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Computational fluid dynamics (CFD) t...

Zalc, Jeffrey Michael,

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Computational fluid dynamics (CFD) tools for investigating flow and mixing in industrial systems: The Koch-Glitsch SMX(RTM) static mixer and a three Rushton turbine stirred tank /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computational fluid dynamics (CFD) tools for investigating flow and mixing in industrial systems: The Koch-Glitsch SMX(RTM) static mixer and a three Rushton turbine stirred tank // Jeffrey Michael Zalc.
Author:	Zalc, Jeffrey Michael,
Description:	1 electronic resource (213 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 62-09, Section: B.
Contained By:	Dissertations Abstracts International62-09B.
Subject:	Chemical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9991956
ISBN:	9780599996519

Computational fluid dynamics (CFD) tools for investigating flow and mixing in industrial systems: The Koch-Glitsch SMX(RTM) static mixer and a three Rushton turbine stirred tank /
Zalc, Jeffrey Michael,

Computational fluid dynamics (CFD) tools for investigating flow and mixing in industrial systems: The Koch-Glitsch SMX(RTM) static mixer and a three Rushton turbine stirred tank /Jeffrey Michael Zalc. - 1 electronic resource (213 pages)

Source: Dissertations Abstracts International, Volume: 62-09, Section: B.

A suite of numerical tools, encompassing both commercial software and algorithms developed over the course of this dissertation research, is implemented for a detailed analysis of laminar flow and mixing in two industrial systems. A four element SMX static mixer geometry and a batch stirred tank equipped with three Rushton turbines are considered. Computational fluid dynamics (CFD) is used to compute the fully three-dimensional flow fields over tine unstructured tetrahedral meshes; at least ten flow conditions are considered for each system. Particle tracking routines are then employed to characterize the velocity components, pressure fields, and local mixing rates. Lagrangian mixing analysis is based on the dispersion of tracer particles. The stretching of fluid elements is used to quantify mixing performance. In the SMX static mixer, CFD results are validated through comparison of computed pressure drops with experimental results reported in the literature. Flow behavior is characterized by contour plots and probability density functions of velocity components and the magnitude of the deformation tensor. It is found that the flow in the static mixer is essentially independent of flow rate up through a Reynolds number of 1, beyond which inertial effects become significant and substantial differences in the nature of the flow are observed. Computed mixing patterns exhibit self-similarity and asymptotic directionality, which are fingerprints of chaotic behavior. Statistical characterization of the partially mixed structures reveals an exponential decay of the coefficient of variance with increasing axial distance. In the three Rushton turbine stirred tank, planar velocity vectors extracted from the CFD results are compared with experimental results obtained from particle imaging velocimetry (PIV). Planar laser-induced fluorescence (PLIF) and Poincare sections are both used to expose persistent poor-mixing regions, whose sizes and shapes depend strongly on the agitation rate. The computational results are used to illustrate strong flow compartmentalization and significant spatial heterogeneity with respect to local deformation rates within the vessel. Partially chaotic mixing behavior is identified by exponential stretching rates and short-term mixing dynamics is illustrated by tracer dispersion simulations.

English

ISBN: 9780599996519Subjects--Topical Terms:

560457
Chemical engineering.
Subjects--Index Terms:

Computational fluid dynamics

Computational fluid dynamics (CFD) tools for investigating flow and mixing in industrial systems: The Koch-Glitsch SMX(RTM) static mixer and a three Rushton turbine stirred tank /
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520 $a A suite of numerical tools, encompassing both commercial software and algorithms developed over the course of this dissertation research, is implemented for a detailed analysis of laminar flow and mixing in two industrial systems. A four element SMX static mixer geometry and a batch stirred tank equipped with three Rushton turbines are considered. Computational fluid dynamics (CFD) is used to compute the fully three-dimensional flow fields over tine unstructured tetrahedral meshes; at least ten flow conditions are considered for each system. Particle tracking routines are then employed to characterize the velocity components, pressure fields, and local mixing rates. Lagrangian mixing analysis is based on the dispersion of tracer particles. The stretching of fluid elements is used to quantify mixing performance. In the SMX static mixer, CFD results are validated through comparison of computed pressure drops with experimental results reported in the literature. Flow behavior is characterized by contour plots and probability density functions of velocity components and the magnitude of the deformation tensor. It is found that the flow in the static mixer is essentially independent of flow rate up through a Reynolds number of 1, beyond which inertial effects become significant and substantial differences in the nature of the flow are observed. Computed mixing patterns exhibit self-similarity and asymptotic directionality, which are fingerprints of chaotic behavior. Statistical characterization of the partially mixed structures reveals an exponential decay of the coefficient of variance with increasing axial distance. In the three Rushton turbine stirred tank, planar velocity vectors extracted from the CFD results are compared with experimental results obtained from particle imaging velocimetry (PIV). Planar laser-induced fluorescence (PLIF) and Poincare sections are both used to expose persistent poor-mixing regions, whose sizes and shapes depend strongly on the agitation rate. The computational results are used to illustrate strong flow compartmentalization and significant spatial heterogeneity with respect to local deformation rates within the vessel. Partially chaotic mixing behavior is identified by exponential stretching rates and short-term mixing dynamics is illustrated by tracer dispersion simulations.
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653 $a Computational fluid dynamics
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