Adaptive chemistry via pre-partitioning of composition space and mechanism reduction

Numerical simulations of multi-dimensional laminar flames with complex kinetic mechanisms are computationally very demanding, because of the large number of species and the strong non-linearity and stiffness of governing equations. In this work, we present and apply a novel adaptive chemistry methodology for mitigating the computational cost of such simulations, based on machine-learning algorithms which automatically classify the composition...