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This paper reviews results from the study of wall-bounded turbulent flows using statistical state dynamics (SSD) that demonstrate the benefits of adopting this perspective for understanding turbulence in wall-bounded shear flows. The SSD…

Fluid Dynamics · Physics 2017-02-10 Brian F. Farrell , Dennice F. Gayme , Petros J. Ioannou

Scaling laws and intermittency in the wall region of a turbulent flow are addressed by analyzing moderate Reynolds number data obtained by single component hot wire anemometry in the boundary layer of a flat plate. The paper aims in…

Chaotic Dynamics · Physics 2009-11-07 B. Jacob , A. Olivieri , C. M. Casciola

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

Identification of coherent structures is an essential step to describe and model turbulence generation mechanisms in wall-bounded flows. To this end we present a clustering method based on Latent Dirichlet Allocation (LDA), a generative…

Fluid Dynamics · Physics 2021-07-07 Mohamed Frihat , Bérengère Podvin , Lionel Mathelin , Yann Fraigneau , François Yvon

Collective response dynamics of a strongly coupled system departs from the continuum phase upon transition to the quasicrystalline phase, or formation of a Wigner lattice. The wave nonlinearity leading to the modulational instability in…

Plasma Physics · Physics 2023-01-25 Sandip Dalui , Prince Kumar , Devendra Sharma

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…

Fluid Dynamics · Physics 2023-11-28 Xu Chu , Sandeep Pandey

In the present paper a new data-driven model is proposed to close and increase accuracy of RANS equations. The divergence of the Reynolds Stress Tensor (RST) is obtained through a Neural Network (NN) whose architecture and input choice…

Fluid Dynamics · Physics 2022-10-19 Stefano Berrone , Davide Oberto

We conducted direct numerical simulations (DNSs) of turbulent flow over three-dimensional sinusoidal roughness in a channel. A passive scalar is present in the flow with Prandtl number $Pr=0.7$, to study heat transfer by forced convection…

Fluid Dynamics · Physics 2019-02-20 M. MacDonald , N. Hutchins , D. Chung

Wall-pressure fluctuations are a practically robust input for real-time control systems aimed at modifying wall-bounded turbulence. The scaling behaviour of the wall-pressure--velocity coupling requires investigation to properly design a…

Fluid Dynamics · Physics 2024-01-11 Woutijn J. Baars , Giulio Dacome , Myoungkyu Lee

We study the behaviour of the streamwise velocity variance in turbulent wall-bounded flows using a DNS database of pipe flow up to $Re_{\tau} \approx 12000$. The analysis of the spanwise spectra in the viscous near-wall region strongly…

Fluid Dynamics · Physics 2024-11-20 Sergio Pirozzoli

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 parameterize sub-grid scale (SGS) fluxes in sinusoidally forced two-dimensional turbulence on the $\beta$-plane at high Reynolds numbers (Re$\sim$25000) using simple 2-layer Convolutional Neural Networks (CNN) having only…

Fluid Dynamics · Physics 2023-04-12 Kaushik Srinivasan , Mickael D. Chekroun , James C. McWilliams

For settling the problem with rotating turbulence modelling, a quasi-dynamic one-equation subgrid-scale (SGS) model is proposed in this paper. Considering the key role of the joint cascade of kinetic energy and helicity in rotating…

Fluid Dynamics · Physics 2024-01-05 Depei Song , Changping Yu , Zheng Yan , Xinliang Li

Data-driven methods have demonstrated strong predictive capabilities in fluid mechanics, yet most current applications still focus on simplified configurations, often characterised by statistical stationarity or limited temporal…

Fluid Dynamics · Physics 2025-11-21 Miguel M. Valero , Marcello Meldi

We formulate a data-driven, physics-constrained closure method for coarse-scale numerical simulations of turbulent fluid flows. Our approach involves a closure scheme that is non-local both in space and time, i.e. the closure terms are…

Fluid Dynamics · Physics 2021-02-16 Alexis-Tzianni G. Charalampopoulos , Themistoklis P. Sapsis

Particle-laden flows are simulated at various scales using numerical techniques that range from particle-resolved Direct Numerical Simulations (pr-DNS) for small-scale systems to Lagrange point-particle methods for laboratory-scale…

Fluid Dynamics · Physics 2025-07-29 Alexander Metelkin , Sam Jacob Jacob , Bernhard Vowinckel

This study investigates chaotic diffusion in multi-scale turbulence driven by nonlinear wave-particle resonance coupling. Turbulent waves with distinct characteristic wavelengths across scales coherently interact with charged particles when…

Plasma Physics · Physics 2025-04-22 Yueheng Huang , Nong Xiang , Jiale Chen , Zong Xu

Using the flow governing equation approach to similarity, Weyburne (D. Weyburne, arXiv:1701.02364, 2016) recently showed that for 2-D turbulent boundary layer flows, the Prandtl Plus scalings are NOT, in general, the proper similarity…

Fluid Dynamics · Physics 2019-11-19 David W. Weyburne

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

The success of recurrent neural networks (RNNs) has been demonstrated in many applications related to turbulence, including flow control, optimization, turbulent features reproduction as well as turbulence prediction and modeling. With this…

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