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Numerical simulations of turbulence provide non-intrusive access to all the resolved scales, although they often invoke idealizations that can compromise realism. In contrast, experimental measurements probe the true flow with lesser…

流体动力学 · 物理学 2021-09-22 Tamer A. Zaki , Mengze Wang

The numerical approximation of solutions to the compressible Euler and Navier-Stokes equations is a crucial but challenging task with relevance in various fields of science and engineering. Recently, methods from deep learning have been…

流体动力学 · 物理学 2024-01-30 Simon Wassing , Stefan Langer , Philipp Bekemeyer

Multi-scale, multi-fidelity numerical simulations form the pillar of scientific applications related to numerically modeling fluids. However, simulating the fluid behavior characterized by the non-linear Navier Stokes equations are often…

The Navier-Stokes-Voigt (NSV) model of viscoelastic incompressible fluid has been recently proposed as a regularization of the 3D Navier-Stokes equations for the purpose of direct numerical simulations. In this work we investigate its…

流体动力学 · 物理学 2009-01-06 Boris Levant , Fábio Ramos , Edriss S. Titi

The dynamics defined by the Navier-Stokes equations under the Marangoni boundary conditions in a two dimensional domain is considered. This model of fluid dynamics involve fundamental physical effects: convection, diffusion and capillary…

偏微分方程分析 · 数学 2015-11-09 Sergei Vakulenko

It is a classical problem in fluid dynamics about the stability and instability of different hydrodynamic patterns in various physical settings, in particular in the high Reynolds number limit of laminar flow with boundary layer. However,…

偏微分方程分析 · 数学 2023-08-29 Tong Yang , Zhu Zhang

We showcase the plain diffusion models with Transformers are effective predictors of fluid dynamics under various working conditions, e.g., Darcy flow and high Reynolds number. Unlike traditional fluid dynamical solvers that depend on…

机器学习 · 计算机科学 2024-09-23 Dongyu Luo , Jianyu Wu , Jing Wang , Hairun Xie , Xiangyu Yue , Shixiang Tang

This proposed work introduces a data-assimilation-assisted approach to train neural networks, aimed at effectively reducing epistemic uncertainty in state estimates of separated flows. This method, referred to as model-consistent training,…

流体动力学 · 物理学 2024-08-02 Minghan Chu

A new model for the "rapid" part of the velocity/pressure-gradient correlation in the Reynolds averaged Navier-Stokes equations is suggested. It is shown that in an inhomogeneous incompressible turbulent flow, the model that is linear in…

流体动力学 · 物理学 2007-05-23 Svetlana V. Poroseva

Numerical simulations of turbulent flows are well known to pose extreme computational challenges due to the huge number of dynamical degrees of freedom required to correctly describe the complex multi-scale statistical correlations of the…

流体动力学 · 物理学 2025-02-27 Giulio Ortali , Alessandro Gabbana , Nicola Demo , Gianluigi Rozza , Federico Toschi

The fate of small particles in turbulent flows depends strongly on the surrounding fluid's velocity gradient properties such as rotation and strain-rates. For non-inertial (fluid) particles, the Restricted Euler model provides a simple,…

流体动力学 · 物理学 2017-04-05 Perry L. Johnson , Charles Meneveau

Turbulent flow over permeable interface is omnipresent featuring complex flow topology. In this work, a data driven, end to end machine learning model has been developed to model the turbulent flow in porous media. For the same, we have…

流体动力学 · 物理学 2023-11-28 Xu Chu , Sandeep Pandey

Turbulent dynamical systems characterized by both a high-dimensional phase space and a large number of instabilities are ubiquitous among many complex systems in science and engineering. The existence of a strange attractor in the turbulent…

流体动力学 · 物理学 2018-02-23 Andrew J. Majda , Di Qi

The influence of turbulent effects on a fluid flow through a (pseudo) porous media is studied by numerically solving the set of Reynolds-averaged Navier-Stokes equations with the $\kappa$-$\epsilon$ model for turbulence. The spatial domains…

统计力学 · 物理学 2009-11-07 H. H. M. Vasconcelos , U. M. S. Costa , M. P. Almeida

We study mathematical and physical properties of a family of recently introduced, reduced-order approximate deconvolution models. We first show a connection between these models and the NS-Voigt model, and that NS-Voigt can be re-derived in…

偏微分方程分析 · 数学 2015-04-21 L. C. Berselli , T. -Y. Kim , L. G. Rebholz

The applicability of computational fluid dynamics (CFD) based design tools depend on the accuracy and complexity of the physical models, for example turbulence models, which remains an unsolved problem in physics, and rotor models that…

流体动力学 · 物理学 2023-05-09 Shanti Bhushan , Greg W Burgreen , Joshua L Bowman , Ian D Dettwiller , Wesley Brewer

Modelling hydrodynamic lubrication is crucial in the design of engineering components as well as for a fundamental understanding of friction mechanisms. The cornerstone of thin-film flow modelling is the Reynolds equation -- a…

流体动力学 · 物理学 2022-03-08 Hannes Holey , Andrea Codrignani , Peter Gumbsch , Lars Pastewka

This paper presents the first high-order computational fluid dynamics (CFD) simulations of static and spinning golf balls at realistic flow conditions. The present results are shown to capture the complex fluid dynamics inside the dimples…

流体动力学 · 物理学 2018-06-04 Jacob Crabill , Freddie Witherden , Antony Jameson

The resolvent formulation of the Navier$\text{--}$Stokes equations gives a means for the characterization and prediction of features of turbulent flows$\text{---}$such as statistics, structures and their nonlinear…

流体动力学 · 物理学 2019-08-28 Scott T. M. Dawson , Beverley J. McKeon

In this manuscript we propose and analyze weighted reduced order methods for stochastic Stokes and Navier-Stokes problems depending on random input data (such as forcing terms, physical or geometrical coefficients, boundary conditions). We…

数值分析 · 数学 2023-03-28 Julien Genovese , Francesco Ballarin , Gianluigi Rozza , Claudio Canuto