Parallel Eulerian-Lagrangian Method with Adaptive Mesh Refinement for Moving Boundary Computation

摘要

In this study, we present a parallelized adaptive moving boundary computation technique on distributed memory multi-processor systems for multi-scale multiphase flow simulations. The solver utilizes the Eulerian-Lagrangian method to track moving (Lagrangian) interfaces explicitly on the stationary (Eulerian) Cartesian grid where the flow fields are computed. Since there exists strong data and task dependency between two distinct Eulerian and Lagrangian domain, we address the decomposition strategies for each domain separately. We then propose a trade-off approach aiming for parallel scalability. Spatial domain decomposition is adopted for both Eulerian and Lagrangian domains for load balancing and data locality to minimize inter-processor communication. In addition, a cell-based unstructured parallel adaptive mesh refinement (AMR) technique is implemented for the flexible local refinement with efficient grid usage and even-distributed computational workload among processors. The parallel performance is evaluated independently for the Cartesian grid solver and sub-procedures in cell-based unstructured AMR. The capability and the overall performance of the parallel adaptive Eulerian-Lagrangian method including moving boundary and topological change is demonstrated by modeling binary droplet collisions. With the aid of the present techniques, large scale moving boundary problems can be effectively computed.