CFD simulation and optimization of the flow field in horizontal turbo air classifiers

Sun, Zhigang; Sun, Guogang; Liu, Jianxin; Yang, Xiaonan

doi:10.1016/j.apt.2017.03.016

Cited by 34 publications

(13 citation statements)

References 28 publications

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“…Therefore, it is more suitable for high-speed rotating fluid motion. Sun [22] and Guizani [23] demonstrated that RSM can more completely predict the turbulent structure and pressure drop inside the turbo air classifier, and calculated results were closer to the experimental results. Therefore, in the present paper, the RSM model was used to simulate the turbulent flow in the new rotor-type dynamic classifier.…”

Section: Model Code Diameter Of the Top Surface R1/mmmentioning

confidence: 57%

A New Rotor-Type Dynamic Classifier: Structural Optimization and Industrial Applications

et al. 2021

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Due to the inadequate pre-dispersion and high dust concentration in the grading zone of the turbo air classifier, a new rotor-type dynamic classifier with air and material entering from the bottom was designed. The effect of the rotor cage structure and diversion cone size on the flow field and classification performance of the laboratory-scale classifier was comparatively analyzed by numerical simulation using ANSYS-Fluent. The grinding process performance with an industrial classifier was also tested on-site. The results revealed that an inverted cone-type rotor cage is more suitable for the under-feed classifier. When the rotor cage’s top-surface diameter to bottom-surface diameter ratio was too large or too small, the radial velocity and tangential velocity at the outer surface of the rotor cage greatly fluctuated. Furthermore, the diameter of the diversion cone also affected the axial velocity and radial velocity of the flow field. Models T-C(1-0.8) and T-D(1-0.7) were determined as the best rotor cage structures. Under stable operating conditions, the classification efficiency of the industrial classifier was 87% and the sharpness of separation was 0.58, which meet the industrial requirements for classification efficiency and energy consumption. This present study provides theoretical guidance and engineering application value for air classifiers.

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Section: Model Code Diameter Of the Top Surface R1/mmmentioning

confidence: 57%

A New Rotor-Type Dynamic Classifier: Structural Optimization and Industrial Applications

et al. 2021

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“…Within the process industry, there are several operations that involve the use of certain physical-mechanical characteristics of solid particles [13][14][15][16]. One of these is the aerodynamic behavior of solid particles, i.e., their aerodynamic separation [17][18][19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Extraction of Mathematical Correlations Applied in the Aerodynamic Separation of Solid Particles

et al. 2022

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This article describes the methodology used to identify the mathematical equation that describes the correlations between the input and output parameters of an experiment. As a technological process, aerodynamic separation was chosen to represent the behavior of a solid particle within an ascending vertical airflow. The experimental data were used to identify two parameters, namely the average linear velocity and the angular velocity. The Table Curve 3D program was used to develop a mathematical equation describing the dependence between the input parameters (the shape and size of the solid particle, as well as the velocity of the airflow) and the monitored parameters. A pyramid-type analysis (following a filtering system, a general equation was determined from a large number of equations that characterize an experimental set mathematically) was designed in order to determine a single mathematical equation that describes the correlation between the input variables and those obtained as accurately as possible. The determination of the mathematical equation started with the number of equations generated by the Table Curve 3D program; then, the equations with a correlation coefficient greater than 0.85 were chosen; and finally, the common equations were identified. Respecting the working methodology, one equation was identified, which has for the average linear velocity, a correlation coefficient r2 of between 0.88-0.99 and 0.86–0.99 for the angular velocity.

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“…Within the process industry there are several operations which involve the use of certain physical-mechanical characteristics of solid particles [1][2][3][4][5][6]. One of these is the aerodynamic behavior of the solid particle, respectively its aerodynamic separation [7][8][9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Identify the Mathematical Models Generated by the Table Curve 3 D Program

Moșneguțu¹,

Panainte-Lehăduș²,

Nedeff³

et al. 2022

Preprint

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This article describes the methodology used to identify the mathematical model that describes the correlations between the input parameters of an experiment and the parameters being followed. As a technological process, the aerodynamic separation was chosen, respectively, the behavior of a solid particle within an ascending vertical airflow. The experimental data obtained were used to identify two parameters, the average linear velocity, and the angular velocity, and through the Table Curve 3D program was developed a mathematical model which describes the dependence between the input parameters (the shape and size of the solid particle and the velocity of the airflow) and the monitored parameters. In order to determine a single mathematical equation that describes as accurately as possible the correlation between the input variables and those obtained, a pyramid-type analysis was designed. The determination of the mathematical equation started from the number of equations generated by the Table Curve 3D program, then the equations with a correlation coefficient greater than 0.85 were chosen, and finally, the common equations were identified. Respecting the working methodology was identified one equation which has for the average linear velocity a correlation coefficient r2 between 0.88-0.99 and 0.86-0.99 for the angular velocity.

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CFD simulation and optimization of the flow field in horizontal turbo air classifiers

Cited by 34 publications

References 28 publications

A New Rotor-Type Dynamic Classifier: Structural Optimization and Industrial Applications

A New Rotor-Type Dynamic Classifier: Structural Optimization and Industrial Applications

Extraction of Mathematical Correlations Applied in the Aerodynamic Separation of Solid Particles

Identify the Mathematical Models Generated by the Table Curve 3 D Program

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