Abstract
Industrial oxalic precipitation processed in an un-baffled magnetically stirred tank, the Vortex Reactor, has been studied with uranium simulating plutonium. Modelling precipitation requires a mixing model for the continuous liquid phase and the solution of population balance for the dispersed solid phase. Being chemical reaction influenced by the degree of mixing at molecular scale, that commercial CFD code does not resolve, a sub-grid scale model has been introduced: the finite mode probability density functions, and coupled with a model for the liquid energy spectrum. Evolution of the dispersed phase has been resolved by the quadrature method of moments, first used here with experimental nucleation and growth kinetics, and an aggregation kernel based on local shear rate. The promising abilities of this local approach, without any fitting constant, are strengthened by the similarity between experimental results and simulations. (author)
Citation Formats
Sommer de Gelicourt, Y.
Turbulent precipitation of uranium oxalate in a vortex reactor - experimental study and modelling; Precipitation turbulente d'oxalate d'uranium en reacteur vortex - etude experimentale et modelisation.
France: N. p.,
2004.
Web.
Sommer de Gelicourt, Y.
Turbulent precipitation of uranium oxalate in a vortex reactor - experimental study and modelling; Precipitation turbulente d'oxalate d'uranium en reacteur vortex - etude experimentale et modelisation.
France.
Sommer de Gelicourt, Y.
2004.
"Turbulent precipitation of uranium oxalate in a vortex reactor - experimental study and modelling; Precipitation turbulente d'oxalate d'uranium en reacteur vortex - etude experimentale et modelisation."
France.
@misc{etde_20998843,
title = {Turbulent precipitation of uranium oxalate in a vortex reactor - experimental study and modelling; Precipitation turbulente d'oxalate d'uranium en reacteur vortex - etude experimentale et modelisation}
author = {Sommer de Gelicourt, Y}
abstractNote = {Industrial oxalic precipitation processed in an un-baffled magnetically stirred tank, the Vortex Reactor, has been studied with uranium simulating plutonium. Modelling precipitation requires a mixing model for the continuous liquid phase and the solution of population balance for the dispersed solid phase. Being chemical reaction influenced by the degree of mixing at molecular scale, that commercial CFD code does not resolve, a sub-grid scale model has been introduced: the finite mode probability density functions, and coupled with a model for the liquid energy spectrum. Evolution of the dispersed phase has been resolved by the quadrature method of moments, first used here with experimental nucleation and growth kinetics, and an aggregation kernel based on local shear rate. The promising abilities of this local approach, without any fitting constant, are strengthened by the similarity between experimental results and simulations. (author)}
place = {France}
year = {2004}
month = {Mar}
}
title = {Turbulent precipitation of uranium oxalate in a vortex reactor - experimental study and modelling; Precipitation turbulente d'oxalate d'uranium en reacteur vortex - etude experimentale et modelisation}
author = {Sommer de Gelicourt, Y}
abstractNote = {Industrial oxalic precipitation processed in an un-baffled magnetically stirred tank, the Vortex Reactor, has been studied with uranium simulating plutonium. Modelling precipitation requires a mixing model for the continuous liquid phase and the solution of population balance for the dispersed solid phase. Being chemical reaction influenced by the degree of mixing at molecular scale, that commercial CFD code does not resolve, a sub-grid scale model has been introduced: the finite mode probability density functions, and coupled with a model for the liquid energy spectrum. Evolution of the dispersed phase has been resolved by the quadrature method of moments, first used here with experimental nucleation and growth kinetics, and an aggregation kernel based on local shear rate. The promising abilities of this local approach, without any fitting constant, are strengthened by the similarity between experimental results and simulations. (author)}
place = {France}
year = {2004}
month = {Mar}
}