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We study the global, i.e. radially averaged, high Reynolds number (asymptotic) scaling of streamwise turbulence intensity squared defined as ${I^2=\overline{u^2}/U^2}$, where $u$ and $U$ are the fluctuating and mean velocities, respectively…

Fluid Dynamics · Physics 2021-06-29 Nils T. Basse

In recent years, machine learning methods represented by deep neural networks (DNN) have been a new paradigm of turbulence modeling. However, in the scenario of high Reynolds numbers, there are still some bottlenecks, including the lack of…

Fluid Dynamics · Physics 2022-11-02 Z. Y. Wang , W. W. Zhang

The PANS equations model is used to predict the transitional Taylor-Green vortex (TGV) flow at Re=3000. A new form of the closure is proposed, in which the PANS procedure is applied to a variant of the BHR turbulence model. The TGV is a…

The ultimate goal of a sound theory of turbulence in fluids is to close in a rational way the Reynolds equations, namely to express the time averaged turbulent stress tensor as a function of the time averaged velocity field. This closure…

Fluid Dynamics · Physics 2021-05-18 Yves Pomeau , Martine Le Berre

Tensor network algorithms can efficiently simulate complex quantum many-body systems by utilizing knowledge of their structure and entanglement. These methodologies have been adapted recently for solving the Navier-Stokes equations, which…

The numerical modeling of hydraulic jumps remains challenging due to complex interactions among free-surface deformation, air entrainment and detrainment, and turbulent bubble transport. Whereas accurate prediction of these flows is…

Fluid Dynamics · Physics 2026-05-08 L. D'Angelo , F. Zabaleta , G. E. Spadari , P. Consol-Lizzi , F. A. Bombardelli

We study streamwise turbulence intensity definitions using smooth- and rough-wall pipe flow measurements made in the Princeton Superpipe. Scaling of turbulence intensity with the bulk (and friction) Reynolds number is provided for the…

Fluid Dynamics · Physics 2019-11-15 Nils T. Basse

Turbulence enhances the wall shear stress in boundary layers, significantly increasing the drag on streamlined bodies. Other flow features such as freestream pressure gradients and streamwise boundary layer growth also strongly influence…

Fluid Dynamics · Physics 2025-10-10 Ahmed Elnahhas , Perry L. Johnson

We performed a series of three-dimensional numerical simulations of supersonic homogeneous Euler turbulence with adaptive mesh refinement (AMR) and effective grid resolution up to 1024^3 zones. Our experiments describe non-magnetized driven…

Astrophysics · Physics 2016-08-30 Alexei G. Kritsuk , Michael L. Norman , Paolo Padoan

Its is a well known fact that Turbulence exhibits non-locality, however, modeling has largely received local treatment following the work of Prandl over mixing-length model. Thus, in this article we report our findings by formulating a…

Fluid Dynamics · Physics 2021-05-11 Pavan Pranjivan Mehta

A recent thrust in turbulence closure modeling research is to incorporate machine learning (ML) elements, such as neural networks, for the purpose of enhancing the predictive capability to a broader class of flows. Such a turbulence closure…

Fluid Dynamics · Physics 2021-01-19 Salar Taghizadeh , Freddie D. Witherden , Sharath S. Girimaji

Direct numerical simulations, performed with a high-order spectral-element method, are used to study coherent structures in turbulent pipe flow at friction Reynolds numbers $Re_{\tau} = 180$ and $550$. The database was analysed using…

Fluid Dynamics · Physics 2023-07-19 Leandra Abreu , André Cavalieri , Philipp Schlatter , Ricardo Vinuesa , Dan Henningson

Recently, Kami\'nski et al. [1] demonstrated that a two-dimensional streamwise waviness with carefully selected amplitude and period can be effectively used in postponement of a flow separation at high Reynolds number which is out of reach…

Fluid Dynamics · Physics 2025-05-29 Piotr Kamiński , Artur Tyliszczak , Witold Elsner , Paweł Niegodajew

In this article we examine channel flow subject to spatially varying viscosity in the streamwise direction. The Reynolds number is imposed locally with three different ramps. The setup is reminiscent of transient channel flow, but with a…

Fluid Dynamics · Physics 2020-06-11 Victor Coppo Leite , Elia Merzari

Data-driven methods for improving turbulence modeling in Reynolds-Averaged Navier-Stokes (RANS) simulations have gained significant interest in the computational fluid dynamics community. Modern machine learning algorithms have opened up a…

Fluid Dynamics · Physics 2019-02-05 Nicholas Geneva , Nicholas Zabaras

This work extends the framework of the partially-averaged Navier-Stokes (PANS) equations to variable-density flow, \text{i.e.}, multi-material and/or compressible mixing problems with density variations and production of turbulence kinetic…

Fluid Dynamics · Physics 2021-08-11 F. S. Pereira , F. F. Grinstein , D. M. Israel , R. Rauenzahn , S. S. Girimaji

We present a direct comparison between interface-resolved and one-way-coupled point-particle direct numerical simulations (DNS) of gravity-free turbulent channel flow laden with small inertial particles, with high particle-to-fluid density…

Fluid Dynamics · Physics 2024-07-26 Pedro Costa , Luca Brandt , Francesco Picano

The partially-averaged Navier-Stokes (PANS) equations are used to predict the variable-density Rayleigh-Taylor (RT) flow at Atwood number 0.5 and maximum Reynolds number $500$. This is a prototypical problem of material mixing featuring…

A Direct Numerical Simulation (DNS) of the incompressible flow around a rectangular cylinder with chord-to-thickness ratio 5:1 (also known as the BARC benchmark) is presented. The work replicates the first DNS of this kind recently…

Fluid Dynamics · Physics 2021-05-04 Alessandro Chiarini , Maurizio Quadrio Politecnico di Milano

Assessing turbulence control effects for wall friction numerically is a significant challenge since it requires expensive simulations of turbulent fluid dynamics. We instead propose an efficient deep reinforcement learning (RL) framework…

Machine Learning · Computer Science 2025-10-07 Zelin Zhao , Zongyi Li , Kimia Hassibi , Kamyar Azizzadenesheli , Junchi Yan , H. Jane Bae , Di Zhou , Anima Anandkumar