Abstract

The numerical modeling of multi-dimensional reactive flows with detailed kinetic mechanisms (including hundreds of species and thousands of reactions) places severe demands on computational resources, because of the number of strongly coupled equations involved and their high non-linearity and stiffness. This talk presents and discusses some numerical techniques for accelerating the simulations of multi-dimensional laminar and turbulent reactive flows in the OpenFOAM framework, with special emphasis on combustion. Starting from an introduction about the operator-splitting method, the connections between the features of detailed kinetic mechanisms and the ODE solvers for solving the chemical step will be analyzed. Then, the focus will be shifted on novel adaptive chemistry methodologies, like SPARC (Sample-Partitioning Adaptive Reduced Chemistry), based on machine-learning algorithms which automatically classify the composition space via a priori defined classifiers. Finally, Cell Agglomeration (CA) algorithms, combined Principal Component Analysis (PCA), like in P(CA)2, or with tabulated chemistry, will be presented as a further step to mitigate the computational cost associated to detailed kinetic mechanisms. To better show the potential benefits and limitations of the described acceleration techniques, several examples will be presented and discussed: laminar coflow flames, temporally-evolving planar jet-flames, turbulent non-premixed flames in decaying isotropic turbulence, etc.

Alberto Cuoci

Full Professor of Chemical Engineering

My research interests include Computational Fluid Dynamics (CFD) and numerical modeling of chemical reactors.