In the continuous casting of steel, the Submerged Entry Nozzle (SEN), in particular the SEN geometry, has a primary influence on the flow pattern: the SEN controls the speed, direction and other characteristics of the jet entering the mould. The SEN is however relatively inexpensive to change (in comparison with other continuous casting equipment). Thus; there is a feasible incentive to exactly understand and predict the flow of molten steel through the SEN and into the mould, in order to maximise the quality of the steel by altering the design of the SEN. By changing the SEN geometry and SEN design, the flow pattern in the mould will also change: it is thus possible to obtain an optimum SEN design if (or when) the desired flow patterns and/or certain predetermined temperature distributions are achieved. Expensive and risky plant trials were traditionally utilised to “perfect” continuous casting processes. As opposed to the plant trials, this dissertation is concerned with the Computational Fluid Dynamics (CFD) modelling of the SEN and mould, which, when used in conjunction with the Mathematical Optimiser LS-OPT, will enable the optimisation of the SEN design to achieve desired results. The CFD models are experimentally verified and validated using 40%-scaled (designed and built in-house) and full-scale water model tests. This dissertation proves that the CFD modelling of the SEN and mould can be quite useful for optimisation and parametric studies, especially when automated model generation (geometry, mesh and solution procedures) is utilised. The importance of obtaining reliable and physically correct CFD results is also emphasised; hence the need for CFD model verification using water modelling.