We have investigated the effect of cohesion and drag models on the bed hydrodynamics of Geldart A particles based on the two-fluid （TF） model. For a high gas velocity U0 = 0.03 m/s, we found a transition from the ho...We have investigated the effect of cohesion and drag models on the bed hydrodynamics of Geldart A particles based on the two-fluid （TF） model. For a high gas velocity U0 = 0.03 m/s, we found a transition from the homogeneous fluidization to bubbling fluidization with an increase of the coefficient C1, which is used to account for the contribution of cohesion to the excess compressibility. Thus cohesion can play a role in the bed expansion of Geldart A particles. Apart from cohesion, we have also investigated the influence of the drag models. When using the Wen and Yu drag correlation with an exponent n = 4.65, we find an under-prediction of the bed expansion at low gas velocities （U0 = 0.009 m/s）. When using a larger exponent （n = 9.6）, as reported in experimental studies of gas-fluidization, a much better agreement with the experimental bed expansion is obtained. These findings suggest that at low gas velocity, a scale-down of the commonly used drag model is required. On the other hand, a scale-up of the commonly used drag model is necessary at high gas velocity （U0 = 0.2 and 0.06 m/s）. We therefore conclude that scaling the drag force represent only an ad hoc way of repairing the deficiencies of the TF model, and that a far more detailed study is required into the origin of the failure of the TF model for simulating fluidized beds of fine powders.展开更多
颗粒物质的混合是化学工业生产的重要单元操作,由于颗粒物质运动行为的复杂性,工业混合器中的颗粒运动规律及物理机制至今仍未被全面认识。作为一种精细的数值方法,离散单元法(discrete element method,DEM)在单颗粒尺度上描述颗粒物质...颗粒物质的混合是化学工业生产的重要单元操作,由于颗粒物质运动行为的复杂性,工业混合器中的颗粒运动规律及物理机制至今仍未被全面认识。作为一种精细的数值方法,离散单元法(discrete element method,DEM)在单颗粒尺度上描述颗粒物质的受力与运动行为,因此在研究混合机理方面具有独特优势。随着DEM模型与计算技术的快速发展,DEM已被广泛应用于各种混合过程的研究。通过DEM可以全面考察不同的颗粒性质、混合器类型以及操作条件等因素对混合机理的影响,从而对于指导粉体工业的生产操作及设备优化改进具有重要意义。本文重点阐述了DEM在无黏颗粒、黏结性颗粒、非球形颗粒混合过程模拟以及大规模计算等方面的最新进展,并对未来发展进行了展望。展开更多
An experimental study on the gravity driven discharge of cohesive particles from a silo with two outlets was performed.The discharge behaviors under the conditions that a single outlet was open and two outlets were op...An experimental study on the gravity driven discharge of cohesive particles from a silo with two outlets was performed.The discharge behaviors under the conditions that a single outlet was open and two outlets were open were investigated by varying the moisture content of the particles and the filling height of the particles in the silo.The results show that the discharge rate of the cohesive particles increases gradually at the beginning,then almost keeps constant,and finally drops obviously.The discharge rate in case of two openings is around 1.1–1.6 times that in case of a single opening.Larger filling height leads to lower discharge rate in case of a single opening but results in higher discharge rate in case of two openings.Furthermore,the avalanche dynamics in case of a single opening was examined,and the mixing behavior of the cohesive particles was evaluated.It is observed that the discharge flow is promoted by the avalanche phenomenon in the silo,generating a general trend that the normalized mass of discharge increases with the filling height at higher moisture contents.In case of a single opening,the transition from mass flow to funnel flow favors the particle mixing,resulting in an increasing mixing index as the moisture content increases.In general,a better performance of mixing can be achieved in case of a single opening compared with in case of two openings.This study provides vital information for fundamental understanding of the gravity driven discharge of cohesive particles from the silo with multiple outlets.展开更多
This research paper presents a comprehensive discrete element method(DEM)examination of the mixing behaviors exhibited by cohesive particles within a twin-paddle blender.A comparative analysis between the simulation a...This research paper presents a comprehensive discrete element method(DEM)examination of the mixing behaviors exhibited by cohesive particles within a twin-paddle blender.A comparative analysis between the simulation and experimental results revealed a relative error of 3.47%,demonstrating a strong agreement between the results from the experimental tests and the DEM simulation.The main focus centers on systematically exploring how operational parameters,such as impeller rotational speed,blender's fill level,and particle mass ratio,influence the process.The investigation also illustrates the significant influence of the mixing time on the mixing quality.To gain a deeper understanding of the DEM simulation findings,an analytical tool called multivariate polynomial regression in machine learning is employed.This method uncovers significant connections between the DEM results and the operational parameters,providing a more comprehensive insight into their interrelationships.The multivariate polynomial regression model exhibited robust predictive performance,with a mean absolute percentage error of less than 3%for both the training and validation sets,indicating a slight deviation from actual values.The model's precision was confirmed by low mean absolute error values of 0.0144(80%of the dataset in the training set)and 0.0183(20%of the dataset in the validation set).The study offers valuable insights into granular mixing behaviors,with implications for enhancing the efficiency and predictability of the mixing processes in various industrial applications.展开更多
The multi-scale characteristics of clusters in a fast fluidized bed and of agglomerates in a fluidized bed of cohesive particles are discussed on the basis of large amounts of experiments. The cluster size and concent...The multi-scale characteristics of clusters in a fast fluidized bed and of agglomerates in a fluidized bed of cohesive particles are discussed on the basis of large amounts of experiments. The cluster size and concentration are dominated by the local voidage of the bed. A cluster consists of many sub-clusters with different sizes and discrete par-ticles, and the sub-cluster size probability density distribution appears as a negative exponential function. The agglom-erates in a fluidized bed of cohesive particles also possess the multi-scale nature. The large agglomerates form a fixed bed at the bottom, the medium agglomerates are fluidized in the middle, and the small agglomerates and discrete parti-cles become the dilute-phase region in the upper part of the bed. The agglomerate size is mainly affected by cohesive forces and gas velocity. The present models for predicting the size of clusters and agglomerates can not tackle the in-trinsic mechanism of the multi-scale aggregation, and a challenging problem for establishing mechanistic model is put forward.展开更多
A particle-particle(p-p)drag model is extended to cohesive particle flow by introducing solid surface energy to characterize cohesive collision energy loss.The effects of the proportion of cohesive particles on the mi...A particle-particle(p-p)drag model is extended to cohesive particle flow by introducing solid surface energy to characterize cohesive collision energy loss.The effects of the proportion of cohesive particles on the mixing of binary particles were numerically investigated with the use of a Eulerian multiphase flow model incorporating the p-p drag model.The bed expansion,mixing,and segregation of Geldart-A and C particles were simulated with varying superficial velocities and Geldart-C particle proportions,from which we found that the p-p drag model can reasonably predict bed expansion of binary particles.Two segregation types of jetsam-mixture-flotsam and mixture-flotsam processes were observed during the fluidization processes for the Geldart-A and C binary particle system.The mixing processes of the binary particle system can be divided into three scales:macro-scale mixing,meso-scale mixing,and micro-scale mixing.At a constant superficial velocity the optimal mixing was observed for a certain cohesive particle proportion.展开更多
A soft-sphere discrete cohesive powder model was used to simulate the transverse mixing of particles in a rotary drum. Using this model, the effect of cohesion strength and baffle length was investigated. Mixing time ...A soft-sphere discrete cohesive powder model was used to simulate the transverse mixing of particles in a rotary drum. Using this model, the effect of cohesion strength and baffle length was investigated. Mixing time （tR） and mixing entropy were used to characterize the mixing behavior. The results showed that increasing particle cohesiveness increases tR. Baffles enhanced transverse mixing, especially for high- cohesive particles. Moreover, the baffle length played a significant role on mixing. An optimized length of 0.50 （L/R） enhances transverse mixing for high-cohesive particles, Further increases in baffle length only decreases the mixing rate by impeding the surface flow layer. In contrast to high-cohesive particles, low-cohesive particles needed much shorter baffles.A soft-sphere discrete cohesive powder model was used to simulate the transverse mixing of particles in a rotary drum. Using this model, the effect of cohesion strength and baffle length was investigated. Mixing time （tR） and mixing entropy were used to characterize the mixing behavior. The results showed that increasing particle cohesiveness increases tR. Baffles enhanced transverse mixing, especially for high- cohesive particles. Moreover, the baffle length played a significant role on mixing. An optimized length of 0.50 （L/R） enhances transverse mixing for high-cohesive particles. Further increases in baffle length only decreases the mixing rate by impeding the surface flow layer. In contrast to high-cohesive particles, low-cohesive particles needed much shorter baffles.展开更多
文摘We have investigated the effect of cohesion and drag models on the bed hydrodynamics of Geldart A particles based on the two-fluid （TF） model. For a high gas velocity U0 = 0.03 m/s, we found a transition from the homogeneous fluidization to bubbling fluidization with an increase of the coefficient C1, which is used to account for the contribution of cohesion to the excess compressibility. Thus cohesion can play a role in the bed expansion of Geldart A particles. Apart from cohesion, we have also investigated the influence of the drag models. When using the Wen and Yu drag correlation with an exponent n = 4.65, we find an under-prediction of the bed expansion at low gas velocities （U0 = 0.009 m/s）. When using a larger exponent （n = 9.6）, as reported in experimental studies of gas-fluidization, a much better agreement with the experimental bed expansion is obtained. These findings suggest that at low gas velocity, a scale-down of the commonly used drag model is required. On the other hand, a scale-up of the commonly used drag model is necessary at high gas velocity （U0 = 0.2 and 0.06 m/s）. We therefore conclude that scaling the drag force represent only an ad hoc way of repairing the deficiencies of the TF model, and that a far more detailed study is required into the origin of the failure of the TF model for simulating fluidized beds of fine powders.
文摘颗粒物质的混合是化学工业生产的重要单元操作,由于颗粒物质运动行为的复杂性,工业混合器中的颗粒运动规律及物理机制至今仍未被全面认识。作为一种精细的数值方法,离散单元法(discrete element method,DEM)在单颗粒尺度上描述颗粒物质的受力与运动行为,因此在研究混合机理方面具有独特优势。随着DEM模型与计算技术的快速发展,DEM已被广泛应用于各种混合过程的研究。通过DEM可以全面考察不同的颗粒性质、混合器类型以及操作条件等因素对混合机理的影响,从而对于指导粉体工业的生产操作及设备优化改进具有重要意义。本文重点阐述了DEM在无黏颗粒、黏结性颗粒、非球形颗粒混合过程模拟以及大规模计算等方面的最新进展,并对未来发展进行了展望。
基金support from the National Natural Science Foundation of China(grant No.51976130)Science and Technology Commission of Shanghai Municipality,China(grant No.13DZ2260900)。
文摘An experimental study on the gravity driven discharge of cohesive particles from a silo with two outlets was performed.The discharge behaviors under the conditions that a single outlet was open and two outlets were open were investigated by varying the moisture content of the particles and the filling height of the particles in the silo.The results show that the discharge rate of the cohesive particles increases gradually at the beginning,then almost keeps constant,and finally drops obviously.The discharge rate in case of two openings is around 1.1–1.6 times that in case of a single opening.Larger filling height leads to lower discharge rate in case of a single opening but results in higher discharge rate in case of two openings.Furthermore,the avalanche dynamics in case of a single opening was examined,and the mixing behavior of the cohesive particles was evaluated.It is observed that the discharge flow is promoted by the avalanche phenomenon in the silo,generating a general trend that the normalized mass of discharge increases with the filling height at higher moisture contents.In case of a single opening,the transition from mass flow to funnel flow favors the particle mixing,resulting in an increasing mixing index as the moisture content increases.In general,a better performance of mixing can be achieved in case of a single opening compared with in case of two openings.This study provides vital information for fundamental understanding of the gravity driven discharge of cohesive particles from the silo with multiple outlets.
基金the Natural Sciences and Engineering Research Council of Canada(grant No.RGPIN-2019-04644)is gratefully acknowledged.
文摘This research paper presents a comprehensive discrete element method(DEM)examination of the mixing behaviors exhibited by cohesive particles within a twin-paddle blender.A comparative analysis between the simulation and experimental results revealed a relative error of 3.47%,demonstrating a strong agreement between the results from the experimental tests and the DEM simulation.The main focus centers on systematically exploring how operational parameters,such as impeller rotational speed,blender's fill level,and particle mass ratio,influence the process.The investigation also illustrates the significant influence of the mixing time on the mixing quality.To gain a deeper understanding of the DEM simulation findings,an analytical tool called multivariate polynomial regression in machine learning is employed.This method uncovers significant connections between the DEM results and the operational parameters,providing a more comprehensive insight into their interrelationships.The multivariate polynomial regression model exhibited robust predictive performance,with a mean absolute percentage error of less than 3%for both the training and validation sets,indicating a slight deviation from actual values.The model's precision was confirmed by low mean absolute error values of 0.0144(80%of the dataset in the training set)and 0.0183(20%of the dataset in the validation set).The study offers valuable insights into granular mixing behaviors,with implications for enhancing the efficiency and predictability of the mixing processes in various industrial applications.
文摘The multi-scale characteristics of clusters in a fast fluidized bed and of agglomerates in a fluidized bed of cohesive particles are discussed on the basis of large amounts of experiments. The cluster size and concentration are dominated by the local voidage of the bed. A cluster consists of many sub-clusters with different sizes and discrete par-ticles, and the sub-cluster size probability density distribution appears as a negative exponential function. The agglom-erates in a fluidized bed of cohesive particles also possess the multi-scale nature. The large agglomerates form a fixed bed at the bottom, the medium agglomerates are fluidized in the middle, and the small agglomerates and discrete parti-cles become the dilute-phase region in the upper part of the bed. The agglomerate size is mainly affected by cohesive forces and gas velocity. The present models for predicting the size of clusters and agglomerates can not tackle the in-trinsic mechanism of the multi-scale aggregation, and a challenging problem for establishing mechanistic model is put forward.
基金This work is currently supported by the National Natural Science Foundation of China through contract No.51606153,91634109 and 2167060316Natural Science Basic Research Plan in Shaanxi Province of China(No.2016JQ5101 and 2017JQ2018)Scien-tific Research Program Funded by Shaanxi Provincial Education Department(No.14JK1729).
文摘A particle-particle(p-p)drag model is extended to cohesive particle flow by introducing solid surface energy to characterize cohesive collision energy loss.The effects of the proportion of cohesive particles on the mixing of binary particles were numerically investigated with the use of a Eulerian multiphase flow model incorporating the p-p drag model.The bed expansion,mixing,and segregation of Geldart-A and C particles were simulated with varying superficial velocities and Geldart-C particle proportions,from which we found that the p-p drag model can reasonably predict bed expansion of binary particles.Two segregation types of jetsam-mixture-flotsam and mixture-flotsam processes were observed during the fluidization processes for the Geldart-A and C binary particle system.The mixing processes of the binary particle system can be divided into three scales:macro-scale mixing,meso-scale mixing,and micro-scale mixing.At a constant superficial velocity the optimal mixing was observed for a certain cohesive particle proportion.
文摘A soft-sphere discrete cohesive powder model was used to simulate the transverse mixing of particles in a rotary drum. Using this model, the effect of cohesion strength and baffle length was investigated. Mixing time （tR） and mixing entropy were used to characterize the mixing behavior. The results showed that increasing particle cohesiveness increases tR. Baffles enhanced transverse mixing, especially for high- cohesive particles. Moreover, the baffle length played a significant role on mixing. An optimized length of 0.50 （L/R） enhances transverse mixing for high-cohesive particles, Further increases in baffle length only decreases the mixing rate by impeding the surface flow layer. In contrast to high-cohesive particles, low-cohesive particles needed much shorter baffles.A soft-sphere discrete cohesive powder model was used to simulate the transverse mixing of particles in a rotary drum. Using this model, the effect of cohesion strength and baffle length was investigated. Mixing time （tR） and mixing entropy were used to characterize the mixing behavior. The results showed that increasing particle cohesiveness increases tR. Baffles enhanced transverse mixing, especially for high- cohesive particles. Moreover, the baffle length played a significant role on mixing. An optimized length of 0.50 （L/R） enhances transverse mixing for high-cohesive particles. Further increases in baffle length only decreases the mixing rate by impeding the surface flow layer. In contrast to high-cohesive particles, low-cohesive particles needed much shorter baffles.
