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We consider a statistical limit of solutions to the compressible Navier--Stokes system in the high Reynolds number regime in a domain exterior to a rigid body. We investigate to what extent this highly turbulent regime can be modeled by an…

偏微分方程分析 · 数学 2022-02-09 Eduard Feireisl , Martina Hofmanova

As one of the violent flow, tornadoes occur in many place of the world. In order to reduce human losses and material damage caused by tornadoes, there are many research methods. One of the effective methods is numerical simulations such as…

偏微分方程分析 · 数学 2016-05-04 Pen-Yuan Hsu , Hirofumi Notsu , Tsuyoshi Yoneda

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

A fully-resolved direct-numerical-simulation (DNS) approach for investigating flexible bodies forced by a turbulent incoming flow is designed to study the flapping motion of a flexible flag at moderate Reynolds number. The incoming…

流体动力学 · 物理学 2021-08-25 Stefano Olivieri , Francesco Viola , Andrea Mazzino , Marco E. Rosti

A new approach to turbulence simulation, based on a combination of large-eddy simulation (LES) for the whole flow and an array of non-space-filling quasi-direct numerical simulations (QDNS), which sample the response of near-wall turbulence…

流体动力学 · 物理学 2017-10-20 Neil D. Sandham , Roderick Johnstone , Christian T. Jacobs

Reynolds-averaged Navier-Stokes (RANS) equations are presently one of the most popular models for simulating turbulence. Performing RANS simulation requires additional modeling for the anisotropic Reynolds stress tensor, but traditional…

流体动力学 · 物理学 2020-12-02 Rui Fang , David Sondak , Pavlos Protopapas , Sauro Succi

Conventional fluid simulations can be time consuming and energy intensive. We researched the viability of a neural network for simulating incompressible fluids in a randomized obstacle-heavy environment, as an alternative to the numerical…

流体动力学 · 物理学 2025-10-28 Rui Hespanha , Elliot McGuire , João Hespanha

Computational fluid dynamics models based on Reynolds-averaged Navier--Stokes equations with turbulence closures still play important roles in engineering design and analysis. However, the development of turbulence models has been stagnant…

流体动力学 · 物理学 2019-10-04 Heng Xiao , Jin-Long Wu , Sylvain Laizet , Lian Duan

Neural networks have been used to solve different types of large data related problems in many different fields.This project takes a novel approach to solving the Navier-Stokes Equations for turbulence by training a neural network using…

数值分析 · 计算机科学 2018-08-22 Megan McCracken

This work is concerned with the numerical investigation of the dynamics of stopping vortex formation in the uniform flow past a wedge mounted on a wall for channel Reynolds number $Re_c=1560$. The streamfunction-vorticity ($\psi$-$\omega$)…

流体动力学 · 物理学 2024-05-06 Jiten C Kalita

This work builds on and confirms the theoretical findings of Part 1 of this paper, Moarref & Jovanovi\'c (2010). We use direct numerical simulations of the Navier-Stokes equations to assess the efficacy of blowing and suction in the form of…

流体动力学 · 物理学 2011-11-29 Binh K. Lieu , Rashad Moarref , Mihailo R. Jovanović

We present a novel moving immersed boundary method (IBM) and employ it in direct numerical simulations (DNS) of the closed-vessel swirling von Karman flow in laminar and turbulent regimes. The IBM extends direct-forcing approaches by…

流体动力学 · 物理学 2022-02-17 M. Houssem Kasbaoui , Tejas Kulkarni , Fabrizio Bisetti

This paper presents a neural network-based turbulence modeling approach for transonic flows based on the ensemble Kalman method. The approach adopts a tensor basis neural network for the Reynolds stress representation, with modified inputs…

流体动力学 · 物理学 2023-05-12 Yi Liu , Xin-Lei Zhang , Guowei He

In order to achieve a virtual certification process and robust designs for turbomachinery, the uncertainty bounds for Computational Fluid Dynamics have to be known. The formulation of turbulence closure models implies a major source of the…

计算工程、金融与科学 · 计算机科学 2023-04-03 Marcel Matha , Karsten Kucharczyk , Christian Morsbach

In the present paper, an aerodynamic investigation of a high-speed train is performed. In the first section of this article, a generic high-speed train against a turbulent flow is simulated, numerically. The Reynolds-Averaged Navier-Stokes…

A conservative coupled finite difference-boundary element computational procedure for the simulation of turbulent magnetohydrodynamic flow in a straight rectangular duct at finite magnetic Reynolds number is presented. The flow is assumed…

流体动力学 · 物理学 2015-11-05 Vinodh Bandaru , Thomas Boeck , Dmitry Krasnov , Jörg Schumacher

In this essay, we recall the specificities of the transition to turbulence in wall-bounded flows and present recent achievements in the understanding of this problem. The transition is abrupt with laminar-turbulent coexistence over a finite…

流体动力学 · 物理学 2016-04-05 Paul Manneville

Estimation of near-wall turbulence in channel flow from outer observations is investigated using adjoint-variational data assimilation. We first consider fully resolved velocity data, starting at a distance from the wall. By enforcing the…

流体动力学 · 物理学 2025-04-09 Mengze Wang , Tamer A. Zaki

Direct numerical simulations (DNS) of fully-developed turbulent channel flows for very low Reynolds numbers have been performed with a larger computational box sizes than those of existing DNS. The friction Reynolds number was decreased…

流体动力学 · 物理学 2014-09-17 Takahiro Tsukahara , Yohji Seki , Hiroshi Kawamura , Daisuke Tochio

In this work we introduce a well-posed mathematical model for the processes involved in the artificial circulation of water, in order to avoid eutrophication phenomena, for instance, in a lake. This novel and general formulation is based on…

偏微分方程分析 · 数学 2019-02-04 Francisco J. Fernández , Lino J. Álvarez-Vázquez , Aurea Martínez
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