Lawrence Berkeley National Laboratory

Recent progress in simulation methodologies and new, high-performance parallel architectures have made it is possible to perform detailed simulations of multidimensional combustion phenomena using comprehensive kinetics mechanisms. However, as simulation complexity increases, it becomes increasingly difficult to extract detailed quantitative information about the flame from the numerical solution, particularly regarding the details of chemical processes. In this paper we present a new diagnostic tool for analysis of numerical simulations of combustion phenomena. Our approach is based on recasting an Eulerian flow solution in a Lagrangian frame. Unlike a conventional Lagrangian viewpoint in which we follow the evolution of a volume of the fluid, we instead follow specific chemical elements, e.g., carbon, nitrogen, etc., as they move through the system. From this perspective an "atom" is part of some molecule that is transported through the domain by advection and diffusion. Reactions cause the atom to shift from one species to another with the subsequent transport given by the movement of the new species. We represent these processes using a stochastic particle formulation that treats advection deterministically and models diffusion as a suitable random-walk process. Within this probabilistic framework, reactions can be viewed as a Markov process transforming molecule to molecule with given probabilities. In this paper, we discuss the numerical issues in more detail and demonstrate that an ensemble of stochastic trajectories can accurately capture key features of the continuum solution. We also illustrate how the method can be applied to studying the role of cyanochemistry on NOx production in a diffusion flame.