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Gradient boosted decision trees for combustion chemistry integration

Yao, S. ; Kronenburg, A.; Shamooni, A.; Stein, Oliver Thomas ORCID iD icon; Zhang, W.


This study introduces the gradient boosted decision tree (GBDT) as a machine learning approach to circumvent the need for a direct integration of the typically stiff system of ordinary differential equations that govern the temporal evolution of chemically reacting species. Stiffness primarily relates to the chemistry integration and here, hydrogen/air systems are taken to train and test the ensemble learning approach. We use the LightGBM (Light Gradient Boosting Machine) algorithm to train GBDTs on the time series of various self-igniting mixtures from the time of ignition to equilibrium composition. The GBDT model provides reasonable predictions of the species compositions and thermodynamic states at the next time step in an a priori study. A much more challenging a posteriori study shows that the model can reproduce a full time–history profile of the igniting H/air mixtures, as the results agree very well with those obtained from a direct integration of the ODEs. The GBDT model can be deployed as standalone C++ codes and a speed-up by one order of magnitude has been demonstrated. The GBDT approach can thus be considered as an efficient method to represent the chemical kinetics in the simulation of reactive flows. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000158658
Veröffentlicht am 12.05.2023
DOI: 10.1016/j.jaecs.2022.100077
Zitationen: 21
Zitationen: 20
Cover der Publikation
Zugehörige Institution(en) am KIT Engler-Bunte-Institut (EBI)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 09.2022
Sprache Englisch
Identifikator ISSN: 2666-352X
KITopen-ID: 1000158658
Erschienen in Applications in Energy and Combustion Science
Verlag Elsevier B.V.
Band 11
Seiten Art.-Nr.: 100077
Vorab online veröffentlicht am 03.08.2022
Externe Relationen Siehe auch
Schlagwörter Ensemble learning, Gradient boosting, Chemical kinetics, Hydrogen combustion
Nachgewiesen in Scopus
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