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A large eddy simulation (LES) with an extended Smagorinsky model has been carried to investigate numerically the fully developed turbulent flow of a shear thinning fluid (n=0.75) in a stationary pipe at a simulation's Reynolds number equals…

Fluid Dynamics · Physics 2020-06-01 Mohamed Abdi , Meryem Ould-Rouiss , Abdelkader Noureddine

The theory of turbulent diffusion of chemically reacting gaseous admixtures developed previously (Phys. Rev. E {\bf 90}, 053001, 2014) is generalized for large yet finite Reynolds numbers, and the dependence of turbulent diffusion…

Fluid Dynamics · Physics 2018-05-24 T. Elperin , N. Kleeorin , M. Liberman , A. Lipatnikov , I. Rogachevskii , R. Yu

In the current study, model expressions for fifth-order velocity moments obtained from the truncated Gram-Charlier series expansions model for a turbulent flow field probability density function are validated using data from direct…

Fluid Dynamics · Physics 2021-02-24 S. V. Poroseva , S. M. Murman

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

We use parsimonious diffusion maps (PDMs) to discover the latent dynamics of high-fidelity Navier-Stokes simulations with a focus on the 2D fluidic pinball problem. By varying the Reynolds number, different flow regimes emerge, ranging from…

Fluid Dynamics · Physics 2024-11-05 Alessandro Della Pia , Dimitris Patsatzis , Lucia Russo , Constantinos Siettos

We perform direct numerical simulations (DNS) of a turbulent channel flow over porous walls. In the fluid region the flow is governed by the incompressible Navier--Stokes (NS) equations, while in the porous layers the Volume-Averaged…

Fluid Dynamics · Physics 2023-07-19 Marco E. Rosti , Luca Cortelezzi , Maurizio Quadrio

The fast and accurate prediction of unsteady flow becomes a serious challenge in fluid dynamics, due to the high-dimensional and nonlinear characteristics. A novel hybrid deep neural network (DNN) architecture was designed to capture the…

Fluid Dynamics · Physics 2020-01-08 Renkun Han , Yixing Wang , Yang Zhang , Gang Chen

Flow past a high-lift low-pressure turbine (LPT) blade in a cascade could be quite complex as phenomena like separation and transition are often involved. For a highly loadedT106A blade at a high incidence and relatively low Reynolds…

Fluid Dynamics · Physics 2020-04-24 Rajesh Ranjan , S. M. Deshpande , Roddam Narasimha

Large-eddy simulation of incompressible turbulent flow has been extensively investigated; hence, a variety of models suited for different numerical schemes have been developed. In the case of compressible flow, the modeling is more…

Fluid Dynamics · Physics 2020-07-15 Ahmad Peyvan , Zia Ghiasi , Dongru Li , Jonathan Komperda , Farzad Mashayek

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

The design of advanced nuclear reactors (Gen IV) involves an array of challenging fluid-flow issues that affect its safety and performance. The calculated DNS database will be instrumental in understanding the flow behavior in the…

Fluid Dynamics · Physics 2023-04-11 Tri Nguyen , Elia Merzari

Computational fluid dynamics (CFD) is a useful tool for prediction of turbulence in aerodynamic and biomedical applications. The choice of appropriate turbulence models is key to reaching accurate predictions. The present investigation…

Fluid Dynamics · Physics 2018-03-13 Fardin Khalili , Peshala P. T. Gamage , Hansen A. Mansy

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…

Fluid Dynamics · Physics 2015-11-05 Vinodh Bandaru , Thomas Boeck , Dmitry Krasnov , Jörg Schumacher

Microvortex generators are passive control devices smaller than the boundary layer thickness that energise the boundary layer to prevent flow separation with limited induced drag. In this work, we use direct numerical simulations (DNSs) to…

We present a direct numerical simulation (DNS) study of pseudo-turbulence in buoyancy driven bubbly flows for a range of Reynolds ($\Rey$) and Atwood ($\At$) numbers. We study the probability distribution function of the horizontal and…

Fluid Dynamics · Physics 2020-02-19 Vikash Pandey , Rashmi Ramadugu , Prasad Perlekar

We present direct numerical simulations (DNSs) of bypass transition over a flat plate with inlet freestream turbulence intensity levels of 0.75%, 1.5%, 2.25%, 3.0%, and 6.0%, respectively. A new definition of the transition intermittency is…

Fluid Dynamics · Physics 2025-02-28 Carlos A. Gonzalez , Rahul Agrawal , Xiaohua Wu

The turbulent boundary layer over a flat plate is computed by direct numerical simulation (DNS) of the incompressible Navier-Stokes equations as a test bed for a synthetic turbulence generator (STG) inflow boundary condition. The inlet…

Fluid Dynamics · Physics 2021-02-15 James R. Wright , Riccardo Balin , John W. Patterson , John A. Evans , Kenneth E. Jansen

We study turbulent flows in pressure-driven ducts with square cross-section through direct numerical simulation in a wide enough range of Reynolds number to reach flow conditions which are representative of fully developed turbulence.…

Fluid Dynamics · Physics 2018-03-14 S. Pirozzoli , D. Modesti , P. Orlandi , F. Grasso

It has previously been shown that by increasing the Reynolds number across a channel by spatially varying the viscosity does not cause an immediate change in the size of turbulent structures and a delay is in fact observed in both wall…

Fluid Dynamics · Physics 2021-06-11 Victor Coppo Leite , Elia Merzari

Physics-informed neural networks (PINNs) provide a mesh-free framework for solving partial differential equations by embedding governing physics into neural-network training. Recent studies have shown that parameterized PINNs can learn…

Fluid Dynamics · Physics 2026-05-29 A. Jangir , R. Clements , R. Goyal , G. Tabor