-pressure coupling was established for fracturing strings in high-temperature and high-pressure wells. The safety factor method and triaxial stress analysis were employed to evaluate the safety of fracturing strings in high-temperature and high-pressure wells. The method was used to assess the fracturing strings in high-temperature and high-pressure wells, and the effects of fracturing fluid injection rate and string size on the stress of the strings were analyzed. The results indicate that the axial force and equivalent stress at the wellhead of the fracturing string are the highest; as the fracturing time and injection rate of the fracturing fluid increase, the axial stress and equivalent stress at the wellhead of the fracturing string also increase. Small-sized pipes are conducive to improving the safety factor of fracturing pipe string operations. The research findings can provide theoretical guidance for designing fracturing strings for high-temperature and high-pressure wells.

To solve the problem of severe rock pressure near coal mining face tunnels, a high-pressure abrasive water jet slotting and roof breaking pressure relief technology is proposed. First, the laneway deformation mechanism and the process of hard rock slotting using high-pressure abrasive water jets under long-distance cantilever conditions are analyzed, and the crack initiation conditions of roof strata are obtained. Second, slotting tests under different slotting pressures, nozzle diameters, abrasive particle sizes and slotting times were carried out, and the slotting parameters of a high-pressure abrasive water jet on a typical roof rock were obtained. Finally, industrial application was carried out in the 81,403 working face of Huayang No.1 Mine. After the hydraulic roof slotting measures were implemented in the test area, the maximum axial force of the anchor cable was reduced to 67 kN, which was 35.5% lower than that of the comparison. The average stress of the coal seam was 15 MPa, which was approximately 25% lower than that of the comparison. The deformation of the tunnel in the experimental area was significantly controlled, with an average movement of 30.0% toward the roof and floor of the tunnel and an average movement of 23.2% toward the two sides of the tunnel. Compared with the movement in the comparison section, the movement toward the roof and floor of the laneway was 42.3% lower, and the movement toward the two sides was 38.2% lower. The industrial application results show that high-pressure abrasive water jet roof slotting and pressure relief technology can cut off the stress transmission path between the roof rock on both sides, effectively improve the stress state of the surrounding rock of the laneway, reduce the deformation of the roof, floor and two sides of the working face in the later stage of the mining laneway.

In this study, computational fluid dynamic (CFD) simulation of a vertical axis wind turbine (VAWT) geometry based on the Unsteady Reynolds–Averaged Navier–Stokes equations was investigated. In addition, the relationship between the geometric parameters of the VAWT and the two response variables, that is, moment and lift force, was determined using response surface methodology (RSM). Then, the Non-Dominated Sorting Genetic Algorithm (NSGA-II) was used to solve the multi-objective optimization problem. The results obtained from the RSM showed that the lift force of the turbine is more sensitive to the change in the blade chord length, and the output moment of the turbine is more sensitive to the change in the rotor radius. Using the NSGA-II multi-objective optimization algorithm and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, it was determined that among the optimal values of the independent variable, the most optimal response occurs in blade chord length = 0.18 m, rotor radius = 0.4 m, blade pitch angle = −3.27° and number of blades = 4. In these optimal values of the independent variables, the values of the dependent variables, which included the turbine's moment and the blades’ lift force, were obtained as 9.58 N m and 57.89 N, respectively.

To further investigate the propagation characteristics of shock waves and flame waves in H-type tunnel gas explosions, numerical simulation studies were conducted on a dual-source gas explosion model using Fluent software. Three distinct operational conditions were designed and modeled, leading to the following outcomes. The shock wave flow field parameters from dual sources (with equal source energy) are symmetrically distributed in the H-type tunnel, with high pressure and low flow velocity in the connecting roadway between the two shock waves. Under different conditions, the pressure is generally higher in closed tunnels (fully closed greater than semi-closed) and lower in open tunnels. The largest overpressure in non-explosion areas occurs at the closed ends and in the connecting roadway, while the areas bearing the greatest impulse are the shock wave reflection zones and pressure coupling regions. In closed conditions, the flame wave first moves forward and then propagates backward after the explosion, influenced by reflected waves and pressure differences between the ends and the tunnel. In open conditions, the pressure in the flame zone is lower than at both ends, inhibiting the forward propagation of the flame wave.

Global warming poses a serious challenge to the human environment, prompting us to rapidly develop new environmentally friendly fuels. However, the time and cost required to determine the physical properties of fuels are constrained by the related industries. In this paper, we propose a multiview features fusion method based on neural networks. This method uses the eight graph neural networks models as the basis of the multichannel network coordinator to extract the compound's molecular feature; also the functional groups in the compound are embedded with molecule weight as functional groups feature, and finally, combining the molecular view with the functional groups view for the prediction of compound flash point (FP). We used a data set consisting of 2200 hydrocarbons and oxygenated compounds for model training and testing. The results show that the model performance is improved in both after feature fusion. Finally, the ablation experiments demonstrate that the use of this method is effective and provides a new idea for fast and accurate screening of fuels. The Attentive FP-FG model was the most effective, with a mean absolute error of 4.395 K, root mean square error of 5.854 K, and R-squared score (R²) of 0.986.

The transient voltage stability of modern power systems is challenged by the increasing uncertainty on source-load dual sides. To cope with it, a robust optimal configuration method for dynamic VAR compensators (DVCs) is proposed. First, a series of power-flow scenarios are generated with uncertainty and correlation considered, where nonparametric kernel density estimation is used to predict the marginal distribution of wind speeds and combined Copula function is employed to characterize correlations between nearby wind farms. Then, the variation-of-information indicator is introduced to assess the similarity among power-flow (PF) scenarios, and representative PF (RPF) scenarios are screened with the help of Spectral clustering algorithm. Finally, locating and sizing of DVCs for RPF scenarios are achieved. By synthesizing the compensation schemes for RPF scenarios, the robust configuration scheme is suggested. Simulation studies in the modified New England 10-machine 39-bus system show the proposed method can ensure transient voltage security of the studied power system under source-load uncertainty.

To investigate the erosive damage effect of the acidic solution on sandstone, experiments were conducted to examine the macro- and micro-characteristics of sandstone samples under different acid etching times. Microscopic morphology changes, pore structure characteristics, mineral composition changes, and mechanical response characteristics were obtained before and after acid etching. Finally, a comprehensive evaluation index for acid etching damage was proposed. The results show that: (1) Acidic solution will erode the sandstone skeleton and cause the sandstone to collapse. The etched surface appears “flocculent” and yellow sediment is produced. (2) The pores in sandstone samples are mainly micro- and mesopores, and the total porosity increases exponentially with the duration of acid etching. The volume fraction of micropores can reach up to 81.5%. (3) The acid etching process of sandstone samples includes physical diffusion and chemical dissolution, which can be divided into four stages and three regions from the outside to the inside. After acid etching, the uniaxial compression failure mode of the sample changes from shear to mixed shear-tensile failure. (4) The comprehensive evaluation index based on three failure modes generated during the loading process shows good consistency with the overall changes of characteristics parameters such as elastic modulus, peak stress, and peak strain. The research findings of this paper can provide theoretical support for the assessment of rock mass stability and disaster prevention in acidic environments.