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Accurate simulation of turbulent flows is fundamental to scientific and engineering applications. Direct numerical simulation (DNS) offers the highest fidelity but is computationally prohibitive, while existing data-driven alternatives…

Fluid Dynamics · Physics 2026-02-12 Yilong Dai , Shengyu Chen , Xiaowei Jia , Peyman Givi , Runlong Yu

The Reynolds-Averaged Navier-Stokes (RANS) approach remains a backbone for turbulence modeling due to its high cost-effectiveness. Its accuracy is largely based on a reliable Reynolds stress anisotropy tensor closure model. There has been…

The paper is devoted to two-phase flow simulations and investigates the ability of a diffusive interface Cahn-Hilliard Volume-of-Fluid model to capture the dynamics of the air-sea interface at geophysically relevant Reynolds numbers. It…

Pore-resolved direct numerical simulations (DNS) are performed to investigate the interactions between streamflow turbulence and groundwater flow through a randomly packed porous sediment bed for three permeability Reynolds numbers, $Re_K$,…

Fluid Dynamics · Physics 2025-07-22 Shashank K. Karra , Sourabh V. Apte , Xiaoliang He , Timothy Scheibe

We investigate rough-wall turbulent flows through direct numerical simulations of flow over three-dimensional transitionally rough sinusoidal surfaces. The roughness Reynolds number is fixed at $k^+=10$, where $k$ is the sinusoidal…

Fluid Dynamics · Physics 2020-12-09 M. MacDonald , L. Chan , D. Chung , N. Hutchins , A. Ooi

Numerical simulations can follow the evolution of fluid motions through the intricacies of developed turbulence. However, they are rather costly to run, especially in 3D. In the past two decades, generative models have emerged which produce…

The influence of near-wall density and viscosity gradients on near-wall turbulence in a channel are studied by means of Direct Numerical Simulation (DNS) of the low-Mach number approximation of the Navier--Stokes equations. Different…

Fluid Dynamics · Physics 2023-02-17 Ashish Patel , Bendiks Jan Boersma , Rene Pecnik

Generalizability of machine-learning (ML) based turbulence closures to accurately predict unseen practical flows remains an important challenge. At the Reynolds-averaged Navier-Stokes (RANS) level, NN-based turbulence closure modeling is…

Fluid Dynamics · Physics 2021-12-15 Salar Taghizadeh , Freddie Witherden , Yassin Hassan , Sharath Girimaji

Direct numerical simulations are used to investigate the individual dynamics of large spherical particles suspended in a developed homogeneous turbulent flow. A definition of the direction of the particle motion relative to the surrounding…

Fluid Dynamics · Physics 2015-06-16 Mamadou Cisse , Holger Homann , Jeremie Bec

Simulating spatiotemporal turbulence with high fidelity remains a cornerstone challenge in computational fluid dynamics (CFD) due to its intricate multiscale nature and prohibitive computational demands. Traditional approaches typically…

Fluid Dynamics · Physics 2024-07-01 Xiantao Fan , Deepak Akhare , Jian-Xun Wang

High-order methods and hybrid turbulence models have independently shown promise as means of decreasing the computational cost of scale-resolving simulations. The objective of this work is to develop the combination of these methods and…

Fluid Dynamics · Physics 2022-01-26 Tarik Dzanic , Sharath Girimaji , Freddie Witherden

This work introduces a novel data-driven framework to formulate explicit algebraic Reynolds-averaged Navier-Stokes (RANS) turbulence closures. Recent years have witnessed a blossom in applying machine learning (ML) methods to revolutionize…

Fluid Dynamics · Physics 2023-01-24 Hongwei Tang , Yan Wang , Tongguang Wang , Linlin Tian

The transitional and well-developed regimes of turbulent shear flows exhibit a variety of remarkable scaling laws that are only now beginning to be systematically studied and understood. In the first part of this article, we summarize…

Fluid Dynamics · Physics 2017-01-04 Nigel Goldenfeld , Hong-Yan Shih

Reynolds-averaged Navier--Stokes (RANS) simulations with turbulence closure models continue to play important roles in industrial flow simulations. However, the commonly used linear eddy viscosity models are intrinsically unable to handle…

Fluid Dynamics · Physics 2019-05-22 Jin-Long Wu , Heng Xiao , Rui Sun , Qiqi Wang

A novel approach to wall modeling for the incompressible Navier-Stokes equations including flows of moderate and large Reynolds numbers is presented. The basic idea is that a problem-tailored function space allows prediction of turbulent…

Fluid Dynamics · Physics 2016-04-18 Benjamin Krank , Wolfgang A. Wall

The analysis of the Reynolds Stress Transport Equation (RSTE) provides fundamental physical insights that are essential for the development and validation of advanced turbulence models. However, a comprehensive and validated tool for…

Fluid Dynamics · Physics 2025-11-07 Mario J. Rincón , Christoffer Hansen , Martino Reclari , Mahdi Abkar

We address the Reynolds-number dependence of the turbulent skin-friction drag reduction induced by streamwise-travelling waves of spanwise wall oscillations. The study relies on direct numerical simulations of drag-reduced flows in a plane…

A direct numerical simulation (DNS) of a channel flow with one curved surface was performed at moderate Reynolds number (Re_tau = 395 at the inlet). The adverse pressure gradient was obtained by a wall curvature through a mathematical…

Fluid Dynamics · Physics 2017-11-22 Matthieu Marquillie , Jean-Philippe Laval , Rostislav Dolganov

In wall-modeled large-eddy simulations (WMLES), the near-wall model plays a significant role in predicting the skin friction, although the majority of the boundary layer is resolved by the outer large-eddy simulation (LES) solver. In this…

Fluid Dynamics · Physics 2020-10-09 Kevin Patrick Griffin , Lin Fu

Fully-developed incompressible Navier-Stokes turbulence in three dimensions is a dissipative dynamical system that exhibits strong departure from absolute equilibrium. Nevertheless, several kinds of representation by Tsallis equilibria have…

Chaotic Dynamics · Physics 2009-11-10 Toshiyuki Gotoh , Robert H. Kraichnan
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