A dynamic data-driven application simulation framework for contaminant transport problems

We describe, devise, and augment dynamic data-driven application simulations (DDDAS). DDDAS offers interesting computational and mathematically unsolved problems, such as, how do you analyze, compute, and predict the solution of a generalized PDE when you do not know either where or what the boundary conditions are at any given moment in the simulation in advance? A summary of DDDAS features and why this is a intellectually stimulating new field are included in the paper. We apply the DDDAS methodology to some examples from a contaminant transport problem. We demonstrate that the multiscale interpolation and backward in time error monitoring are useful to long running simulations.