| Summary: | Includes bibliographical references (leaves 63-68). === 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 flow 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 CFD software Fluent 6. 1 was used to develop the model of a laboratory scale stirred tank. Numerical experiments were conducted to investigate the effects of the computational grid density, discretization schemes, turbulence models and impeller modelling method on the accuracy of the simulated flow. 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 effect on the mean velocity and turbulent kinetic energy. However 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.
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