Modeling of aerated stirred tanks with shear-thinning power law liquids

Sungkorn, Radompon; Derksen, J. J.; Khinast, Johannes G.

doi:10.1016/j.ijheatfluidflow.2012.04.006

Cited by 40 publications

(25 citation statements)

References 37 publications

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“…Sungkorn et al. argue with a tolerable grid spacing to bubble diameter range of 0.5 < Δ S / d b < 10 if the total number of bubbles is small. An advanced approach to simulate bubbles larger than cell size comes with the immersed boundary methods, where coupling between the disperse and continuous phase is performed with a Lagrangian surface mesh .…”

Section: Two‐phase Systemsmentioning

confidence: 99%

“…A limitation of EL is the predicament with the spatial resolution of the continuous phase domain because the cell size must not exceed bubble size excessively [10]. Sungkorn et al [119] argue with a tolerable grid spacing to bubble diameter range of 0.5 < Δ S /d b < 10 if the total number of bubbles is small. An advanced approach to simulate bubbles larger than cell size comes with the immersed boundary methods, where coupling between the disperse and continuous phase is performed with a Lagrangian surface mesh [120].…”

Section: Marchisio and Fox [94]mentioning

confidence: 99%

See 1 more Smart Citation

Models for the Numerical Simulation of Bubble Columns: A Review

Mühlbauer

Hlawitschka

Bart

2019

Chemie Ingenieur Technik

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This work reviews the state‐of‐the‐art models for the simulation of bubble columns and focuses on methods coupled with computational fluid dynamics (CFD) where the potential and deficits of the models are evaluated. Particular attention is paid to different approaches in multiphase fluid dynamics including the population balance to determine bubble size distributions and the modeling of turbulence where the authors refer to numerous published examples. Additional models for reactive systems are presented as well as a special chapter regarding the extension of the models for the simulation of bubble columns with a present solid particle phase, i.e., slurry bubble columns.

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Section: Two‐phase Systemsmentioning

confidence: 99%

Section: Marchisio and Fox [94]mentioning

confidence: 99%

Models for the Numerical Simulation of Bubble Columns: A Review

Mühlbauer

Hlawitschka

Bart

2019

Chemie Ingenieur Technik

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“…Table 2 shows the average k L a values for different Impellers at the Gas Flow Rate of 40 and 70 liters per minute (lpm). The figures show the plot of k L a of each impeller versus the mixing time at the two flow rates [49,50]. Figure 11 and Figure 12 shows that non uniform k L a values were observed for all four Impellers with respect to Mixing Time (T m ) at the Gas Flow Rate of 40 lpm and 70 lpm.…”

Section: Correlation Between Mixing Time Tm (Sec) and Volumetric Masmentioning

confidence: 99%

Effect of Different Impellers and Baffles on Aerobic Stirred Tank Fermenter using Computational Fluid Dynamics

Sharma¹

2014

J Bioproces Biotech

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The present research was carried out with objective to study the effect of different types of Impellers and Baffles on mixing and to examine the correlation between mixing time and mass transfer of Aerobic Stirred Tank Fermenter. An Aerobic Stirred Tank fermenter was assembled with different Impellers and Baffles alternately. The four different Impellers used were Rushton Impeller, Marine Impeller, A320 Impeller and HE3 Impeller while walled and un walled baffles were used in combination with these Impellers. The Fermenter was assembled with Resistance Temperature Detector, pH Probe and Pressure Gauge. Tachometer was used to calculate the Mixing Time of Fermenter. Volumetric Mass Transfer Coefficient was experimentally determined and calculated by the respective formulae. MATLAB was used for Mathematical Modelling of CFD, while Autodesk Simulation CFD and ANSYS FLUENT were used to generate the simulations. Turbulence lengths and Trailing Vortices were used to understand flow patterns inside the fermenter created by Impellers and Baffles. The k L a values suggested that Rushton Impeller was the most efficient among all. Turbulent Kinetic Energy and Turbulent Dissipation Rate were to understand the mixing efficiency of every Impeller.

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“…Different rheological properties may result in dissimilar optimal operating conditions of mixing systems 8–10. Previous works on non‐Newtonian fluids stirred in vessels are mostly focused on the laminar flow, especially on power consumption 11–13, mixing time 10, 14, and computational simulation 15, 16. Only few reports are found in literature on flow characteristics and energy dissipation rates of stirred non‐Newtonian fluids.…”

Section: Introductionmentioning

confidence: 99%

Energy Dissipation Rates of Newtonian and Non‐Newtonian Fluids in a Stirred Vessel

2014

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Energy dissipation rates of water and glycerol as Newtonian fluids and carboxyl methyl carbonate solution as non-Newtonian fluid in a stirred vessel are investigated by 2D particle image velocimetry and compared. Mean velocity profiles reflect the Reynolds (Re) number similarity of two flow fields with different rheological properties, but the root mean square velocity profiles differ in rheology at the same Re-number. Energy dissipation rates are estimated by direct calculation of fluctuating velocity gradients. The varying energy dissipation rates of Newtonian and non-Newtonian fluids result from the difference in fluid rheology and apparent viscosity distribution which decides largely the flow pattern, circulation intensity, and rate of turbulence generation.

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Modeling of aerated stirred tanks with shear-thinning power law liquids

Cited by 40 publications

References 37 publications

Models for the Numerical Simulation of Bubble Columns: A Review

Models for the Numerical Simulation of Bubble Columns: A Review

Effect of Different Impellers and Baffles on Aerobic Stirred Tank Fermenter using Computational Fluid Dynamics

Energy Dissipation Rates of Newtonian and Non‐Newtonian Fluids in a Stirred Vessel

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