Data-assisted combustion simulations with dynamic submodel assignment using random forests

• Published in: arXiv.org 2021-01
• Main Authors: Wai Tong Chung, Aashwin Ananda Mishra, Perakis, Nikolaos, Ihme, Matthias
• Format: Article
• Language: English
• Subjects: Classifiers; Combustion chambers; Computational fluid dynamics; Computer simulation; Flow simulation; Local flow; Reacting flow; Simulation
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2009.04023

In this investigation, we outline a data-assisted approach that employs random forest classifiers for local and dynamic combustion submodel assignment in turbulent-combustion simulations. This method is applied in simulations of a single-element GOX/GCH4 rocket combustor; a priori as well as a posteriori assessments are conducted to (i) evaluate the accuracy and adjustability of the classifier for targeting different quantities-of-interest (QoIs), and (ii) assess improvements, resulting from the data-assisted combustion model assignment, in predicting target QoIs during simulation runtime. Results from the a priori study show that random forests, trained with local flow properties as input variables and combustion model errors as training labels, assign three different combustion models - finite-rate chemistry (FRC), flamelet progress variable (FPV) model, and inert mixing (IM) - with reasonable classification performance even when targeting multiple QoIs. Applications in a posteriori studies demonstrate improved predictions from data-assisted simulations, in temperature and CO mass fraction, when compared with monolithic FPV calculations. These results demonstrate that this data-driven framework holds promise for the dynamic combustion submodel assignment in reacting flow simulations.