A statistical model to predict ignition probability

Esclapez, Lucas; Collin-Bastiani, Félix; Riber, Eleonore; Cuenot, Bénédicte

doi:10.1016/j.combustflame.2020.10.051

A statistical model to predict ignition probability

Journal Article · Wed Nov 11 00:00:00 EST 2020 · Combustion and Flame

DOI:https://doi.org/10.1016/j.combustflame.2020.10.051· OSTI ID:1762255

Esclapez, Lucas ^[1]; Collin-Bastiani, Félix ^[2]; Riber, Eleonore ^[3]; Cuenot, Bénédicte ^[3]

Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS), Toulouse (France); Safran Aircraft Engines, Moissy Cramayel (France); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Center for Computational Sciences and Engineering (CCSE)
Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS), Toulouse (France); Safran Aircraft Engines, Moissy Cramayel (France)
Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS), Toulouse (France)

Ignition capability is a critical design constraint for aeronautical gas turbines. However the current trend toward overall lean burn is detrimental to the engine ignition and relight and the ignition system must be adapted to ensure a fast and reliable light-round in all circumstances. As ignition is a stochastic phenomenon, the optimization of an ignition system requires to build ignition probability maps, which is difficult and costly with either experiment or numerical simulation as both require many tests. Here, this work proposes a model to predict the ignition probability map, knowing only flow statistics in non-reacting conditions, i.e., with only one test. The originality of the model is to construct statistics of the flame kernel trajectory, which are then combined with local flow indicators to evaluate the ignition probability at the considered sparking location. Application to a swirled burner operated in premixed, non-premixed and spray combustion modes illustrates the model concepts and demonstrates its ability to recover the experimental ignition map with good accuracy.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1762255

Journal Information:: Combustion and Flame, Journal Name: Combustion and Flame Vol. 225; ISSN 0010-2180

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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