A Comparison of Physics-Based and Data-Based Methods of Dimension Reduction in Turbulent Combustion

Turbulent combustion simulation requires solving for many interdependent variables over disparate scales. To alleviate the computationally expensive nature of these simulations, reduced-order models for the thermochemical state are often necessary. Dimension reduction can be achieved through physics-based approaches, such as in Flamelet-Generated Manifolds or the Flamelet-Progress Variable model, or through data-based approaches, such as Principal Component Analysis. While both classes of approaches have been assessed separately, a combined analysis has not been reported. In this work, the efficacy of reduced-order models created via data-based and physics-based methods is assessed using 3D DNS databases of nonpremixed and premixed hydrogen-air jet flames and a more chemically-complex sooting flame, as well as 1D laminar flame calculations. The data-based techniques produce reasonably accurate representations of the thermochemical state using almost as few parameters as the physics-based models. The leading parameters of the data-based models have similar physical interpretation to the parameters in the physics-based models, indicating the potential to discover additional model parameters in more complex configurations.