Within the past thirty years, a variety of subgrid scale closures have been developed for large eddy simulation (LES) of turbulent flows. The filtered density function (FDF) is one of such closures and has proven very effective in a variety of applications, especially for LES of reactive flows. Despite its demonstrated capabilities, the computational cost associated with FDF can be expensive compared to other (more conventional) methods. This problem can be effectively alleviated by taking advantages of modern developments in computing and information science.
A novel strategy is to couple an architecture aware graph partitioning algorithm with a dynamic (re)partitioning framework. This provides an optimal load balance, while minimizing the cost of data migration. Each of the partitions is treated via an entirely self-contained solver (in either Eulerian or Lagrangian contexts). The communication between the solvers is local, and shared information is limited to neighboring partitions. Quantum computing is also very promising for future LES via FDF. Recent developments in quantum enhanced measurements provide an algorithm that facilitates a quadratic speedup over classical FDF solvers. This demonstration identifies FDF as a viable problem to take advantage of speedups offered by future quantum computers.