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P-DRGEP: A novel methodology for the reduction of kinetics mechanisms for plasma-assisted combustion applications

par Bellemans, Aurélie ;Kincaid, Nicholas;Deak, Nicholas;Pepiot, Perrine;Bisetti, Fabrizio
Référence Proceedings of the Combustion Institute
Publication Publié, 2020-11-01

Article révisé par les pairs

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Résumé :

Detailed kinetics mechanisms for plasma-assisted combustion contain numerous species and reactions that model the interplay between non-equilibrium plasma processes and hydrocarbon oxidation. While physically accurate and comprehensive, such detailed mechanisms are impractical for simulations of unsteady multi-dimensional plasma discharges and their effect on reactive mixtures in practical devices. In this work, we develop and apply a novel methodology for the reduction of large detailed plasma-assisted combustion mechanisms to smaller skeletal ones. The methodology extends the Directed Relation Graph with Error Propagation (DRGEP) approach in order to consider the energy branching characteristics of plasma discharges during the reduction. Ensuring tight error tolerances on the relative proportions of energy lost by the electrons to the various classes of impact processes (i.e. vibrational and electronic excitation, ionization, and impact dissociation) is key to preserving the correct discharge physics in the skeletal mechanism. To this end, new targets that include energy transfers are defined and incorporated in DRGEP. The performance of the novel framework, called P-DRGEP, is assessed for the simulation of ethylene-air ignition by nanosecond repetitive pulsed discharges at conditions relevant to supersonic combustion and flame holding in scramjet cavities, i.e. from 600 K to 1000 K, 0.5 atm, and equivalence ratios between 0.75 and 1.5. P-DRGEP is found to be greatly superior to the traditional reduction approach applied to plasma-assisted ignition in that it generates a smaller skeletal mechanism with significantly lower errors. For ethylene-air ignition at the target conditions, P-DRGEP generates a skeletal mechanism with 54 species and 236 reactions, resulting in a 84% computational speed-up for ignition simulations, while guaranteeing errors below 10% on the time required for ignition following the first pulse, 1% on the mean electron energy, between 4 and 35% on electron energy losses depending on the process, and 5% on the laminar flame speed.