Computational fluid dynamics (CFD) has been employed in the simulation of a wide variety of phenomenon in areas such as aerodynamics, meteorology, oceanography, combustion and biomedical flows. However the usefulness of computational fluid dynamics is limited by an inability to perform system simulations in a timely fashion. This is due to the complexity and computational expense of performing high fidelity unsteady CFD simulations. The fundamental goal of this research is the development of a rapid, robust and efficient reduced order simulation technique for accelerating CFD simulation. The reduced order simulation environment must be applicable to the study of fluid flow problems across a wide spectrum of applications domains. Existing techniques for improving the performance of fluids simulations are: (a) parallelization of the computations and (b) employment of reduced order modeling. Each of these techniques alone have demonstrated reductions of ten-fold to a hundred-fold in simulation time. However, the practical application of proper orthogonal decomposition (POD) has not been realized due to theinability to quantify the accuracy of the reduced order model without a comparison to a full simulation of the same scenario. This work will investigate and demonstrate feasibility of POD simulation methods and the ability to use residual tracking and related methods to validate and correct reduced order simulations. In addition we will demonstrate self-adaptive performance control of a 3D parallel CFD solver.
