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    Exploring Soot Pathways: High-Fidelity Large Eddy Simulation Investigation of Soot Formation and Oxidation in Rich–Quench–Lean Combustion Systems Under Real Conditions

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    Date
    2026
    Author
    Koob, Philipp
    Ferraro, Federica
    Magens, Eggert
    Heinze, Johannes
    Soworka, Thomas
    Behrendt, Thomas
    Eggels, Ruud L. G. M.
    Hasse, Christian
    Nicolai, Hendrik
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    Abstract
    Abstract. Developing low-emission aero-engines presents a critical step in meeting near-term climate goals. A particular challenge is accurate soot predictions with computational fluid dynamics (CFD), where the integration of advanced thermochemical interaction models is required. The extreme conditions typical of aero-engines—characterized by high temperatures, elevated pressures, and strong transients—demand reliable and accurate modeling to capture the complex pathways of soot formation and oxidation. This study focuses on the soot formation, evolution, and oxidation in a single-sector rich–quench–lean (RQL) aero-engine model combustor, employing high-fidelity numerical simulations validated against experimental data obtained as part of this investigation. Based on the validated simulation results, the high-fidelity large eddy simulations (LESs) coupled with the split-based extended quadrature method of moments (S-EQMOM) soot model are used to examine soot dynamics within the combustor. The LES predictions accurately reproduce experimental trends across a range of operating conditions. By categorizing the combustor flow field into distinct zones—flame, mixing, recirculation, and a transition between flame and mixing—the study provides a detailed quantification of soot behavior. Soot formation and growth are predominantly confined to the flame zone, while oxidation occurs throughout the chamber, reducing the soot volume fraction. High mixing rates corresponding to very low local residence times prevent complete soot oxidation, increasing the probability of soot breakthrough into the lean region. These findings provide critical insights for developing reduced-order models that efficiently predict soot formation. Such models are essential for reducing computational costs and advancing the design of future low-emission aero-engines.
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    http://yetl.yabesh.ir/yetl1/handle/yetl/4314910
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