Energy Science & Engineering最新文献

To investigate the influence of the thickness of the intermediate water layer and the thickness of the explosive on the quality of underwater explosive welding of Q235R carbon steel 304 stainless steel, underwater explosive welding experiments were designed under different process conditions. The bonding speed and bonding pressure of the base composite plate during the welding process were tested, and the waveform and mechanical properties of the bonding interface of the composite plate were tested. The experimental results show that its tensile strength is between 444.2750 and 464.7724 MPa, with an average tensile strength of 454.5337 MPa, which is 7%–13% higher than the composite plate prepared by the hot rolling process. When the thickness of the explosive layer and the intermediate water layer is 10 mm and the bonding pressure is 865 MPa, the welding is successful. When the thickness of the explosive layer is 20 mm, as the thickness of the intermediate water layer increases from 10 to 30 mm, the bonding pressure decreases from 8668 to 3245 MPa, and the welding is successful. However, when the thickness of the intermediate layer was further increased to 40 mm, the welding failed and the bonding pressure dropped to 1084 MPa. Due to the fixed thickness of the intermediate water layer, increasing the thickness of the explosive layer will weaken the mechanical strength of the composite plate. Our research provides theoretical support for the preparation of composite metals by explosive welding, which is of great significance for promoting the development of explosive welding technology.

The geological structure of coal mines and the precise prediction of coal seam gas content are key factors in creating the transparent working face, and they also represent an important aspect of intelligent coal mining. The traditional technology of coal seam geological construction and gas content prediction is not advanced. This paper presents a methodology for 3D implicit geological modeling and visualization using Gempy and PyVista libraries, as well as gas prediction and distribution based on the Scikit-learn library, all of which are underpinned by machine learning techniques. Under this method, the geological modeling of coal seam was converted to the kriging interpolation algorithm based on machine learning of coal seam thickness data. The problem of coal seam gas content is converted into a regression prediction problem of coal seam characteristic values and gas content target values based on machine learning. The pykrige package under Python is used to interpolate the obtained coal seam thickness. Based on the linear regression prediction model, loss function and other prediction methods and algorithms, the accurate prediction of coal seam gas content based on borehole data is realized. Under the above various operations, a 3D geological model of the mine and the gas content distribution map of the coal seam are finally obtained. Compared to actual borehole data and gas geological maps, this method offers high precision and enhanced efficiency.

The investigation into the internal flow characteristics of the bulb tubular pump device has important practical significance for improving both optimal design and operation stability. This paper uses different RANS/LES turbulence models to conduct numerical simulation research on the bulb tubular pump model device, specifically focusing on the differences in internal flow characteristics under the small flow stall condition 0.5Q_des and the design condition 1.0Q_des. The macroscopic energy characteristics of different turbulence models are verified through experiment tests, revealing that the DDES turbulence model provides better predicted hydraulic performance under stall conditions. The numerical difference under low flow conditions is mainly due to the different degrees of turbulent flow field analysis, while the analysis degree of high-efficiency flow rate conditions with uniform internal flow remains similar. The vortex identification method Omega is used to visualize the vortex structure characteristics of the time-averaged flow field, uncovering large-scale stall vortex structures under small flow conditions, with the blending RANS/LES turbulence model offering superior resolution of vortex structures. Furthermore, the paper deduces the calculation method of the RANS/LES turbulence model pulsation entropy production based on the SST turbulence model pulsation entropy production calculation formula. A comprehensive investigation of the local power loss characteristics of the main flow-passing components—impeller, diffuser, and bulb—reveals that the blades and wall surfaces are the main contributors to increase in power losses. The comparison shows that the DDES turbulence model provides more accurate predictions of the hydraulic performance of stall conditions and visualizing flow field characteristics.

High impedance faults (HIFs) can lead to crucial damage to the utility grid, such as the risk of fire in material assets, electricity supply interruptions, and long service restoration times. Due to their low current magnitude, conventional protective equipment, such as overcurrent relays, cannot detect these faults. Alternatively, the waveform and variation range of current in HIFs are similar to other phenomena, such as linear and nonlinear load changes and capacitor banks. This paper employs a support vector machine (SVM) classification algorithm that demonstrates reliable accuracy and discrete wavelet transform (DWT) in HIF detection. First, the data set containing measured current signals of HIFs is collected to implement this approach. Then, DWT decomposes it to extract the features of each sample in the data set. The extracted features from this part are used as input to the SVM classification algorithm. The proposed idea is initially implemented on the IEEE 34-bus distribution test network. The proposed method achieves high capability and accuracy in detecting high-impedance faults. The proposed method is also applied to a real power distribution network in Markazi Province of Iran, yielding satisfactory results. EMTP-RV simulation software is used to simulate and evaluate the proposed method for power network modeling. Moreover, MATLAB software is used for feature extraction, and Python programming language in Google Colab and Spyder environment is applied to implement the SVM algorithm. The simulation results confirm the high accuracy of the suggested method. The main criteria obtained by the proposed method include accuracy, sensitivity, specificity, precision, F-score, and Dice, which are 99.581%, 98.684%, 100%, 100%, 99.338%, and 99.338%, respectively, for the test network, and 97.94%, 93.45%, 100%, 100%, 96.614%, and 96.618%, respectively, for the real power distribution network.

Climate change is caused by an increase in global temperatures, known as global warming. This is largely attributed to the rising levels of greenhouse gases in the atmosphere, with carbon dioxide emissions from fossil fuel power plants being the major culprit. Implementing carbon capture, utilization, and storage (CCUS) strategies is essential to effectively mitigate climate change. However, the complexity and diverse range of emission sources, which vary in terms of volume, composition, location, type, and industry, demand a multifaceted strategy that involves the development of a broad spectrum of carbon capture and storage (CCS) technologies, materials, and processes. This review article provides an in-depth review of the three dominant material types utilized globally for CO2 capture from flue gases: Absorbents, Membranes, and Adsorbents (AMA). The author examines the benefits and drawbacks of employing different forms of AMA in post-combustion capture, highlighting recent breakthroughs in experimental and theoretical modeling, simulation, and optimization studies. The review also explores the strengths and limitations of various AMA configurations, including single-stage, multi-stage, and hybrid systems, identifying knowledge gaps and opportunities for advancement in this field. While two-stage hybrid configurations have emerged as the most promising approach to maximizing CO2 recovery, energy efficiency, and cost savings, further in-depth techno-economic evaluations are required to determine the most effective and viable configuration within this hybrid category and pinpoint the optimal solution for real-world applications.

The mechanism of cavity growth in a UCG process is mainly dependent on the presence of fractures and microcracks in the coal seam. In this study, the rate of cavity growth and the crack propagation mechanism in brittle coal samples under high thermal conditions are investigated using a two-dimensional particle flow code (PFC2D). Coal samples with different cleats' orientation under thermal environments are numerically simulated. The numerical modeling results show that the induced thermal stress is one-third of the coal sample failure stress. This is due to the increase in particles' volume, the change in normal force between the particles' bonds, and the changes in thermal and mechanical parameters resulting from the applied source temperature, which breaks the bond around the particle. The effects of heat and heterogeneity on the strength of coal samples are also studied under different temperatures ranging from 50°C to 900°C. The results showed that the presence of high-strength coal seams reduces the formation and propagation of heat-induced cracks, consequently reducing the cavity growth rate. The soft coal sample has more plasticity, and the cavity growth rate in the soft coal is more than that of the hard coal. The elasticity modulus and uniaxial compressive strength decrease with the increase of the source temperature and the sample begins to deform in a plastic mode. Also, increasing temperature causes an exponential increase in thermal stress. From the fracture mechanics point of view, knowing the conditions and the mechanism of pre-existing crack propagation in the coal seam can lead to a correct understanding of cavity growth during the UCG process.

Lithology identification is a crucial task in coal underground gasification projects, serving as a prerequisite for ensuring the safe operation of these endeavors. The inherent complexity in the relationship between logging parameters and lithological compositions creates ambiguity, leading to biases in traditional logging interpretation methodologies. We introduce a lithological prediction model, the deep residual shrinkage network (DRSN), which integrates residual networks, attention mechanisms, and soft-thresholding strategies. This network mitigates the gradient vanishing issue common in traditional neural networks and enhances the model's focus on essential features, thereby improving its ability to capture critical information. Acoustic, bulk density, neutron, gamma, and deep resistivity logs are used as inputs, with lithology as the output. A comparative analysis between the DRSN and other newer lithological prediction models is conducted. Blind well testing results demonstrate the superior performance of the DSRN, with higher Accuracy, Precision, Recall, and F1 Scores of 0.8221, 0.7198, 0.8004, and 0.7465, respectively. This study provides a novel and rapid method for lithology evaluation of strata in the early stages of underground coal gasification.

Addressing the demand for cooking energy is crucial in developing countries, particularly as populations rise and natural resources decline. Abundant and freely accessible solar energy offers a promising solution to this challenge. Despite the global development of various solar cooking devices, BTSC has not achieved the anticipated level of popularity. This study aims to explore the factors hindering the adoption of BTSC and to discuss how these findings can inform the design of future BTSC models, focusing on four key areas. These include proposing innovative design features to align with the Indian lifestyle, identifying opportunities in nonurban areas, and providing commercialization strategies. The evaluation relies upon the Weight-decision method, results of ethnographic study, potential user analysis using qualitative techniques, and an assessment of commercialization strategies by examining feasibility. The study highlights the need for hybridizing the BTSC by incorporating features such as active tracking, a secondary heating source (battery or grid-operated heating coil), and self-power generation through thermoelectric conversion to increase user acceptance. nonurban users-including those in semi-urban, rural, tribal, nomadic, and trekking communities-demonstrate suitability for efficient solar cooker use. The continued subsidy by the Haryana government has crucially promoted BTSC adoption. Additionally, 30 out of 34 states fall within the high radiation zone, indicating favorable conditions for the adoption of BTSC. However, the assessment also reveals a limitation in the new design related to indoor cooking. These design features should be incorporated into the prototype in the future.

This study is concerned with the development of a finite element (FE) model using COMSOL Multiphysics for hybrid greenhouse dryers in humid subtropical climate of Ranchi, under no-load condition. The temperature and humidity distribution in the dryer was visualized using the FE model and compared against experimental data to evaluate its reliability in simulating dryer behavior. The performance evaluation conducted under no load condition showed that the efficiency of the proposed dryer was 42.35% and maximum overall convective heat transfer coefficient was 6.27 W/m²°C. The temperature inside the dryer ranged from 55°C to 80°C, while the relative humidity ranged from 37% to 45%. The developed FE model using COMSOL Multiphysics software, shows a close agreement between experimental and predicted results with a lower statistical error. Overall, this study's findings suggest that the hybrid greenhouse dryer could be a useful tool for drying crops in humid subtropical climates and the FEM could be a valuable tool for future research.

The rock burst is one of the major dynamic disasters in deep underground engineering, such as coal mining, and has become a significant technical challenge urgently requiring solutions in rock mechanics and engineering. In the related research, there are many reports on the stability analysis of the laminar split structure under static loads and few reports on the stability analysis under dynamic loads. This paper addresses the stability of surrounding rock in deep roadways, focusing on the key factor of disturbed load. First, the paper theoretically establishes the control equation for analyzing the dynamic stability of the laminar split structure in the coal wall of roadway, deriving the minimum critical load for instability of the laminar split structure under two constraint conditions: simply supported at both ends and fixed at both ends. Second, using discrete element software, the paper analyzes the influence of disturbing load on the stability and energy accumulation characteristics of the surrounding rock of the roadway. It examines the variation patterns of stress in the surrounding rock, deformation of both sides and roof-floor, distribution of plastic zones, and energy accumulation characteristics with respect to time t and the intensity of disturbing load p_max. Finally, the paper analyzes the stability of surrounding rock in a mine involved in a rock burst accident. The research results provide a basis and reference for analyzing the risk of rock burst under similar conditions.