ABSTRACT
The efficiency of mixing processes in impeller agitated tanks depends highly on the
hydrodynamics. Computational fluids dynamics (CFD) provides a method of predicting the
complex tlow structures in stirred tanks. As with any approximate numerical method, CFD
methods are subject to errors due to assumptions in the underlying mathematical models, as well
as errors due to the numerical solution procedures. The aim of this thesis was to present a CFD
method that accurately models the hydrodynamic properties of the 110w in stirred tanks.
The general purpose eFD software Fluent 6. f was used to develop the model of a laboratoryscale
stirred tank. Numerical experiments were conducted to investigate the effects of the
computational grid density, discretization schemes, turbulence models and impeller modeling
method on the accuracy of the simulated tlow. The results were validated with Laser Doppler
Velocimetry data from the literature.
It was found that the density of the numerical grid had more influence on the predicted turbulent
quantities than on the mean velocity components. For the mean velocity components, reasonable
agreement with the experimental data was observed even on relatively coarse grids. The choice of
discretization scheme was found to have significant effect on the predicted turbulent kinetic
energy and Power numbers. Very good agreement with experimental data was achieved for both
these flow variables when higher order discretization schemes were used on fine grids. This is an
important finding as it suggests that the generally reported underestimation of turbulence in
literature is caused by numerical errors in the CFD simulation as opposed to inadequacies in the
turbulence models as suggested by most researchers.
Steady-state and time-dependent impeller models were compared and found to have little etlect
on the mean velocity and turbulent kinetic energy. Ilowever impeller Power numbers calculated
from the time-dependent simulations were found to be in better agreement with the experimental
values. A comparison was also made between the standard k-s and RNG models. It was found
that the standard k-s turbulence model gave better predictions of the flow than the RNG- k-s
turbulence model.
Siwale, N (2021). Modeling of Flow in Impeller Stirred Tanks using Computational Fluids Dynanlics. Afribary. Retrieved from https://afribary.com/works/modeling-of-flow-in-impeller-stirred-tanks-using-computational-fluids-dynanlics
Siwale, Namwawa "Modeling of Flow in Impeller Stirred Tanks using Computational Fluids Dynanlics" Afribary. Afribary, 15 May. 2021, https://afribary.com/works/modeling-of-flow-in-impeller-stirred-tanks-using-computational-fluids-dynanlics. Accessed 04 Oct. 2024.
Siwale, Namwawa . "Modeling of Flow in Impeller Stirred Tanks using Computational Fluids Dynanlics". Afribary, Afribary, 15 May. 2021. Web. 04 Oct. 2024. < https://afribary.com/works/modeling-of-flow-in-impeller-stirred-tanks-using-computational-fluids-dynanlics >.
Siwale, Namwawa . "Modeling of Flow in Impeller Stirred Tanks using Computational Fluids Dynanlics" Afribary (2021). Accessed October 04, 2024. https://afribary.com/works/modeling-of-flow-in-impeller-stirred-tanks-using-computational-fluids-dynanlics