This paper contains the results of a design optimization study performed on the steady-casting operation of the Columbus Stainless single-strand stainless steel caster tundish. Residence Time Distribution (RTD) data such as minimum residence time (or plug flow volume fraction) and dead volume are used as objective functions in the mathematical optimization process. Water is used in the first two case studies as modeling fluid to allow for comparison with water model results. Liquid steel is used in the last case study to investigate the effect of temperature and buoyancy on the resulting flow patterns and the optimum design. Two separate tundish configurations are considered. The first has one dam and one weir, while the second comprises a baffle with angled holes and an impact pad. Significant improvements of up to 34 % in minimum residence time are obtained for the second configuration.

SUMMARYThis paper describes the use of Computational Fluid Dynamics (CFD) and mathematical optimization techniques to minimize pollution due to industrial sources like stacks. The optimum placement of a new pollutant source (e.g. a new power plant with its stacks) depends on many parameters. These include stack height, stack distance from surrounding populated areas, barriers, local meteorological conditions, etc. As an experimental approach is both time-consuming and costly, use is made of numerical techniques. Using CFD without optimization on a trial-and-error basis, however, does not guarantee optimal solutions. A better approach, that until recently has been too expensive, is to combine CFD with mathematical optimization techniques, thereby incorporating the influence of the variables automatically. The current study investigates a simplified two-dimensional case of the minimisation of pollutant stack distance to a street canyon with or without barrier for a given maximum ground-level concentration of pollutants in a street canyon. Two to five design variables are considered. The CFD simulation uses the STAR-CD code with RNG k-turbulence model. Making use of initial field restarts drastically reduces CFD solution time. The optimization is carried out by means of Snyman's DYNAMIC-Q method, which is specifically designed to handle constrained problems where the objective or constraint functions are expensive to evaluate. The paper illustrates how the parameters considered influence the stack placement and how these techniques can be used by the environmental engineer to perform impact studies of new pollutant sources.

This paper describes the use of CFD and mathematical optimization to minimise heat sink mass given a maximum allowable heat sink temperature, a constant cooling fan power and heat source. Heat sink designers have to consider a number of conflicting parameters. Heat transfer is influenced by, amongst others, heat sink properties (such as surface area), airflow bypass and the location of heat sources, whilst size and/or mass of the heat sink needs to be minimized. This multiparameter problem lends itself naturally to optimization techniques. In this study a commercial CFD code, STAR-CD, is linked to the DYNAMIC-Q method of Snyman. Five design variables are considered for three heat source cases. Optimal designs are obtained within six design iterations. The paper illustrates how mathematical optimization can be used to design compact heat sinks for different types of electronic enclosures.

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