Numerical simulation for the initial state of avalanche in polydisperse particle systems

Numerical simulation is employed to investigate the initial state of avalanche in polydisperse particle systems.Nucleation and propagation processes are illustrated for pentadisperse and triadisperse particle systems,respectively.In these processes,particles involved in the avalanche grow slowly in the early stage and explosively in the later stage,which is clearly different from the continuous and steady growth trend in the monodisperse system.By examining the avalanche propagation,the number growth of particles involved in the avalanche and the slope of the number growth,the initial state can be divided into three stages:T1(nucleation stage),T2(propagation stage),T3(overall avalanche stage).We focus on the characteristics of the avalanche in the T2 stage,and find that propagation distances increase almost linearly in both axial and radial directions in polydisperse systems.We also consider the distribution characteristics of the average coordination number and average velocity for the moving particles.The results support that the polydisperse particle systems are more stable in the T2 stage.

Using the discrete element method to assess the mixing of polydisperse solid particles in a rotary drum

Despite the wide applications of powder and solid mixing in industry, knowledge on the mixing of polydisperse solid particles in rotary drum blenders is lacking. This study investigates the mixing of monodisperse, bidisperse, tridisperse, and polydisperse solid particles in a rotary drum using the dis- crete element method. To validate the model developed in this study, experimental and simulation results were compared. The validated model was then employed to investigate the effects of the drum rotational speed, particle size, and initial loading method on the mixing quality. The degree of mixing of polydis- perse particles was smaller than that for monodisperse particles owing to the segregation phenomenon. The mixing index increased from an initial value to a maximum and decreased slightly before reaching a plateau for bidisperse, tridisperse, and polydisperse particles as a direct result of the segregation of par- ticles of different sizes. Final mixing indices were higher for polydisperse particles than for tridisperse and bidisperse particles. Additionally, segregation was weakened by introducing additional particles of intermediate size. The best mixing of bidisperse and tridisperse particles was achieved for top-bottom smaller-to-larger initial loading, while that of polydisperse systems was achieved using top-bottom smaller-to-larger and top-bottom larger-to-smaller initial loading methods.

Machine Learning for heat radiation modeling of bi-and polydisperse particle systems including walls

We investigated the ability of four popular Machine Learning methods i.e.,Deep Neural Networks(DNNs),Random Forest-based regressors(RFRs),Extreme Gradient Boosting-based regressors(XGBs),and stacked ensembles of DNNs,to model the radiative heat transfer based on view factors in bi-and polydisperse particle beds including walls.Before training and analyzing the predictive capability of each method,an adjustment of markers used in monodisperse systems,as well as an evaluation of new markers was performed.On the basis of our dataset that considers a wide range of particle radii ratios,system sizes,particle volume fractions,as well as different particle-species volume fractions,we found that(i)the addition of particle size information allows the transition from monodisperse to bi-and polydisperse beds,and(ii)the addition of particle volume fraction information as the fourth marker leads to very accurate predictions.In terms of the overall performance,DNNs and RFRs should be preferred compared to the other two options.For particle-particle view factors,DNN and RFR are on par,while for particle-wall the RFR is superior.We demonstrate that DNNs and RFRs can be built to meet or even exceed the prediction quality standards achieved in a monodisperse system.

CFD-DEM simulation of fluorination reaction in fluidized beds with local grid and time refinement method

The gas-solid reaction process with wide particle size distribution is extensively used in the chemical engineering field,especially the particle reacts with the gas gradually,such as fluorination reactions in fluidized beds.When the computational fluid dynamics-discrete element method(CFD-DEM)is used for the coupling simulation of multiphase and polydisperse particle reaction system,the grid size directly affects the accuracy of flow field information and simulation of chemical reaction.Furthermore,particle calculation time step will directly affect the efficiency of coupling calculation.In this work,a local grid and time step refinement method is proposed to simulate multiphase and polydisperse particle fluid-ization reaction system.In this method,the refined DEM grids are automatically generated in the computational domain around the fine particles,and the detailed fluid phase information is obtained with the interpolation algorithm.In the two-phase coupling process,particles are divided into different groups based on physical properties,each group has its own independent time step.The multistage conical-cylindrical spouted bed is proposed for the fluorination reaction process;the operating gas ve-locity range of the polydisperse particle system is extended by the new design while the particle size distribution changes with the gas-solid reaction process.It is demonstrated that the local grid and time step refinement method can improve the accuracy and efficiency of the traditional CFD-DEM method in the reaction process simulation,which describes a polydisperse particle system with wide particle size distribution.Aimed at improving the simulation accuracy and efficiency,this paper will be helpful for simulating the particle reaction process in the gas-solid fluidized bed and beneficial for the development of the CFD-DEM method.

Simulation of fine polydisperse particle condensational growth under an octadecane-nitrogen atmosphere

The evolution of particle size distribution （PSD） of fine polydisperse particles at high number concen- trations （7105 cm-3） was simulated through a combined model employing direct quadrature method of moments （DQMOM） with heat and mass transfer equations. The PSD was assumed to retain log-normal distribution during the heterogeneous condensation process. The model was first verified by exact solu- tion and experimental data prior to investigating the influence of initial conditions on final PSD under an octadecane-nitrogen atmosphere. Low particle number concentrations and high vapor concentrations were beneficial to shift the PSD to larger particles having a narrower distribution. Additionally, vapor depletion has more influence on the final PSD than the heat release parameter for a number concentra- tion of 10^6 cm^-3. This study may assist the design process of a gas-solid separating cyclone, to eliminate dust from high-temperature volatiles by pyrolysis of solid fuels.