P(CA)²: PCA-Based Cell Agglomeration for CFD Simulations with Detailed Kinetic Mechanisms

Moreover, the PCA space is dynamically updated during the simulation, allowing the clustering procedure to adapt to the evolving thermochemical structure of the reacting flow. The P(CA)² methodology was assessed on an unsteady laminar coflow diffusion flame using kinetic mechanisms of increasing complexity, including a mechanism with 224 species and 5939 reactions. The method was also tested on a temporally evolving turbulent planar jet flame.

The results show that P(CA)² provides accurate solutions while achieving significant computational acceleration, with effective overall speed-ups reaching approximately 70–80% of the asymptotic maximum allowed by the nonchemistry fraction of the computational cost. The proposed approach removes the need for heuristic selection of clustering variables, provides systematic control of the solution accuracy, and can be easily integrated into existing CFD solvers based on operator splitting.

These features make P(CA)² a promising tool for accelerating simulations of reactive flows involving detailed chemical kinetic mechanisms.