Grimm, J.P., 2004, An evaluation of alternative methodologies for the numerical simulation of solute transport, PhD thesis, Department of Civil and Structural Engineering, University of Sheffield.
The aim of this research was to establish whether it was feasible to use CFD software ( in this case Fluent) to predict the transport of solute through a pipe.
Two approaches were evaluated: the species transport model and the discrete phase (particle tracking) model. The species transport model predictions were found to be sensitive to spatial and temporal discretization scheme, and to the time step. However, the options that result in robust predictions for both the mean travel time and dispersion coefficient were identified. The particle tracking model was found to be computationally efficient and consistent predictions were attainable. However, the prediction of mean travel time was inaccurate, and consequently the model was eliminated from further investigation.
The second half of the thesis focuses on the validation of the species transport modelling approach, with a suitable [pipe flow] laboratory data set being identified. The most appropriate modelling options to use in order to represent the experimental flow conditions were identified through consideration of the system being modelled, a grid refinement study and two parametric studies. With the exception of turbulent viscosity, good correlations between measured and simulated flow fields were observed for all the turbulence model configurations.
The species transport model was utilised to predict solute transport at three flowrates. At each flowrate the measured dispersion was underpredicted. Reanalysis of the laboratory data, and consideration of certain model set-up options (including the turbulent Schmidt number and the upstream boundary conditions) tended to align the simulation results and the experimental data more closely.
With further development, the modelling approach developed within this thesis should enable dispersion coefficients to be identified for a wide range of urban drainage structures. Such predictions are required to enhance urban drainage quality models, and, ultimately, to improve sewer management and pollution control.