基于EMMS介尺度模型的双分散鼓泡流化床的模拟

双分散气固鼓泡流化床中颗粒通常具有不同粒径或密度,导致产生颗粒偏析等现象,影响传递和反应行为。颗粒分离和混合与气泡运动密不可分,其中相间曳力起关键作用。最近Ahmad等提出了一种基于气泡结构的双分散介尺度曳力模型,能成功预测双分散鼓泡流化床的床层膨胀系数。本研究耦合该曳力模型与连续介质方法,模拟了两种不同的双分散鼓泡流化床,通过分析不同流化状态下的气泡运动、颗粒浓度比的轴向分布等参数,进一步检验模型的适用性。研究表明,当双分散颗粒处于完全流化状态时,耦合双分散介尺度曳力模型可合理预测不同颗粒的分离现象;而其处于过渡流化状态时,新曳力模型和传统模型均无法获得合理结果,此时调节固固曳力可改进模拟结果。     

Effect of mixing state of binary particles on bubble behavior in 2D fluidized bed

In this study, a thin 2D fluidized bed was used to investigate the effect of mixing state of the binary particles on bubble behavior through the analysis of images captured by a high-speed digital camera. Experimental results show that the mixing index increases gradually with increasing gas velocity and the binary particles are in different mixing states though they are in the steady fluidization state. The maximal bubble number is near the interface of the bed when the binary particles are in the segregation state, whereas the maximal bubble number is at the bottom when the binary particles are in the well mixing state. The small bubbles are position at the bottom and are adjacent to the bed wall, while the large bubbles are mainly located in the central regions of the bed. The average bubble diameter shows the different variation trends with the different mixing states of the binary particles. The correlations estimating bubble diameter according to the mixing state of the binary particles are developed, and the computing value agrees well with the experimental data.     

Characterizing bubble behavior in a pseudo-2D fluidized bed of wet particles

By applying digital image analysis on the bubble characteristics in a two dimensional wet-particle fluidized-bed, we report two-stage evolution of bubble characteristics with increasing liquid content. In the first stage, bubble number and uniformity of bubble fraction increase, while bubble average diameter and aspect ratio decrease. In the second stage, these characteristics shift toward an opposite direction. This two-stage evolution of bubble characteristics are analogous to that of reducing particle size in dry-particle fluidization, and the fluidizing properties of particles shifts from Geldart Group B to Group A and then to Group C. Furthermore, liquid addition causes a continuous decrease of bubble fraction and bubble flow rate. This is different from dry-particle fluidization, in which reducing particle size causes an increase trend. An explanation for this difference is that liquid addition increases the equivalent size of agglomerates in wet-particle fluidization, which is opposite to the effect of reducing particle size.     

Simulation and experiment of segregating/mixing of rice husk–sand mixture in a bubbling fluidized bed

The fluidization behavior of rice husk–sand mixture in the gas bubbling fluidized bed is experimentally and theoretically studied. The relevancy of the pressure drop profile of rice husk–sand mixture to the definition of its minimum fluidization velocity is discussed, and the minimum fluidization velocity of rice husk–sand binary mixture is determined. The distributions of mass fraction of rice husk particles along the bed height are measured, and the profiles of the mean particle diameter of mixture are determined. A multi-fluid gas–solid flow model is presented where equations are derived from the kinetic theory of granular flow. Separate transport equations are constructed for each of the particle classes, allowing for the interaction between particle classes, as well as the momentum and energy are exchanged between the respective classes and the carrier gas. The distributions of the mass fraction of rice husk particles and the mean particle diameter of binary mixture are predicted. The numerical results are analyzed, and compared with experimental data.     

Measuring binary fluidization of nonspherical and spherical particles using machine learning aided image processing

The binary fluidization of Geldart D type nonspherical wood particles and spherical low density polyethylene (LDPE) particles was investigated in a laboratory-scale bed. The experiment was performed for varying static bed height, wood particles count, as well as superficial gas velocity. The LDPE velocity field were quantified using particle image velocimetry (PIV). The wood particles orientation and velocity are measured using particle tracking velocimetry (PTV). A machine learning pixel-wise classification model was trained and applied to acquire wood and LDPE particle masks for PIV and PTV processing, respectively. The results show significant differences in the fluidization behavior between LDPE only case and binary fluidization case. The effects of wood particles on the slugging frequency, mean, and variation of bed height, and characteristics of the particle velocities/orientations were quantified and compared. This comprehensive experimental dataset serves as a benchmark for validating numerical models.     

Transition to turbulent fluidization in a binary solids fluidized bed

This paper presents a study on the transition velocity from bubbling to turbulent fluidization in a binary solids fluidized bed. Experiments were carried out with two kinds of binary solids mixtures with FCC as fine particles and silica sands as coarse particles. The onset velocity to turbulent fluidization, U_c, determined by the measurement of pressure fluctuations, was found to increase with increasing the fraction of coarse/heavy solids. By introducing an equivalent particle diameter and an equivalent particle density, the results obtained in this study can properly be described by a general correlation of U_c proposed by Cai and co-workers (1989) for mono-density particles with relatively narrow size distribution.     

Effect of cohesive powders on pressure fluctuation characteristics of a binary gas-solid fluidized bed

The effect of cohesive particles on the pressure fluctuations was experimentally investigated in a binary gas-solid fluidized bed. The pressure fluctuation signals were measured by differential pressure sensors under conditions of various weight percentages of cohesive particles. The cohesive particles increased the fixed bed pressure drop per unit height and decreased the minimum fluidization velocity. The Wen & Yu equation well predicts the minimum fluidization velocity of the binary system. The addition of cohesive particles slightly decreased the bubble size in bubbling flow regime when the cohesive particles and the coarse particles mixed well, while the bubble size greatly decreased when the cohesive particles agglomerated on the bed surface. The time series of pressure fluctuations was analyzed by using the methods of time domain, frequency domain and wavelet transformation. The normalized standard deviation of pressure fluctuations decreased with increasing weight percentages of cohesive particles. A wide bandwidth frequency of 0 to 1Hz got narrower with a single peak around 0.6Hz with an increase in proportion of the cohesive particles. The meso-energy and micro-energy of pressure fluctuations were decreasing with increasing cohesive particles proportions, which indicated that adding cohesive particles could reduce the energy dissipation of bubble and particle fluctuations.     

Fluidization characteristics of printed circuit board plastic particles with different sizes

The experiments were carried out in a fluidized bed of 56 mm in diameter and 1 600 mm in height to determine the fluidization characteristics of four sizes of printed circuit board plastic (PCBP) particles. It indicates that the fluidization characteristics of PCBP particles depend on the average size and particle type. 123 µm PCBP particles (1#), belonging to Geldart A group with strong viscous force, whose fluidization behaviours was similar to those of Geldart C, was difficult to fluidize. Whereas, 275 µm (2#), 354 µm (3#), and 423 µm (4#) PCBP particles, belonging to Geldart B, were fluidized smoothly. The bed collapsing process is composed of three stages: the bubble escaping stage, the sedimentation stage, and the solid consolidation stage. The collapsing process of 1# PCBP particle lasts 6 s or long. 2#, 3#, and 4# PCBP particles, Geldart group B particles, collapse process consists of the bubble escaping stage and the solid consolidation stage. The minimum fluidization velocities from modified Ergun Equation were agreement with experimental data for 2#, 3#, and 4# PCBP particles.     

Predictions on bubble and solids movement in laboratory polyethylene fluid beds as visualized by X-ray computer assisted tomography (CAT) scanning

Solid and gas distributions are tomographically quantified as a function of position with high resolution in a series of laboratory fluid beds containing air and polyethylene particles. The resolution used is 0.4 mm by 0.4 mm by 3 mm. The laboratory models are Plexiglas columns of 10 cm in diameter and the settling bed L/D ratios vary between one and three. Large particles (up to 1.5 mm in diameter) of high density polyethylene and linear low density polyethylene are used. The superficial gas velocities vary from the minimum fluidization velocity to 50 cm/s. In this paper, the analysis of fluid bed CAT scanner images is extended to show bubble, emulsion and dense phase distribution. The analysis is also used to determine the bubble diameter and to predict the flow direction of solid particles as well as the velocity of descending solids. The voidage frequency distributions of a bed at different gas flow rates are compared to each other and the voidage threshold values corresponding to gas, emulsion and dense phases are determined. These threshold values are used to prepare ternary images that clearly show the parts of the bed cross-section corresponding to bubble, emulsion and dense phases.     

气固流化床气泡发生频率的研究

在单孔二维气固流化床中(292mm×16mm)用高灵敏度电容探针研究气泡发生频率.以频谱分析仪分析气泡频率分布曲线.考察了一系列参数对气泡频率功率分布密度曲线的影响,其中包括颗粒直径(0.105—0.590mm),颗粒重度(590—2990kg/m~3),颗粒最小流化速度(0.0072—0.481m/s),床层初始高度(205—565mm),探针离孔口垂直距离,孔口气体流率(0.5—35×10~(-4)m~3/s)以及床层辅助流化气速(0—3倍最小流化速度)等.对于重度低的小颗粒流化床,单孔气泡发生频率符合Davidson和Harrison早先推导的模型.随着颗粒直径和重度的增大,实验数据与上述模型呈有规律的偏差.本文提出气体从形成中气泡的顶半球以最小流化速度值向乳浊相泄漏的模型,推导了气泡发生频率的基本方程.以本研究的泄漏模型,用数值计算方法在计算机上计算的气泡发生频率与实验数据相吻合.     

A second-order moment method of dense gas-solid flow for bubbling fluidization

A gas-solid two-fluid model with the second-order moment method is presented to close the set of equations applied to fluidization. With the kinetic theory of granular flow, transport equations for the velocity moments are derived for the particle phase. Closure equations for the third-order moments of velocity and for the fluid-particle velocity correlation are presented. The former is based on a modified model with the contribution of the increase of the binary collision probability, and the latter uses an algebraic model proposed by Koch and Sangani [1999. Particle pressure and marginal stability limits for a homogeneous monodisperse gas-fluidized bed: kinetic theory and numerical simulations. Journal of Fluid Mechanics 400, 229-263]. Boundary conditions for the set of equations describing flow of particles proposed by Strumendo and Canu [2002. Method of moments for the dilute granular flow of inelastic spheres. Physical Review E 66, 041304/1-041304/20] are modified with the consideration of the momentum exchange by collisions between the wall and particles. Flow behavior of gas and particles is performed by means of gas-solid two-fluid model with the second-order moment model of particles in the bubbling fluidized bed. The distributions of velocity and moments of particles are predicted in the bubbling fluidized bed. Predictions are compared with experimental data measured by Muller et al. [2008. Granular temperature: comparison of magnetic resonance measurements with discrete element model simulations. Powder Technology 184, 241-253] and Yuu et al. [2000. Numerical simulation of air and particle motions in bubbling fluidized bed of small particles. Powder Technology 110, 158-168]. in the bubbling fluidized beds. The simulated second-order moment in the vertical direction is 1.1-2.5 [Muller, C.R., Holland, D.J., Sedeman, A.J., Scott, S.A., Dennis, J.S., Gladden, L.F., 2008. Granular temperature: comparison of magnetic resonance measurements with discrete element model simulations. Powder Technology 184, 241-253] and 1.1-4.0 [Yuu, S., Umekage, T., Johno, Y., 2000. Numerical simulation of air and particle motions in bubbling fluidized bed of small particles. Powder Technology 110, 158-168] times larger than that in the lateral direction because of higher velocity fluctuations for particles in the bubble fluidized bed. The bubblelike Reynolds normal stresses per unit bulk density used by Gidaspow et al. [2004. Hydrodynamics of fluidization using kinetic theory: an emerging paradigm 2002 Flour-Daniel lecture. Powder Technology 148, 123-141.] are computed from the simulated hydrodynamic velocities. The predictions are in agreement with experimental second-order moments measured by Muller et al. [2008. Granular temperature: comparison of magnetic resonance measurements with discrete element model simulations. Powder Technology 184, 241-253] and fluctuating velocity of particles measured by Yuu et al. [2000. Numerical simulation of air and particle motions in bubbling fluidized bed of small particles. Powder Technology 110, 158-168].     

Dynamics of segregation and fluidization of binary mixtures in a cylindrical fluidized bed

Dynamics of segregation and fluidization of unary particles and binary mixtures in a cylindrical fluidized bed is investigated using temporally– and spatially–resolved measurements of solids volume fraction (α_s) performed using Electrical Capacitance Tomography (ECT). Through the comparison with high-speed imaging, we have shown that ECT can be used to measure the segregation behavior in cylindrical fluidized beds quantitatively. ECT measurements have been used further to quantify the effects of mixture composition, particle–diameter ratio, and superficial gas velocity on the bed segregation behavior. Dynamics of fluidization behavior is characterized using the time–evolution of local α_s fluctuations, corresponding frequency distribution, and bubble size distribution. Further, a relation between the measured variance of α_s fluctuations at different radial locations and corresponding flow structures under different fluidization conditions is established. The present work helps to understand dynamics of segregation and fluidization of binary mixtures and to provide a database for validation of Eulerian multifluid CFD models.     

振动流化床双组分颗粒的混合与分离

研究了振动流化床中颗粒的轴向浓度分布及最小流化速度,根据不同操作条件下最小流化速度的实验值,定义了最小流化速度的计算式;同时考察双组分颗粒混合与分离情况,根据实验结果,修正了Ｎｉｅｎｏｗ定义的双组分颗粒的转变气速,并与实验值相符。     

铁基载氧体与干化市政污泥二元混合物流化特性

化学链燃烧技术处置污泥可有效抑制有害气体排放，但干化污泥与铁基载氧体的物理性质相差较大，在流化床内会产生混合分离等问题。在内径(φ)为100 mm的有机玻璃冷态流化床装置上，进行了干化污泥与载氧体二元颗粒流化实验，讨论了颗粒粒径大小、干化污泥与载氧体质量比和操作风速对二元颗粒流化特性的影响。结果表明，污泥与平均粒径为0.66 mm的载氧体能实现混合流化，最小流化速度 和最小混合操作风速Um相等；污泥与平均粒径为1.46 mm的载氧体流化时，随操作风速增大，逐渐由分离流化状态过渡到混合流化状态，Um? ；污泥与平均粒径为2.43 mm的载氧体流化时，始终保持分离流化状态。基于提出的表征混合/分离流化状态的无量纲数Gd，当00.8时处于分离流化。     

Minimum Fluidization Velocities of Binary Solid Mixtures: Empirical Correlation and Genetic Algorithm-Artificial Neural Network Modeling

Experimental investigation of the fluidization behavior in single and binary solid-liquid fluidized beds of nonspherical particles as solid phase and water as liquid phase was performed in a Perspex column. Different particle sizes were used to prepare single and binary mixtures with different weight ratios for fluidization. Minimum fluidization velocity increased with increasing average particle size and decreasing sphericity for the binary mixture. An empirical correlation was developed to predict the minimum fluidization velocity. Genetic algorithm-artificial neural network (GA-ANN) modeling was applied to predict the minimum fluidization velocity for single and binary solid-liquid fluidized beds. The application of GA-ANN analysis leads to designing binary solid-liquid fluidization systems without experimentation.     

Numerical modeling of particle fluidization behavior in a rotating fluidized bed

In this study, numerical modeling of particle fluidization behaviors in a rotating fluidized bed (RFB) was conducted. The proposed numerical model was based on a DEM (Discrete Element Method)-CFD (Computational Fluid Dynamics) coupling model. Fluid motion was calculated two-dimensionally by solving the local averaged basic equations. Particle motion was calculated two-dimensionally by the DEM. Calculation of fluid motion by the CFD and particle motion by the DEM were simultaneously conducted in the present model. Geldart group B particles (diameter and particle density were 0.5 mm and 918 kg/m³, respectively) were used for both calculation and experiment. First of all, visualization of particle fluidization behaviors in a RFB was conducted. The calculated particle fluidization behaviors by our proposed numerical model, such as the formation, growth and eruption of bubble and particle circulation, showed good agreement with the actual fluidization behaviors, which were observed by a high-speed video camera. The estimated results of the minimum fluidization velocity (U_mf) and the bed pressure drop at fluidization condition (ΔP_f) by our proposed model and other available analytical models in literatures were also compared with the experimental results. It was found that our proposed model based on the DEM-CFD coupling model could predict the U_mf and ΔP_f with a high accuracy because our model precisely considered the local downward gravitational effect, while the other analytical models overpredicted the ΔP_f due to ignoring the gravitational effect.     

气泡驱动液固流化床内二元颗粒的流化行为

采用密度稍重于和稍轻于流体的两种颗粒,研究了气泡驱动液固流化床内二元颗粒的流化行为。通过测量压差和拍摄视频的方法确定了初始流化气速U_in,g、固含率和气含率。重颗粒的U_in,g通过流化床底部的压差变化确定,轻颗粒的U_in,g则通过观察得到。研究表明,在气泡驱动的液固流化床内,重颗粒和轻颗粒的初始流化气速都随藏量的增加而增加,但重颗粒增加幅度更大。完全流化后,重颗粒固含率在轴向上分布不均匀,而轻颗粒则分布较为均匀。在二元颗粒体系内,上部轻颗粒的流化受到下部重颗粒的影响而底部重颗粒的流化不受轻颗粒影响,导致重颗粒U_in,g和固含率分布主要受自身藏量影响,而轻颗粒U_in,g随二元颗粒的总藏量变化。     

快速流态化气固两相间的动量交换

本文根据一维定常态流动模型,对快速流化床内气固两相间的相互作用及其机理进行了研究.结果表明,在快速流态化条件下,颗粒总是趋于聚集,以减小气固两相间的相互作用力,从而使曳力系数c_D小于单颗粒标准曳力系数c_(DS)(c_D/c_(DS)<1.0).c_D/c_(DS)不仅与(?)有关,而且受气固流动状况以及颗粒物性、床层直径等因素的影响.通过对大量数据的分析,得到预测曳力系数的经验关联式(平均相对偏差小于5%)c_D/c_(DS)=1.685(?)~(0.253)(Re_r/Re_t)~(-1.213)(d_p/D)~(0.105)     

Axial segregation in bubbling gas‐fluidized beds with Gaussian and lognormal distributions of Geldart Group B particles

Bubbling, gas‐fluidized bed experiments involving Geldart Group B particles with continuous‐size distributions have been carried out. Sand of various widths of Gaussian or lognormal distributions were completely fluidized, then axial concentration profiles were obtained from frozen‐bed sectioning. Similar to previous works on binary systems, results show that mean particle diameter decreases with increasing bed height, and that wider Gaussian distributions show increased segregation extents. Surprisingly, however, lognormal distributions exhibit a nonmonotonic segregation trend with respect to distribution widths. In addition, the shape of the local‐size distribution is largely preserved with respect to that of the overall distribution. These findings on the nature of local‐size distribution provide experimental confirmation of previous results for granular and gas‐solid simulations. Lastly, an interesting observation is that although monodisperse Geldart Group D particles cannot be completely fluidized, their presence in lognormal distributions investigated still results in complete fluidization of all particles. © 2010 American Institute of Chemical Engineers AIChE J, 2010     

Simulation study of the effect of wall roughness on the dynamics of granular flows in rotating semicylindrical chutes

A discrete element model (DEM) is used to investigate the behavior of spherical particles flowing down a semicylindrical rotating chute. The DEM simulations are validated by comparing with particle tracking velocimetry results of spherical glass particles flowing through a smooth semicylindrical chute at different rotation rates of the chute. The DEM model predictions agree well with experimental results of surface velocity and particle bed height evolution. The validated DEM model is used to investigate the influence of chute roughness on the flow behavior of monodisperse granular particles in rotating chutes. To emulate different base roughnesses, a rough base is constructed out of a square close packing of fixed spherical particles with a diameter equal to, smaller, or larger than the flowing particles. Finally, the DEM model is used to study segregation in a binary density mixture for different degrees of roughness of the chute. © 2015 American Institute of Chemical Engineers AIChE J, 61: 2117–2135, 2015