Search Results (1,727)

When compared with conventional replacement reinforcement methods, the method of replacement using a local reinforcement with an unsupported roof has the advantages of shortening the reinforcement cycle and reducing material loss, and many scholars have carried out useful explorations thereof. At present, the formula for the bearing capacity of reinforcement by replacing concrete in the Code does not consider the effect of stress hysteresis on the parts of the reinforcement; so when the initial stress level is greater than 0.4, the Code’s strength utilization coefficient of 0.8 for the new concrete in the replaced area is on the side of insecurity. In this study, we are trying to improve and supplement the formula in the Code through the following work. Firstly, 18 groups of shear wall models were constructed using a VFEAP finite element analysis program to analyze the bearing performances of the shear walls after the replacement. The results showed that the replacement concrete strength, the initial stress level and the size of the replaced area had a significant influence on the bearing capacity of the shear wall after the replacement. Secondly, utilizing the replacement concrete strength, the initial stress level and the size of the replacement area as key parameters, then introducing the strength improvement coefficient considering the constraints of the stirrups, the modified strength utilization coefficient of new concrete in the replaced area was formulated. Finally, based on the modified strength utilization coefficient, the replacement bearing capacity formulas for the one-batch, two-batch, and three-batch replacements were derived, and an N-batch replacement bearing capacity formula was regressed and fitted on the basis of these equations, which are less discrete and more secure than the Code’s formula. Full article

In order to solve the problems of existing spiral fertilizer apparatuses, such as the variation in cavity filling rate with rotational speed, fluctuation of fertilizer discharge flow, and inability to discharge fertilizer precisely, a triple-head gradually reducing spiral fertilizer apparatus is designed, which gradually compresses fertilizer particles through the triple-head reducing fertilizer spiral structure to achieve complete cavity filling and uniform fertilizer discharge. The main factors that affect the particle motion state and the structural size of the spiral fertilizer through theoretical analysis are determined, and its theoretical fertilizer discharge amount and rotational speed are calculated. Using EDEM (Discrete Element Method Software 2022) to establish a simulation model of a single-head gradually reducing fertilizer apparatus, the spiral lead reduction percentage x₁, spiral diameter reduction percentage x₂, and rotational speed x₃ are determined as experimental factors, and the filling rate μ and spiral torque Y_average are used as experimental indicators to conduct a simulation study on the secondary universal rotation combination design experiment. The results show that when the rotational speed is 95 r/min, the spiral lead reduction percentage is 60.00~73.21%, the spiral diameter reduction percentage is 86.55~97.05%, the filling rate μ is greater than 95%, and the spiral torque Y_average is less than 16 N·m. In order to further improve the uniformity of fertilizer discharge and ensure the controllable accuracy of fertilizer discharge, comparative verification experiments are conducted on single-, double-, and triple-head gradually reducing spiral fertilizer discharge devices and ordinary spiral fertilizer discharge devices. The results show that the precision of the gradually reducing spiral fertilizer apparatus is better than that of the ordinary spiral fertilizer apparatus. Moreover, it is determined that the three-head style performed best. The triple-head gradually reducing spiral fertilizer apparatus is also validated by randomly adjusting six rotational speeds. The experiment results show that the average deviation of the fertilizer discharge flow rate of the fertilizer apparatus from the preset value is 3.16%. The two have a minor deviation, and the fertilizer precision is high. Precise control of the amount of fertilizer discharged can be achieved by adjusting the rotational speed, and the research can provide a specific reference for the improved design and precise control of the spiral fertilizer apparatus. Full article

In view of issues affecting manual on-demand sowing of spinach, such as high working intensity, low seeding efficiency, and high cost, a mechanized precision seeding device for spinach seeds is proposed, based on a spoon-wheel including a combined cell with two intersecting surfaces. The key structure was designed, and motion analysis was conducted employing SolidWorks software (2022). Additionally, the seeding process was simulated using EDEM simulation software (2022). The number of spoons, the radius of the spoons, and the speed of the seed wheel were chosen as the influencing factors, while the qualified index, reseeding index, and missing index were utilized as the evaluation indexes. A response surface method test based on the Box–Behnken design was carried out, and the test results were analyzed to obtain the optimal parameter combination for the seed-metering device. The field test results showed that when the rotation speed was 18 rpm, the radius of each seed spoon was 2.5 mm, and the number of seed spoons was 50, the average qualified index was 90.89%, the reseeding index was 8.22%, and the missed seeding index was 0.89%. The test results satisfy the technical production requirements for spinach seed sowing. Full article

During the operation of a shaped hole seed-metering device, poor seed-filling quality and inconsistent seed-casting points lead to poor seed spacing uniformity, especially in a one-chamber double-row seed-metering device. To solve this problem, a soybean double-row seed-metering device with double-beveled seed guide groove was designed to ensure a high single-seed rate and seed-casting point consistency. Through the theoretical analysis of the working process of the seed-metering device, dynamic and kinematic models of the seeds were established, and the main structural parameters of the seed discharge ring, triage convex ridge, shaped hole, and seed guide groove were determined. The main factors affecting the seeding performance were obtained as the following: the inclination angle of the triage convex ridge, the radius of the shaped hole, and the depth of the seed guide groove. A single-factor test was carried out by discrete element simulation to obtain the inclination angle of the triage convex ridge α₃ = 29°, the radius of the shaped hole r₁ = 4.16–4.5 mm, and the depth of the seed guide groove l₁ = 0.49–1.89 mm. A two-factor, five-level, second-order, orthogonal rotation combination test was conducted to further optimize the structural parameters of the seed-metering device. The two test factors were the radius of the shaped hole and the depth of the seed guide groove, and the evaluation indices were the qualified rate, replay rate, and missed seeding rate. The results showed that the optimal combinations of the structural parameters were the radius of the shaped hole r₁ = 4.33 mm and the depth of the seed guide groove l₁ = 1.20 mm. Subsequent bench testing demonstrated that the seed discharge’s qualified rate was above 94% at operating speeds of 6–10 km/h, and the seeding performance was stable. The final results of the soil trench test showed that the seed-metering device exhibited a qualified rate of 93.31%, replay rate of 2.04%, and missed seeding rate of 4.65% at an operating speed of 8 km/h. This research outcome may serve as a valuable reference and source of inspiration for the innovative design of precision seed-metering devices. Full article

In order to solve the problems of high impurity rate and large loss rate in the whole fresh peanut harvesting and production process in hilly areas of China, the method of computational fluid dynamics (CFD) and discrete element (DEM) coupling is used to examine the gas–solid two-phase simulation of the cleaning device of the crawler fresh-peanut-picking combine harvester. In addition, the Box–Behnken test method is used to analyze the influence of different parameters on the impurity content and loss rate of materials in the cleaning process by taking the fan speed, feed amount, and air inlet angle as the test factors, and the optimal combination of working parameters is sought. The simulation results show that when the fan speed is 2905.07 r/min, the feed rate is 0.80 kg/s, the air inlet angle is 43.14°, the impurity content is 7.32%, and the loss rate is 4.78%. Compared with the simulation test results, the impurity content is increased by 0.68%, and the loss rate is increased by 1.24%, which verifies the reliability of the simulation model, and the research results provide some technical support for the improvement of the cleaning device in the later stages. Full article

Strain rockburst is a severe failure phenomenon caused by the release of elastic strain energy in intact rocks under high-stress conditions. They frequently occur in deep tunnels, causing significant economic losses, casualties, and construction delays. Understanding the factors influencing this disaster is of significance for tunnel construction. This paper first proposes a novel three-dimensional (3D) discrete element numerical analysis method for rockburst numerical analysis considering the full stress state energy based on the bonded block model and the mechanics, brittleness, integrity, and energy storage of the surrounding rock. This numerical method is first validated via laboratory tests and engineering-scale applications and then is applied to study the effects of compressive and tensile strengths of rock mass, tunnel depth, and lateral pressure coefficient on strain rockburst. Meanwhile, sensitivity analyses of these influencing factors are conducted using numerical results and systematic analysis methods, and the influence degree of each factor on the rockburst tendency is explored and ranked. The results reveal that laboratory tests and actual engineering conditions are consistent with numerical simulation results, which validates the rationality and applicability of the novel rockburst analysis method proposed in this paper. With the increase in compressive strength, the stress concentration degree, energy accumulation level, maximum stress difference, and maximum elastic strain energy within the rock mass all increase, leading to a stronger rockburst tendency. Tunnel depth and the lateral stress coefficient are positively correlated with rockburst tendency. As the lateral pressure coefficient and tunnel depth increase, rockburst tendency exponentially increases, while the maximum stress difference and maximum elastic strain energy within the rock mass also increase. The influence degree of each factor is ranked from highest to lowest as follows: tensile strength, lateral pressure coefficient, compressive strength, and tunnel depth. The research results provide theoretical support and technical guidance for the effective prediction, prevention, and control of rock burst disasters in deep tunnels. Full article

As urbanization accelerates and surface space becomes increasingly scarce, the development and utilization of urban underground space have become more critical. The sand–fine mixture soils commonly found in river-adjacent and coastal areas pose significant challenges to the design and construction of underground structures due to their unique mechanical properties. In soil mechanics, the minimum and maximum void ratios are crucial indicators for assessing soil compressibility, permeability, and shear strength. This study employed the discrete element method (DEM) to simulate the minimum and maximum void ratios of sand–fine mixtures under various conditions by setting six fine contents and three mean diameter ratios. The results indicate that as the fine content increases, these void ratios exhibit a trend of initially decreasing and then increasing, which can be effectively modelled using a single-parameter quadratic function. Additionally, the initial shear modulus was closely related to the uniformity of contact distribution at the microscopic level within the specimens. This study also introduced a dimensionless parameter that simultaneously described changes in contact distribution and initial shear modulus. Full article

Concentrated solar power (CSP) technology relies on thermal energy storage to extend operating hours, making the selection of heat storage media crucial for system efficiency. Bauxite powder, known for its availability and high-temperature stability, emerges as a potential alternative to conventional materials in CSP systems. This study employed the discrete element method to investigate the influence of particle shape on the packing and heat transfer characteristics of non-spherical particles. The research focused on assessing the impact of particle sphericity by comparing spherical particles with non-spherical shapes, including ellipsoids and cylinders, and exploring the effect of varying the aspect ratio (AR) of the cylindrical particles. Particle sphericity significantly influenced packing morphology, with the cylindrical particles exhibiting distinct structural patterns that were absent in the ellipsoidal particles, and strongly affected heat transfer, as observed in the average temperature variations within the packed bed over time. The cylinders with higher aspect ratios demonstrated enhanced heat transfer rates, driven by the increased contact area and coordination numbers, despite their predominant misalignment with the heat flux direction. These insights are valuable for optimizing thermal energy storage media in CSP systems. Full article

After hydraulic fracturing, unconventional reservoirs frequently encounter challenges related to limited effective proppant support distance and suboptimal proppant placement. Due to the strong heterogeneity of offshore reservoirs, which causes varying fracture roughnesses depending on different lithologies, a systematic study of the relationship between roughness and proppant transport could optimize operational parameters. This study incorporates the box dimension method for fractal dimension analysis to quantify roughness in auto-correlated Gaussian distributed surfaces created by true triaxial tests. Combined with the numerical analysis of (computational fluid dynamics) CFD-DEM (discrete element method) for bidirectional coupling, the laws of proppant deposition and transport processes within fractures with different roughnesses are obtained through comparative verification simulations. The results show that for rougher fractures of shale, the proppants are transported farther, but at JRC_52, (joint roughness coefficient), where there may be plugging in curved areas, there is a risk of near-well blockages. Compared to the smooth model, fluctuations in JRC_28 (tight sandstone) drastically increase turbulent kinetic energy within the fracture, altering particle transport dynamics. Moreover, smaller proppants (d/w ≤ 0.3) exhibit better transport capacity due to gravity, but the conductivity of the proppant is limited when the particles are too small. A d/w of 0.4 is recommended to guarantee transport capacity and proppant efficiency near the well. Additionally, proppants injected sequentially from small to large in shale fractures offer optimal propping effects, and can take advantage of the better transport capacity of smaller proppants in rough fractures. The large proppant (d/w = 0.8) is primarily deposited by gravity and forms a sloping sand bed, which subsequently ensures the aperture of the fractures. This research provides a fresh perspective on the influence of fracture roughness on proppant transport in offshore unconventional reservoirs and offers valuable considerations for the order of proppant injection. Full article

As a fundamental element of the transportation system, traffic signs are widely used to guide traffic behaviors. In recent years, drones have emerged as an important tool for monitoring the conditions of traffic signs. However, the existing image processing technique is heavily reliant on image annotations. It is time consuming to build a high-quality dataset with diverse training images and human annotations. In this paper, we introduce the utilization of Vision–language Models (VLMs) in the traffic sign detection task. Without the need for discrete image labels, the rapid deployment is fulfilled by the multi-modal learning and large-scale pretrained networks. First, we compile a keyword dictionary to explain traffic signs. The Chinese national standard is used to suggest the shape and color information. Our program conducts Bootstrapping Language-image Pretraining v2 (BLIPv2) to translate representative images into text descriptions. Second, a Contrastive Language-image Pretraining (CLIP) framework is applied to characterize not only drone images but also text descriptions. Our method utilizes the pretrained encoder network to create visual features and word embeddings. Third, the category of each traffic sign is predicted according to the similarity between drone images and keywords. Cosine distance and softmax function are performed to calculate the class probability distribution. To evaluate the performance, we apply the proposed method in a practical application. The drone images captured from Guyuan, China, are employed to record the conditions of traffic signs. Further experiments include two widely used public datasets. The calculation results indicate that our vision–language model-based method has an acceptable prediction accuracy and low training cost. Full article

Geological reservoirs are widely used for storing or disposing of various fluids and gases, including groundwater, wastewater, carbon dioxide, air, gas, and hydrogen. Monitoring these sites is essential due to the stored assets’ economic value and the disposed materials’ hazardous nature. Reservoir pressure monitoring is vital for ensuring operational success and detecting integrity issues, but it presents challenges due to the difficulty of obtaining comprehensive pressure distribution data. While direct pressure measurement methods are costly and localized, indirect techniques offer a viable alternative, such as inferring reservoir pressure from surface deformation data. This inversion approach integrates a forward model that links pressure distribution to deformation with an optimization algorithm to account for the ill-posed nature of the inversion. The application of forward models for predicting subsidence, uplift, and seismicity is well-established, but using deformation data for monitoring underground activity through inversion has yet to be explored. Previous studies have used various analytical, semi-analytical, and numerical models integrated with optimization tools to perform efficient inversions. However, analytical or semi-analytical solutions are impractical for complex reservoirs, and advanced numerical models are computationally expensive. These studies often rely on prior information, which may only sometimes be available, highlighting the need for innovative approaches. This study addresses these challenges by leveraging advanced numerical models and genetic algorithms to estimate pressure distribution from surface deformation data without needing prior information. The forward model is based on a discrete Green matrix constructed by integrating the finite element method with Python scripting. This matrix encapsulates the influence of reservoir properties and geometry on the displacement field, allowing for the rapid evaluation of displacement due to arbitrary pressure distributions. Precomputing Green’s matrix reduces computational load, making it feasible to apply advanced optimization methods like GA, which are effective for solving ill-posed problems with fewer observation points than unknown parameters. Testing on complex reservoir cases with synthetic data showed less than 5% error in predicted pressure distribution, demonstrating the approach’s reliability. Full article

Grey cast iron releases energy in the form of stress waves when damaged. To analyse the evolution of the physical and mechanical properties and acoustic emission characteristics of grey cast iron under uniaxial compression, acoustic emission signals were collected at different rates (0.5, 1, and 2 mm/s). Combined with load-time curves, damage modes were identified and classified using the parametric RA-AF correlation analysis method. The results indicate the loading rate effects on the strength, deformation, acoustic emission (AE), and energy evolution of grey cast iron specimens. The acoustic emission counts align with the engineering stress–strain response. To better illustrate the entire failure process of grey cast iron, from its internal microstructure to its macroscopic appearance, X-ray diffraction (XRD) and optical microscopy (OM) were employed for qualitative and quantitative analyses of the material’s internal microstructural characteristics. The equivalent crystal model of grey cast iron was constructed using a Particle Flow Software PFC2D 6.00.30 grain-based model (GBM) to simulate uniaxial compression acoustic emission tests. The calibration of fine parameters with indoor test results ensured good agreement with numerical simulation results. Acoustic emission dynamically monitors the compression process, while discrete element particle flow software further analyses the entire damage process from the inside to the outside. It provides a new research method and idea for the study of crack extension in some metal materials such as grey cast iron. Full article

Ore collection is very important in deep-sea mining for seafloor massive sulfide (SMS). In view of the characteristics of SMS ores produced by mechanical crushing, which contain coarse particles and a wide particle size distribution, in-depth research on the collection process with a device combining a rotary crushing head and a flat suction mouth was conducted. In this paper, solid–liquid two-phase flow in the hydraulic collection process with a drum rotation is carried out using the computational fluid dynamics-discrete element method (CFD-DEM), and the flow field characteristics and particle motion characteristics are analyzed. The results indicate that particles with a maximum diameter of 20 mm can be effectively collected when the suction velocity is 3 m/s. The collection process of SMS mainly goes through three stages: particle disturbance start-up, partial particle influx, and stable collection. In addition, the appropriate drum speed facilitates the collection of SMS ore. Finally, the correctness of the numerical method was assessed using similarity experiments. This work can be used to guide the design of underwater mining equipment. Full article

Modeling the dynamic properties of wood and wood-based composites is a challenging task due to naturally growing structure and moisture-dependent material properties. This paper presents the finite element modeling of plywood panels’ dynamic properties. Two panels differing in thickness were analyzed: (i) 18 mm and (ii) 27 mm. The developed models consisted of individual layers of wood, which were discretized using three-dimensional finite elements formulated using an orthotropic material model. The models were subjected to an updating procedure based on experimentally determined frequency response functions. As a result of a model updating relative errors for natural frequencies obtained numerically and experimentally were not exceeding 2.0%, on average 0.7% for 18 mm thick panel and not exceeding 2.6%, on average 1.5% for 27 mm thick panel. To prove the utility of the method and at the same time to validate it, a model of a cabinet was built, which was then subjected to experimental verification. In this case, average relative differences for natural frequencies of 6.6% were obtained. Full article

The discrete-element method (DEM) has become a pivotal tool for investigating soil–plant interactions in agricultural and environmental engineering. This review examines recent advancements in DEM applications, focusing on both the challenges and opportunities that shape future research in this field. This paper first explores the effectiveness of DEM in simulating soil and plant materials, including seeds, roots, and residues, highlighting its role in understanding interactions that affect agricultural practices. Challenges such as long computation times and the complexity of determining accurate contact parameters are discussed, alongside emerging methods like machine learning that offer potential solutions. Notable advancements include the integration of machine learning algorithms for contact parameter estimation, the use of expanded particle models for dynamic processes, and the development of new techniques for detailed post-processing of DEM simulations. The review also identifies key future research directions, including the incorporation of environmental factors such as air and water, and the exploration of residue management for carbon storage and erosion prevention. By addressing these challenges and seizing these opportunities, future research can enhance the accuracy and applicability of DEM models, advancing our understanding of soil–plant interactions and contributing to more sustainable agricultural and environmental practices. Full article