Skip to main navigation Skip to search Skip to main content

Prediction of Particle Dynamics in Bubbling Fluidized Beds with Inertial Number-Based Drag and Solid Stress Models

  • Xinyao Guo
  • , Guodong Liu*
  • , Xiaolong Yin
  • , Junnan Zhao
  • , Chunlei Wang
  • , Xinyi Zuo
  • *Corresponding author for this work
  • School of Energy Science and Engineering, Harbin Institute of Technology
  • Heilongjiang Key Laboratory of New Energy Storage Materials and Processes
  • Eastern Institute of Technology, Ningbo
  • Longmen Laboratory

Research output: Contribution to journalArticlepeer-review

Abstract

Gas-solid fluidized bed reactors are widely used in industry. Since two-phase flows in fluidized beds often coexist with multiple flow states, the interaction mechanism between the phases is complex, and accurate simulation has always been a challenge. In this paper, the inertia number is used as a measure of the transition of the drag model and the solid stress model from the inertial region (low particle concentration) to the quasi-static region (high particle concentration) to achieve the dynamic transition of different interphase interaction mechanisms. Through the simulation of the fluidization process of Geldart D particles, it is found that the drag and solid stress models based on the inertia number can more accurately describe the particle flow behavior in the 3D fluidized bed than the two-fluid model combined with traditional kinetic theory of granular flow and achieve a prediction accuracy close to CFD-DEM by presenting a nearly realistic bubble formation process. This provides a more computationally resource-efficient and effective tool for accurately modeling gas-solid two-phase flows at the reactor scale.

Original languageEnglish
Pages (from-to)22150-22165
Number of pages16
JournalIndustrial and Engineering Chemistry Research
Volume63
Issue number50
DOIs
StatePublished - 18 Dec 2024
Externally publishedYes

Fingerprint

Dive into the research topics of 'Prediction of Particle Dynamics in Bubbling Fluidized Beds with Inertial Number-Based Drag and Solid Stress Models'. Together they form a unique fingerprint.

Cite this