Evaluation of Co-optimized Machine-Learned Manifolds for Modeling Premixed Combustion

Many modeling strategies for turbulent combustion employ a projection of the thermochemical state onto a low-dimensional manifold to reduce computational cost. Frequently, neural networks are applied to compute relevant thermochemical variables from the manifold for both physics-based approaches, such as flamelet generated manifolds (FGM), and for empirical approaches, such as principal component analysis (PCA). Recently, an approach to co-optimize the definition of the manifold and the mapping to (filtered) thermochemical outputs of the model has been proposed and shown to reduce the error associated with the neural network models in either case. In this work, the co-optimized manifolds approach is further evaluated and compared against FGM and PCA for representative premixed turbulent combustion systems. A particular emphasis is ensuring robustness of the neural networks when integrated into practical simulations.