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In this work we present a reduced basis Smagorinsky turbulence model for steady flows. We approximate the non-linear eddy diffusion term using the Empirical Interpolation Method, and the velocity-pressure unknowns by an independent…

It is well known in the Reduced Basis approximation of saddle point problems that the Galerkin projection on the reduced space does not guarantee the inf-sup approximation stability even if a stable high fidelity method was used to generate…

Numerical Analysis · Mathematics 2023-08-08 Shafqat Ali , Francesco Ballarin , Gianluigi Rozza

In this work we present a Reduced Basis VMS-Smagorinsky Boussinesq model, applied to natural convection problems in a variable height cavity, in which the buoyancy forces are involved. We take into account in this problem both physical and…

We present a localized a-posteriori error estimate for the localized reduced basis multi-scale (LRBMS) method [Albrecht, Haasdonk, Kaulmann, Ohlberger (2012): The localized reduced basis multiscale method]. The LRBMS is a combination of…

Numerical Analysis · Mathematics 2019-10-30 Mario Ohlberger , Felix Schindler

In this work, we introduce an a posteriori error indicator for the reduced basis modelling of turbulent flows. It is based upon the $k^{-5/3}$ Kolmogorov turbulence theory, thus it may be applied to any numerical discretisation of LES…

This paper presents a rigorous theoretical extension of the Smagorinsky model for turbulence simulations. The author builds on its fundamental framework, addressing known limitations, and making new mathematical advances. Specifically, this…

Fluid Dynamics · Physics 2024-11-19 Rômulo Damasclin Chaves dos Santos

The purpose of this work is to present a reduced order modeling framework for parametrized turbulent flows with moderately high Reynolds numbers within the variational multiscale (VMS) method. The Reduced Order Models (ROMs) presented in…

Numerical Analysis · Mathematics 2023-08-08 Giovanni Stabile , Francesco Ballarin , Giacomo Zuccarino , Gianluigi Rozza

This paper studies non inf-sup stable finite element approximations to the evolutionary Navier--Stokes equations. Several local projection stabilization (LPS) methods corresponding to different stabilization terms are analyzed, thereby…

Numerical Analysis · Mathematics 2017-09-27 Javier de Frutos , Bosco García-Archilla , Volker John , Julia Novo

We consider a class of parameter-dependent optimal control problems of elliptic PDEs with constraints of general type on the control variable. Applying the concept of variational discretization, [4], together with techniques from the…

Optimization and Control · Mathematics 2018-08-20 Ahmad Ahmad Ali , Michael Hinze

In the Reduced Basis approximation of Stokes and Navier-Stokes problems, the Galerkin projection on the reduced spaces does not necessarily preserved the inf-sup stability even if the snapshots were generated through a stable full order…

Numerical Analysis · Mathematics 2023-08-08 Shafqat Ali , Francesco Ballarin , Gianluigi Rozza

In this paper, we propose a new stabilized projection-based POD-ROM for the numerical simulation of incompressible flows. The new method draws inspiration from successful numerical stabilization techniques used in the context of Finite…

Numerical Analysis · Mathematics 2019-07-23 Samuele Rubino

The predictive simulation of fluid dynamics in densely packed microfluidic devices, such as Deterministic Lateral Displacement (DLD) arrays, stagnates with standard iterative solvers. We show that this failure is not algorithmic but rooted…

Numerical Analysis · Mathematics 2026-05-26 Qi Xin , Shihua Gong , Jinchao Xu

We develop a feedback control framework for stabilizing the McKean-Vlasov PDE on the torus. Our goal is to steer the dynamics toward a prescribed stationary distribution or accelerate convergence to it using a time-dependent control…

Optimization and Control · Mathematics 2026-05-01 Dante Kalise , Lucas M. Moschen , Grigorios A. Pavliotis

This work proposes an adaptive structure-preserving model order reduction method for finite-dimensional parametrized Hamiltonian systems modeling non-dissipative phenomena. To overcome the slowly decaying Kolmogorov width typical of…

Numerical Analysis · Mathematics 2022-02-02 Jan S. Hesthaven , Cecilia Pagliantini , Nicolò Ripamonti

In this paper, we propose a certified reduced basis (RB) method for quasilinear elliptic problems together with its application to nonlinear magnetostatics equations, where the later model permanent magnet synchronous motors (PMSM). The…

Numerical Analysis · Mathematics 2020-07-03 Michael Hinze , Denis Korolev

We present a stabilized POD-Galerkin reduced order method (ROM) for a Leray model. For the implementation of the model, we combine a two-step algorithm called Evolve-Filter (EF) with a computationally efficient finite volume method. In both…

Numerical Analysis · Mathematics 2021-07-01 Michele Girfoglio , Annalisa Quaini , Gianluigi Rozza

Proper orthogonal decomposition (POD) stabilized methods for the Navier-Stokes equations are considered and analyzed. We consider two cases, the case in which the snapshots are based on a non inf-sup stable method and the case in which the…

Numerical Analysis · Mathematics 2020-06-02 Julia Novo , Samuele Rubino

The aim of this paper is to introduce a consistent velocity smoothing method for smoothed particle hydrodynamics (SPH). First the locally averaged Navier-Stokes equations are derived in a mathematically rigorous way to demonstrate the…

Fluid Dynamics · Physics 2018-07-31 Kalale Chola

We present a reduced basis technique for long-time integration of parametrized incompressible turbulent flows. The new contributions are threefold. First, we propose a constrained Galerkin formulation that corrects the standard Galerkin…

Numerical Analysis · Mathematics 2017-10-11 Lambert Fick , Yvon Maday , Anthony T Patera , Tommaso Taddei

Large eddy simulation has been widely used to simulate turbulence at balanced computational cost and accuracy. Many Subgrid-Scale (SGS) models have been proposed over the years, where data-driven and machine learning-aided approaches set…

Fluid Dynamics · Physics 2026-05-13 Takeru Hashimoto , Takahiro Tsukahara , Ryo Araki
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