Parallel solution-adaptive scheme for multi-phase core flows in rocket motors

The development of a parallel adaptive mesh refinement (AMR) scheme is described for solving the governing equations for multi-phase (gas-particle) core flows in solid pro-pellant rocket motors (SRM). An Eulerian formulation is used for both the gas and particle phases, which leads to a degenerate hyperbolic system of partial differential equations. The cause and effect of the degeneracy is examined. A cell-centered upwind finite-volume discretization and the use of limited solution reconstruction, Riemann solver based flux functions for the gas and particle phases, and explicit multi-stage time-stepping allows for high solution accuracy and computational robustness. A Riemann problem is formulated for prescribing boundary data at the burning surface of the pro-pellant grain and an iterative solver is proposed for its solution. Efficient and scalable parallel implementations are achieved with domain decomposition on distributed memory multi-processor architectures. High-scalability of the model has been achieved on a Beowulf-class cluster consisting of 104 processors. Numerical results are described to demonstrate the capabilities of the approach for predicting SRM core flows.