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    A Dynamic Thickening Strategy for High-Fidelity Computational Fluid Dynamics Analyses of Multi-Regime Combustion

    Source: Journal of Engineering for Gas Turbines and Power:;2024:;volume( 146 ):;issue: 012::page 121010-1
    Author:
    Ballotti, A.
    ,
    Castellani, S.
    ,
    Andreini, A.
    DOI: 10.1115/1.4066212
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: In this study, a dynamic thickening strategy for dynamic thickened flame model for large eddy simulations (DTFLES) application to multi-regime combustion is proposed. The main idea lies in using the numerical solution of an ordinary differential equation (ODE) as a thickening factor. The equation relates the time derivative of the local thickening factor to its production and destruction rates, which are proportional to the gap between the instantaneous value and optimal target values. The smoothness of the thickening factor in time is ensured by the ODE solution, while in space it is achieved through a mathematical function defined in a continuous flame index space. The equation is numerically integrated with a semi-implicit scheme by making use of the backward Euler formula. The strategy has been implemented in a commercial computational fluid dynamics (CFD) solver and it has been tested by performing Large Eddy Simulations of the hydrogen/air flame produced by the HYLON injector, which has been individuated as an interesting test case for the proposed dynamic strategy. Turbulence-chemistry interactions are recovered by means of a well-assessed subgrid efficiency model. Numerical results are compared with the experimental ones obtained at Institut de Mécanique des Fluides de Toulouse (IMFT).
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      A Dynamic Thickening Strategy for High-Fidelity Computational Fluid Dynamics Analyses of Multi-Regime Combustion

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4303007
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    contributor authorBallotti, A.
    contributor authorCastellani, S.
    contributor authorAndreini, A.
    date accessioned2024-12-24T18:56:08Z
    date available2024-12-24T18:56:08Z
    date copyright9/5/2024 12:00:00 AM
    date issued2024
    identifier issn0742-4795
    identifier othergtp_146_12_121010.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4303007
    description abstractIn this study, a dynamic thickening strategy for dynamic thickened flame model for large eddy simulations (DTFLES) application to multi-regime combustion is proposed. The main idea lies in using the numerical solution of an ordinary differential equation (ODE) as a thickening factor. The equation relates the time derivative of the local thickening factor to its production and destruction rates, which are proportional to the gap between the instantaneous value and optimal target values. The smoothness of the thickening factor in time is ensured by the ODE solution, while in space it is achieved through a mathematical function defined in a continuous flame index space. The equation is numerically integrated with a semi-implicit scheme by making use of the backward Euler formula. The strategy has been implemented in a commercial computational fluid dynamics (CFD) solver and it has been tested by performing Large Eddy Simulations of the hydrogen/air flame produced by the HYLON injector, which has been individuated as an interesting test case for the proposed dynamic strategy. Turbulence-chemistry interactions are recovered by means of a well-assessed subgrid efficiency model. Numerical results are compared with the experimental ones obtained at Institut de Mécanique des Fluides de Toulouse (IMFT).
    publisherThe American Society of Mechanical Engineers (ASME)
    titleA Dynamic Thickening Strategy for High-Fidelity Computational Fluid Dynamics Analyses of Multi-Regime Combustion
    typeJournal Paper
    journal volume146
    journal issue12
    journal titleJournal of Engineering for Gas Turbines and Power
    identifier doi10.1115/1.4066212
    journal fristpage121010-1
    journal lastpage121010-10
    page10
    treeJournal of Engineering for Gas Turbines and Power:;2024:;volume( 146 ):;issue: 012
    contenttypeFulltext
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