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Pollutant prediction in numerical simulations of laminar and turbulent flames using virtual chemistry

Abstract : CFD is nowadays used by research engineers as a numerical tool to design and optimize advanced combustion devices that are employed in energy conversion systems. In the development of advanced numerical CFD tools, one of the main research challenges is the identification of a reduced combustion chemistry model able to find a compromise between accurate reproduction of the flame structure and pollutants formation with an affordable CPU cost. In particular, pollutants formation prediction is a difficult task when complex flame environments are encountered: flame characterized by mixture stratification, heat loss and burnt gas recirculation. The present research work focuses on the modeling of CO and NOx formation in complex flame conditions using a reduced finite rate chemistry approach. CO and NOx reduced chemistry models are here developed using the recent virtual chemistry model; it consists in designing reduced mechanisms made of a network of an optimized number of virtual species interacting through virtual optimized reactions. In the first step, the virtual chemistry mechanisms are developed and validated in 1-D flames comparing the results with detailed chemistry. In a second step, they are employed to compute several 2-D laminar and 3-D turbulent flame configurations which include different combustion regimes: premixed, onpremixed, partially-premixed and non-adiabatic conditions. The obtained results are validated either with experimental data or with detailed chemistry computations.
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https://tel.archives-ouvertes.fr/tel-02893174
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Submitted on : Wednesday, July 8, 2020 - 9:45:14 AM
Last modification on : Wednesday, October 14, 2020 - 4:21:49 AM

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73858_MAIO_2020_diffusion.pdf
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  • HAL Id : tel-02893174, version 1

Citation

Giampaolo Maio. Pollutant prediction in numerical simulations of laminar and turbulent flames using virtual chemistry. Chemical and Process Engineering. Université Paris-Saclay; Politecnico di Milano, 2020. English. ⟨NNT : 2020UPASC003⟩. ⟨tel-02893174⟩

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