Model Reduction for Compressible Cavity Simulations Towards Uncertainty Quantification of Structural Loading

This report summarizes FY16 progress towards enabling uncertainty quantification for compressible cavity simulations using model order reduction (MOR). The targeted application is the quantification of the captive-carry environment for the design and qualification of nuclear weapons systems. To accurately simulate this scenario, Large Eddy Simulations (LES) require very fine meshes and long run times, which lead to week-long runs even on parallel state-of-the-art supercomputers. MOR can reduce substantially the CPU-time requirement for these simulations. We describe two approaches for nonlinear model order reduction, which can yield significant speedups when combined with hyper-reduction: the Proper Orthogonal Decomposition (POD)/Galerkin method and the POD/Least-Squares Petrov Galerkin (LSPG) method. The implementation of these methods within the in-house compressible flow solver SPARC is discussed. Next, a method for stabilizing and enhancing low-dimensional reduced bases that was developed as a part of this project is detailed. This approach is based on a premise termed “minimal subspace rotation”, and has the