Computational Particle Mechanics最新文献

The rotary kiln has been widely used in the chemical pyrolysis industry for decades. This study focused on elucidating the impact of incorporating baffles on the motion of pesticide waste salt (WS) particles during pyrolysis in a rotary kiln. Through discrete element method software simulation, the effects of different baffle lengths (75 cm, 120 cm, and 150 cm) and baffle angles (0°, 15°, and 45°) were investigated to provide valuable insights into predicting the movement and heat transfer of WS. The simulation results indicated that the length of the baffle had a significant impact on the behavior of WS particles with longer baffles resulting in enhanced mean residence time and velocity of particles but the reduced temperature variations. These changes were accompanied by a cyclic pattern of initially rising and subsequently falling velocities. Conversely, the baffle setting angle exhibits an insignificant impact in most cases. Notably, the inclusion of baffles enhanced the elastic forces generated by particle collisions, as well as friction from rolling and relative sliding, which led to enhanced particle dispersion and rotation of WS particles. The findings confirmed that incorporating baffles positively affected both particle movement and heat transfer, thereby relieving the burden associated with subsequent management and disposal processes.

Establishment of discrete element model of hilly red soil is an important means to carry out the interaction mechanism of soil tillage components and optimize the tillage components. The Hertz–Mindlin with bonding model was selected as the discrete element model for the hilly red soil due to its viscosity and ease of consolidation. To calibrate the parameters of the discrete element model, simulation experiments were designed based on the Box–Behnken experimental method to determine both angle of response (AOR) and penetration resistance (PR). The results indicate that the AOR is 40.67º with a soil–soil coefficient of restitution, coefficient of static friction, and coefficient of rolling friction of 0.594, 1.159, and 0.193, respectively; the PR is 517.11 N with a soil shear modulus, soil–steel coefficient of static friction, and critical normal stress of 10.1 MPa, 0.457, and 14.892 kPa, respectively; and the relative error of AOR and PR between simulation and actual measurements is 2.22 and 2.48%, respectively. Finally, the discrete element model was verified through ditching simulation and field experiment using a spiral opener. The results show that the relative error of resistance torque between simulation and field experiments is 2.18%; the relative errors of ditch depth, height of soil ridge on the left and right sides, and soil throwing distances on the left and right sides between simulation and field experiments are 4.68, 3.96, 10.24, 5.99, and 10.64%, respectively.

In recent years, digital core technology as an emerging numerical simulation method has been widely used in various fields. This study investigates the unsaturated microscopic seepage of gas–water phases in the pore-throat structure of sandstone. First, the real three-dimensional pore-throat structure of sandstone is extracted by digital core CT scanning technique. Then, a 3D numerical model that used in the unsaturated microscopic seepage is established based on the visualization image processing technology. Next, the two-phase unsaturated seepage coupling equations are developed by using the two-phase volume-averaged momentum equation and the continuity equation. Finally, by combining the two-phase saturation relation, the van Genuchten model, and the Mualem model, we realize the simulation of the two-phase unsaturated seepage in a real pore-throat structure of sandstone. The results demonstrate that the effective porosity and permeability of the model are 14.97% and 21.5 mD, respectively. The variation of wetting phase saturation is not uniform due to the existence of dominant channels in the unsaturated seepage process. The streamlines at the large pore throat are denser than elsewhere, and the velocity of the fluid is faster. The relative permeabilities of the two phases at different positions in the model are similar. Moreover, the shape of the relative permeability curve is concave. The final relative permeability of the non-wetting phase is approximately equal to 1.

The paper presents a numerical study of violent liquid sloshing on a two-dimensional rectangular tank of small dimensions by the Moving Particle Semi-implicit method. The numerical model considers a surface tension model to smoothly track the surface behavior, for this, pressure impact results were compared with and without a surface tension model. Also, the profiles during the run-up and run-down breaking waves on the sloshing process are compared with experimental results from the literature of similar scale. From these comparisons is highlighted the importance of the surface tension model on small dimensions on sloshing breaking waves, to an accurate simulation process, is showed by comparing the impact pressure with experimental literature results. Particle discretization for the numerical test considers two scales dimensions close to the experimental test from the literature, and two oscillation periods close to the natural resonant period of the fluid in the tank.

Aiming at the difficult problem that the surface of the chute of the mulching device is easy to be clogged with clay when the starter mulching machine operates under the wet and sticky soil environment, the study designed a bionic convex bag chute based on the effect of bionic adhesion reduction and studied its adhesion reduction and desorption characteristics to further explore the mechanism of bionic convex bag adhesion reduction and desorption. Firstly, according to the surface characteristic parameters of the convex packet microstructure of the dung beetle head, 13 kinds of bionic convex packet soil chute were designed in Solidworks2023 software, and Hertz-Mindlin with JKR model was chosen as the contact model of wet soil particles, and then, the simplified model of the mulching device was imported into EDEM2022.2 software for the mulching simulation analysis. Finally, the soil adhesion simulation data were imported into Design-Expert13 software and analyzed by Box–Behnken experimental design and results to obtain the optimal dimensions of the surface bump structure of the biomimetic chute as follows: The bump diameter is 4.68 mm, the height of the bump is 1.5 mm, and the area of the bump is 47.86%. Field tests were carried out at the off-campus test base to verify the reliability of the simulation analysis, and it is known through orthogonal tests that the four factors of the bionic convex bag skidding trough adhesion test indexes affect the amount of adherent soil in the order from the largest to the smallest, namely, the soil moisture content, the type of skidding troughs, scraper spacing, and the operating speed. The results of the validation and comparison test show that the viscosity reduction and desorption effects of the bionic convex bag chute are I, II, and III in descending order, and the test results are highly consistent with the results obtained in the EDEM simulation, which verifies the reliability of the simulation model, among which the viscosity reduction of the bionic convex bag chute I is the best, and the average amount of soil adhesion in the soil with water content of 20%, 25%, and 30% is reduced by 36.27%, 17.08%, and 9.57%, respectively, compared to that of the prototypical chute. The results of the study can provide a feasible research method for the investigation of the viscosity reduction mechanism of the soil touching parts of the ridging mulching machine and the optimization design of the structural improvement.

In the numerical simulation of particle flow code (PFC^2D), the values of micro-parameters directly influence the macroscopic mechanical parameters and overall performance of the numerical model. However, traditional methods for determining micro-parameters often require extensive manual trials and adjustments, leading to a highly blinded, time-consuming, labor-intensive process with limited accuracy. This paper employs the discrete element software PFC^2D in combination with four machine learning algorithms: support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), and xtreme gradient boosting (XGBoost), to analyze the sensitivity of PFC parameters. The machine learning algorithms use 6 particle flow parameters encompassing 156 sets of data, as input variables, with the model's peak stress and elastic modulus (E) as output variables. Simultaneously, three performance evaluation metrics used to assess the performance of the algorithms. The research results indicate that the RF algorithm outperforms other models in simulating the test set of mesoscopic parameters, with the highest trend evaluation index. The parameter pb_coh has the greatest positive impact on the model's peak stress, while the parameter deform emod has the greatest positive impact on the model's elastic modulus. The machine learning algorithms provide a better method for parameter calibration, aiding in a better understanding and prediction of micro-parameters for PFC^2D rock models.

This article presents, to the best of our knowledge, a novel simulation model for bacterial adhesion on rough surfaces, combining the SPH method with the LJ potential. A Staphylococcus aureus-like spherical bacterial cell is modeled as a rigid body in a fluid simulated with PySPH. The rough surface is characterized by shear force microscopy (ShFM), with bacteria–surface interactions described by the LJ potential. Surface–fluid interactions are modeled using WCSPH, and bacterial–fluid coupling is addressed through SPH, overcoming limitations of previous models. This model offers a powerful tool for studying bacterial adhesion and surface interactions in fluid environments.

The macro-scale material parameters of sea-ice and meso-scale model parameters in the discrete element method (DEM) for sea ice have a strongly nonlinear relationship because of the size effect in the DEM model. The parametric calibration is necessary to obtain high precision of sea-ice dynamics including the failure and fragmentation. This paper proposes a deep-learning-based parametric calibration for the parallel-bonding-based DEM model of sea ice, considering that the deep learning is good at establishing the nonlinear relationship of multiple input and output parameters. The training and prediction data are generated through DEM simulations, including uniaxial compression and three-point bending tests of sea ice in the DEM. The neural networks are employed to train the model by using the training data in which material parameters are the input data and model parameters are the output data. The prediction data illustrate that the prediction errors for different model parameters are less than 30%. The empirical formula that determines the bonding strength and internal friction from the compressive and flexural strength of sea ice is used for the validation as well. The comparison indicates that the neural networks have better precision than the empirical formula, and more parameters can be determined in the neural networks. Furthermore, the DEM simulation is used to validate whether the simulation results of strength can reach the input strength parameters. The validation shows that the error is lower than 6%. Hence, the proposed deep-learning-based parametric calibration yields highly accurate and effective results for DEM simulations.

The dynamics of a confined granular bed moving under gravity past stationary cylinders with different arrangements has been studied through discrete element modeling. The formation of a stagnation zone ahead and a void zone just below the cylinders is the most unique features. For a single cylinder, the exit width and geometry does not affect the stagnation zone, but it influences the void zone. For the inline arrangement, if cylinders are closely spaced, a single large stagnation zone on the top and a single void zone beneath are noticed. The tandem arrangement does not significantly affect the stagnation zone on the top cylinder. The stagnation zone between the cylinders and the void zone below the bottom cylinder minimally increases with the pitch. It is also interesting to note that the rate of drainage is affected only marginally by the arrangement and the number of cylinders.

Acoustic emission (AE) testing serves as a widely employed non-destructive technique for examining the behaviors of rocks under stress, with a particular focus on understanding the characteristics of the fracture process zone (FPZ). This paper investigates this phenomenon by conducting a numerical study using the two-dimensional discrete element method to simulate a three-point bending test with a center notch. An innovative displacement-softening contact law is incorporated to monitor the energy dissipation during bond damage and breakage. Additionally, the paper investigates the variation of AE energy levels corresponding to different loading stages, shedding light on intrinsic FPZ properties. The study further endeavors to categorize AE events based on their energy levels, showcasing the potential of the proposed model in capturing various FPZ characteristics. The simulation results affirm the model’s capability to represent diverse FPZ behaviors, providing valuable insights for the calibration of numerical models for quasi-brittle rocks. This study lays the groundwork for potential advancements in predicting the behavior of rock formations by offering essential numerical evidence supporting the utilization of the proposed model.