摘要
The minimum fluidization velocity of a fluid-solid particle fluidized bed is the primary focus of this paper.The computationally economic Eulerian Granular model has been used to analyze fluidization for both gas-solid particle and liquid-solid particle fluidized beds.The conventional approach of finding minimum fluidization velocity(umf)is either with a pressure drop across the particle bed or the change in bed height.However,these parameters are often unstable and cannot be used to generalize the degree of fluidization accurately.In this paper,the dominant factor of unstable pressure drop estimation in the 2D Two-Fluid Model(TFM)and a key non-dimensional Euler number has been investigated in deter-mining minimum fluidization velocity for different quasi-2D fluidized beds for different bed sizes,par-ticle sizes,and particle numbers.Averaging assumptions and limitations of these numerical models are discussed in detail for four different fluidized bed cases.A comparative study of the drag model shows little to no influence in unstable pressure drop estimation near fluidization velocity,and all drag models perform similarly.It is observed that particle-particle collision is not the dominant reason for unstable pressure drop near minimum fluidization.Instead,wall effects on the particle bed including frictional losses and wall-particle collision play a key role in unstable pressure drop calculation for the quasi-2D fluidized beds.Pressure drop characteristics alone do not suffice to obtain minimum fluidization ve-locity with 2D TFM using existing models.Thus,a different approach has been proposed to investigate minimum fluidization involving the Euler number,which has shown promising performance in deter-mining minimum fluidization velocity and characterizing fluidization with 2D TFM.Results show con-sistency in Euler number characteristics for all different fluidized bed cases considered in this paper.This can revitalize computationally economic 2D Eulerian simulations,increase the range of possible appli-cations,and provide guidance to the future development of computationally efficient and more accurate numerical models,and empirical correlations for minimum fluidization velocity.
