Reduced Order Modeling of Unsteady Flows Using Sparse Bases
Abstract
This effort will leverage sparse coding principles to construct accurate, robust, and efficient reduced order representations of the Navier-Stokes equations for sea-based aviation problems. Specifically, the effort will seek to identify a sparse set of modes in unsteady, nonlinear flow fields. The sparse basis approach will be applied to a variety of datasets ranging from those computed with Direct Numerical Simulations (DNS) and Large Eddy Simulations (LES) on structured meshes to Detached Eddy Simulation (DES) and Unsteady Reynolds Averaged Navier-Stokes (URANS) on unstructured meshes. Initial applications will include flow predictions for canonical configurations - oscillating cylinders, airfoils, backward facing steps, and combinations of these. Using these flow fields, the PI will seek both improved understanding on the unique properties of sparse bases in the context of reduced order solution of the Navier-Stokes equations, and adaptive procedures for non-stationary mean flow conditions.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 12, 2016
- Source ID
- N000141612620
Entities
People
- Jack J. McNamara
Organizations
- Office of Naval Research
- Ohio State University
- United States Navy