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Metallic foams or sponges are materials with a cell structure suitable for many industrial applications, such as reformers, heat catalytic converters, etc. The success of these materials is due to the combination of various characteristics such as mechanical strength, low density, high specific surface, good thermal exchange properties, low flow resistance and sound absorption. Different materials and manufacturing processes produce different type of structure and properties for various applications. In this work a genetic algorithm has been developed and applied to support the design of catalytic devices. In particular, two substrates were considered, namely the traditional honeycomb and an alternative open-cell foam type. CFD simulations of pressure losses and literature based correlations for the heat and mass transfer were used to support the genetic algorithm in finding the best compromise between flow resistance and pollutant abatement.

The transient heat transfer behavior of an automotive catalytic converter has been simulated with OpenFOAM in 1D. The model takes into consideration the gas-solid convective heat transfer, axial wall conduction and heat capacity effects in the solid phase, but also the chemical reactions of CO oxidation, based on simplified Arrhenius and Langmuir-Hinshelwood approaches. The associated parameters are the results of data in literature tuned by experiments. Simplified cases of constant flow rates and gas temperatures in the catalyst inflow have been chosen for a comprehensive analysis of the heat and mass transfer phenomena. The impact of inlet flow temperatures and inlet flow rates on the heat up characteristics as well as in the CO emissions have been quantified. A dimensional analysis is proposed and dimensionless temperature difference and space-time coordinates are introduced.