Development of a Chemistry Dynamic Load Balancing Solver with Sparse Analytical Jacobian Approach for Rapid and Accurate Reactive Flow Simulations

In addressing the demands of industrial high-fidelity computation, the present study introduces a rapid and accurate customized solver developed on the OpenFOAM platform. To enhance computational efficiency, a novel integrated acceleration strategy is introduced. Initially, a sparse analytical Jacobian approach utilizing the SpeedCHEM chemistry library was implemented to increase the efficiency of the ODE solver. Subsequently, the Dynamic Load Balancing (DLB) code was employed to uniformly distribute the computational workload for chemistry among multiple processes. Further optimization was achieved through the introduction of the Open Multi-Processing (OpenMP) method to enhance parallel computing efficiency. Lastly, the Local Time Stepping (LTS) scheme was integrated to maximize the individual time step for each computational cell, resulting in a noteworthy minimum speed-up of over 31 times. The effectiveness and robustness of this customized solver were systematically validated against three distinct partially turbulent premixed flames, Sandia Flames D, E, and F. Additionally, a comparative analysis was conducted, encompassing different turbulence models, turbulent Prandtl numbers, and model constants, resulting in the recommendation of optimal numerical parameters for various conditions. The present study offers one viable solution for rapid and accurate calculations in the OpenFOAM platform, while also providing insights into the selection of turbulence models and parameters for industrial numerical simulation.