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VERIFICATION OF SMOM AND QMOM POPULATION BALANCE MODELING IN CFD CODE USING ANALYTICAL SOLUTIONS FOR BATCH PARTICULATE PROCESSES
作者姓名:Bin  Wan  Terry  A.Ring
作者单位:[1]Department of Chemical Engineering, University of Utah, Salt Lake City, UT 84112 [2]Fluent Inc., 10 Cavendish Court, Lebanon, NH 03766
摘    要:For many processes of industrial significance, due to the strong coupling between particle interactions and fluid dynamics, the population balance must be solved as part of a computational fluid dynamics (CFD) simulation. In this work, a CFD based population balance model is tested using a batch crystallization reactor. In this CFD model, the population balance is solved by the standard method of moments (SMOM) and the quadrature method of moments (QMOM). The results of these simulations are compared to analytical solutions for the population balance in a batch tank where 1) nucleation, 2) growth, 3) aggregation, and 4) breakage are taking place separately. The results of these comparisons show that the first 6 moments of the population balance are accurately predicted for nucleation, growth, aggregation and breakage at all times.

关 键 词:种群平衡  计算流体动力学  颗粒加工  建模
收稿时间:2006-03-08
修稿时间:2006-03-082006-08-20

VERIFICATION OF SMOM AND QMOM POPULATION BALANCE MODELING IN CFD CODE USING ANALYTICAL SOLUTIONS FOR BATCH PARTICULATE PROCESSES
Bin Wan Terry A.Ring.VERIFICATION OF SMOM AND QMOM POPULATION BALANCE MODELING IN CFD CODE USING ANALYTICAL SOLUTIONS FOR BATCH PARTICULATE PROCESSES[J].China Particuology,2006,4(5):243-249.
Authors:Bin Wan  Terry ARing
Abstract:For many processes of industrial significance, due to the strong coupling between particle interactions and fluid dynamics, the population balance must be solved as part of a computational fluid dynamics (CFD) simulation. In this work, a CFD based population balance model is tested using a batch crystallization reactor. In this CFD model, the population balance is solved by the standard method of moments (SMOM) and the quadrature method of moments (QMOM). The results of these simulations are compared to analytical solutions for the population balance in a batch tank where 1) nucleation, 2) growth, 3) aggregation, and 4) breakage are taking place separately. The results of these com- parisons show that the first 6 moments of the population balance are accurately predicted for nucleation, growth, ag- gregation and breakage at all times.
Keywords:population balance  computational fluid dynamics  particle process  modeling
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