| Turbo air classifier is one of the most popular key equipment of powder classification technology in the field of powder preparation because of its simple structure,easy operation and maintenance,and adjustable product particle size.With the rapid development of new industries and new technologies,The demand for ultra-fine powders which have the characteristic with small particle size and narrow particle size distribution is increasing,Therefore,it’s critical to improve the classification performance of turbo air classifier.In order to make the internal flow field of turbo air classifier stable and uniform and improve its classification performance,This thesis takes the horizontal turbo air classifier as the research object to study.Meanwhile taking some methods Combining numerical simulation technology,theoretical analysis and material classification test method to analyze its the characteristics of the internal flow field,improve its the key component,and optimize operating parameters in the selected range.The main contents include:(1)Aiming at the characteristics of the internal flow field and particle movement in the turbo air classifier,Fluent is used to simulate the continuous phase and the discrete phase,and the relevant numerical simulation calculation methods are determined.Reasonably construct the geometric model of the horizontal turbo classifier and divide it into four areas(classification room area,middle cylinder area,diversion area and lower cone area);use hexahedral grids to model the horizontal turbo classifier Divide,and complete the quality inspection and independence verification of the classifier mesh model;the calculation model for the continuous phase is selected as the RNG k-ε two-equation turbulence model,and the particle motion trajectory simulation adopts the discrete model(DPM)in the non-coupling Calculation.(2)The characteristics of the classifier’s internal flow field for three different working conditions(the wind speed of the air inlet is 18 m/s,and the rotating speed of the rotating cage are respectively 3 000 r/min,6 000 r/min,and 12 000 r/min)are studied by combining the numerical simulation and material classification experiment.Numerical simulation results show that the horizontal vortex flow direction and double-layer flow direction in the classification chamber of the classifier are related to the inlet position and working conditions,and they will affect the distribution of the "double vortex" structure of the flow field in the classifier;the formation and distribution of the internal vortex is constrained by the horizontal vortex,and the small swing of the vortex core is conducive to the dispersion and classification of the particles;the eccentricity of the vortex core center of each section at different axial heights is different,which has a certain degree of volatility;The distribution of classifier’s internal flow field under simulated three different working conditions is unreasonable.The experimental results show that there are quite differences about the Stability and uniformity of the classifier’s internal flow field under the three working conditions,which will cause the "double vortex" distribution changes,and then affect the classification accuracy.With the increase of the rotating speed of the rotating cage,the average cutting particle size decreased by 28.85μm,the average yield of coarse powder increased by 33%,and the classification accuracy decreased by 10.5% on average.The experimental results are consistent with the numerical simulation results,but the selected three working conditions are not good and need further adjustment.(3)Through numerical simulation and blade shape design theory methods,the flow field characteristics in the flow channel of the rotating cage blades and the air flow velocity at the inlet of the rotating cage under selected working conditions are analyzed,and the relevant design of the "T"-shaped blade structure is completed.According to the relative velocity distribution formula of the flow path of the rotating cage blades,the triangle inlet velocity of the rotating cage and the numerical simulation results,the installation angle of the "T" blade is determined;Field characteristic distribution and particle movement trajectory determined the optimal "T"-shaped blade structure,and completed the secondary optimization.The simulation results show that the flow field of the blade channel of the T75 structure is more stable and uniform,which can reduce the impact of air flow and particles on the classification blades,and has better classification performance;the simulated classification particles are reduced by 24.4% compared with the T0 structure.,The classification accuracy is improved by 31.6% compared with T0 structure.The flow cross-section area of the blade channel inlet of the T75-C structure which is optimized is relatively increased,and the conveying capacity of some fine particles is enhanced;the simulated classification particle size is reduced by 8.7% compared with the T75 structure,and the classification accuracy is increased by 10.2%.(4)Using single variable method,response surface optimization method and material classification experiment,the interaction of operating parameters(rotating speed,inlet wind speed,feed speed)of various single factors and their interactions on the classification performance of the turbo air classifier are discussed.And optimized the operating parameters within the selected range.The following results are concluded: The level of the classification performance of the classifier is related to the operating parameters,and the single factors of the operating parameters have a non-linear relationship with the classification particle size and classification accuracy.The order of the magnitude of their influence on the classification particle size and classification accuracy is: rotating cage speed > inlet wind speed > feeding speed.There are varying degrees of interaction between the rotating speed of the rotating cage,the inlet wind speed and the feeding speed.The interaction between the rotating speed of the rotating cage and the inlet wind speed has a significant influence on the grading particle size.The interaction between the material speed is relatively strong,and the impact on the classification accuracy is more significant.The optimal operating parameters optimized by the response surface method are: the rotating speed of the rotating cage is7463.1r/min,the inlet wind speed is 15.4m/s,and the feeding speed is 16.7kg/h;the predicted value of the classification particle size and classification accuracy of the classifier under this condition They are respectively 15.3μm and 52.4%.The actual values of optimal operating parameters obtained by the response surface optimization method are selected and verified by material tests.The results show that the average error rates of the grading particle size and grading accuracy are respectively 3.7% and 2.8%. |
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