2022 journal article

Principal component analysis based combustion model in the context of a lifted methane/air flame: Sensitivity to the manifold parameters and subgrid closure

Combustion and Flame.

co-author countries: Belgium 🇧🇪 United States of America 🇺🇸
author keywords: Turbulent combustion; Principal component analysis; Nonlinear regression; Large eddy simulation; Auto-ignition; Low-dimensional manifolds
Source: ORCID
Added: June 12, 2022

The present work advances the PC-transport approach in the context of Large Eddy Simulation (LES) of turbulent combustion. Accurate modeling of combustion systems requires large kinetic mechanisms. However, realistic high-fidelity simulations of turbulent reacting flows still represent a big challenge on the current computational tools. Therefore, a parameterization of the thermo-chemical state-space using a reduced number of variables is needed. To this end, the potential offered by Principal Component Analysis (PCA) in identifying low-dimensional manifolds is very appealing. The present paper extends the PC-transport approach, coupled with Gaussian Process Regression (GPR), to a lifted methane/air flame in LES. Previous investigations by the authors showed the great potential of the PC-GPR model in the context of Sandia flames. This study investigated some key features of the model: the sensitivity to the training data set and the scaling methods . To this end, two different canonical reactors were used: unsteady counter-flow laminar flames (CFLF) and unsteady perfectly stirred reactor (PSR). Moreover, the authors proposes an approach to address the issue of data density inherent to large numerical data sets, by means of a kernel density weighting of the data set before applying PCA. Finally, a subgrid scale (SGS) closure model was coupled to the PC-transport approach to treat complex turbulence/chemistry interactions.