Student Posters Repository

A pre-partitioned adaptive chemistry (PPAC) methodology has been proposed recently by Liang et al. (Combustion and Flame, 2015) for reducing the CPU time and memory requirement of particle PDF methods. PPAC generates a library of reduced kinetic models in an offline preprocessing stage. At runtime, these reduced models are dynamically utilized to perform reaction integration. The particles retain only the reduced skeletal representation during the adaptive simulation, leading to memory reduction and the use of reduced models for reaction integration leads to a reduction in CPU time. PPAC has been augmented by coupling it with in-situ adaptive tabulation (ISAT) to further improve upon the CPU time reduction (Newale et al., 10th US National Combustion Meeting, 2017). The testing of PPAC and PPAC-ISAT in these works was done in a simplified PaSR configuration. This works examines the performance of PPAC methodology in a LES/PDF simulation of Sandia flame D. We show that the PPAC methodology leads to a significant reduction in the memory requirements. This is especially due to the use of a highly reduced mechanism for the coflow, which covers a large section of the computational domain. The PPAC methodology is also shown to provide a sizable reduction in the CPU time through the use of reduced mechanisms for performing reaction integration.