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In this work a stabilised and reduced Galerkin projection of the incompressible unsteady Navier-Stokes equations for moderate Reynolds number is presented. The full-order model, on which the Galerkin projection is applied, is based on a…

Numerical Analysis · Mathematics 2019-02-08 Giovanni Stabile , Gianluigi Rozza

We present a component-based model order reduction procedure to efficiently and accurately solve parameterized incompressible flows governed by the Navier-Stokes equations. Our approach leverages a non-overlapping optimization-based domain…

Numerical Analysis · Mathematics 2023-11-01 Tommaso Taddei , Xuejun Xu , Lei Zhang

A model reduction technique based on an optimization principle is employed to coarse-grain inviscid, incompressible fluid dynamics in two dimensions. In this reduction the spectrally-truncated vorticity equation defines the microdynamics,…

Fluid Dynamics · Physics 2016-09-21 Bruce Turkington , Qian-Yong Chen , Simon Thalabard

In this effort we propose a data-driven learning framework for reduced order modeling of fluid dynamics. Designing accurate and efficient reduced order models for nonlinear fluid dynamic problems is challenging for many practical…

Computational Physics · Physics 2018-12-05 Xuping Xie , Guannan Zhang , Clayton G. Webster

We present a high-order accurate reconstructed discontinuous Galerkin (rDG) method in arbitrary Lagrangian-Eulerian (ALE) formulation, for solving two-dimensional compressible flows on moving and deforming domains with unstructured curved…

Computational Physics · Physics 2020-05-26 Chuanjin Wang , Hong Luo

A new high order accurate semi-implicit space-time Discontinuous Galerkin method on staggered grids, for the simulation of viscous incompressible flows on two-dimensional domains is presented. The designed scheme is of the Arbitrary…

Numerical Analysis · Mathematics 2020-03-17 Francesco Lohengrin Romeo

Because the Navier-Stokes equations are dissipative, the long-time dynamics of a flow in state space are expected to collapse onto a manifold whose dimension may be much lower than the dimension required for a resolved simulation. On this…

Fluid Dynamics · Physics 2023-01-12 Alec J. Linot , Michael D. Graham

We propose a novel method for model-based time super-sampling of turbulent flow fields. The key enabler is the identification of an empirical Galerkin model from the projection of the Navier-Stokes equations on a data-tailored basis. The…

We present a comparative computational study of two stabilized Reduced Order Models (ROMs) for the simulation of convection-dominated incompressible flow (Reynolds number of the order of a few thousands). Representative solutions in the…

Fluid Dynamics · Physics 2024-05-01 Pierfrancesco Siena , Michele Girfoglio , Annalisa Quaini , Gianluigi Rozza

Model order reduction in high-dimensional, nonlinear dynamical systems if often enabled through fast-slow timescale separation. One such approach involves identifying a low-dimensional slow manifold to which the state rapidly converges and…

Dynamical Systems · Mathematics 2026-05-14 Dan Wilson

Leveraging recent work on data-driven methods for constructing a finite state space Markov process from dynamical systems, we address two problems for obtaining further reduced statistical representations. The first problem is to extract…

Chaotic Dynamics · Physics 2024-05-14 Ludovico Theo Giorgini , Andre N. Souza , Peter J. Schmid

In this paper, we consider flow and transport problems in thin domains. The mathematical model considered in the paper is described by a system of equations for velocity, pressure, and concentration, where the flow is described by the…

Numerical Analysis · Mathematics 2021-07-07 Maria Vasilyeva , Valentin Alekseev , Eric T. Chung , Yalchin Efendiev

Reduced-order models have been widely adopted in fluid mechanics, particularly in the context of Newtonian fluid flows. These models offer the ability to predict complex dynamics, such as instabilities and oscillations, at a considerably…

Fluid Dynamics · Physics 2023-12-05 Cassio M. Oishi , Alan A. Kaptanoglu , J. Nathan Kutz , Steven L. Brunton

We present a new method for approximating solutions to the incompressible miscible displacement problem in porous media. At the discrete level, the coupled nonlinear system has been split into two linear systems that are solved…

Computational Engineering, Finance, and Science · Computer Science 2018-09-18 Maurice S. Fabien , Matthew G. Knepley , Beatrice M. Riviere

Reduced-order models (ROMs) of turbulent flows based on Galerkin projection often require many degrees of freedom to resolve the dynamics of the turbulence, or simulation data to obtain an optimal modal basis. However, obtaining simulation…

Fluid Dynamics · Physics 2025-11-21 Ian Addison-Smith , Igor A. Maia , Benjamin Herrmann , Andre V. G. Cavalieri

This paper presents a high-order discontinuous Galerkin finite element method to solve the barotropic version of the conservative symmetric hyperbolic and thermodynamically compatible (SHTC) model of compressible two-phase flow, introduced…

Numerical Analysis · Mathematics 2025-01-29 Laura Río-Martín , Michael Dumbser

This paper is concerned with the partitioned iterative formulation to simulate the fluid-structure interaction of a nonlinear multibody system in an incompressible turbulent flow. The proposed formulation relies on a three-dimensional (3D)…

Fluid Dynamics · Physics 2019-03-05 P S Gurugubelli , R Ghoshal , V Joshi , R K Jaiman

A new approach to modelling free surface flows is developed that enables, for the first time, 3D consistent non-hydrostatic baroclinic physics that wets and dries in the large aspect ratio spatial domains that characterise geophysical…

Fluid Dynamics · Physics 2017-03-28 Adam S. Candy

This work presents a novel stabilization strategy for the Galerkin formulation of the incompressible Navier-Stokes equations, developed to achieve high accuracy while ensuring convergence and compatibility with high-order elements on…

Numerical Analysis · Mathematics 2025-09-05 Antonio Blanco-Casares , Vishal Kumar , Daniel Mira , Oriol Lehmkuhl

We introduce a family of stochastic models motivated by the study of nonequilibrium steady states of fluid equations. These models decompose the deterministic dynamics of interest into fundamental building blocks, i.e., minimal vector…

Probability · Mathematics 2025-05-07 Andrea Agazzi , Jonathan C. Mattingly , Omar Melikechi
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