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      Error Analysis of Supremizer Pressure Recovery for POD based Reduced Order Models of the time-dependent Navier-Stokes Equations

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          Abstract

          For incompressible flow models, the pressure term serves as a Lagrange multiplier to ensure that the incompressibility constraint is satisfied. In engineering applications, the pressure term is necessary for calculating important quantities based on stresses like the lift and drag. For reduced order models generated via a Proper orthogonal decomposition, it is common for the pressure to drop out of the equations and produce a velocity-only reduced order model. To recover the pressure, many techniques have been numerically studied in the literature; however, these techniques have undergone little rigorous analysis. In this work, we examine two of the most popular approaches: pressure recovery through the Pressure Poisson equation and recovery via the momentum equation through the use of a supremizer stabilized velocity basis. We examine the challenges that each approach faces and prove stability and convergence results for the supremizer stabilized approach. We also investigate numerically the stability and convergence of the supremizer based approach, in addition to its performance against the Pressure Poisson method.

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          Most cited references27

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          Galerkin Proper Orthogonal Decomposition Methods for a General Equation in Fluid Dynamics

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            Galerkin proper orthogonal decomposition methods for parabolic problems

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              The need for a pressure-term representation in empirical Galerkin models of incompressible shear flows

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                Author and article information

                Journal
                12 September 2019
                Article
                1909.06022
                d352559a-0736-4859-9097-0dfa2de3cafe

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                math.NA cs.NA

                Numerical & Computational mathematics
                Numerical & Computational mathematics

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