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The flow of emulsions in confined microfluidic channels is affected by surface roughness. Directional roughness effects have recently been reported in channels with asymmetric boundary conditions featuring a flat wall, and a wall textured…

Fluid Dynamics · Physics 2024-04-17 Francesca Pelusi , Daniele Filippi , Ladislav Derzsi , Matteo Pierno , Mauro Sbragaglia

Well-resolved direct numerical simulations (DNSs) have been performed of the flow in a smooth circular pipe of radius $R$ and axial length $10\pi R$ at friction Reynolds numbers up to $Re_\tau=5200$. Various turbulence statistics are…

Fluid Dynamics · Physics 2023-02-15 Jie Yao , Saleh Rezaeiravesh , Philipp Schlatter , Fazle Hussain

Using weak wave turbulence theory analysis, we distinguish three main regimes for 2D stratified fluids in the dimensionless parameter space defined by the Froude number and the Reynolds number: discrete wave turbulence, weak wave…

Fluid Dynamics · Physics 2026-03-30 Vincent Labarre , Michal Shavit

In this paper, deep learning (DL) methods are evaluated in the context of turbulent flows. Various generative adversarial networks (GANs) are discussed with respect to their suitability for understanding and modeling turbulence. Wasserstein…

Fluid Dynamics · Physics 2022-10-31 Mathis Bode , Michael Gauding , Jens Henrik Göbbert , Baohao Liao , Jenia Jitsev , Heinz Pitsch

Turbulent flows consist of a wide range of interacting scales. Since the scale range increases as some power of the flow Reynolds number, a faithful simulation of the entire scale range is prohibitively expensive at high Reynolds numbers.…

Fluid Dynamics · Physics 2023-07-24 Dhawal Buaria , Katepalli R. Sreenivasan

Direct Statistical Simulation (DSS) solves the equations of motion for the statistics of turbulent flows in place of the traditional route of accumulating statistics by Direct Numerical Simulation (DNS). That low-order statistics usually…

Fluid Dynamics · Physics 2020-07-15 Altan Allawala , S. M. Tobias , J. B. Marston

We describe tests validating progress made toward acceleration and automation of hydrodynamic codes in the regime of developed turbulence by three Deep Learning (DL) Neural Network (NN) schemes trained on Direct Numerical Simulations of…

Fluid Dynamics · Physics 2018-12-06 Ryan King , Oliver Hennigh , Arvind Mohan , Michael Chertkov

Turbulence is ubiquitous in engineering and science, yet direct simulation is prohibitively expensive. The Reynolds-averaged Navier-Stokes (RANS) equations provide savings exceeding ten orders of magnitude but introduce unclosed terms (the…

Fluid Dynamics · Physics 2026-05-27 Daniel Dehtyriov , Jonathan F. MacArt , Justin Sirignano

Modelling the near-wall region of wall-bounded turbulent flows is a widespread practice to reduce the computational cost of large-eddy simulations (LESs) at high Reynolds number. As a first step towards a data-driven wall-model, a…

Direct numerical simulations (DNSs) of turbulent pipe flow subjected to streamwise-varying wall rotation are performed. This control method is able to achieve drag reduction and even relaminarize the flow under certain control parameters at…

Fluid Dynamics · Physics 2022-11-30 Xu Liu , Hongbo Zhu , Yan Bao , Dai Zhou , Zhaolong Han

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…

Turbulent flows above a solid surface are characterised by a hydrodynamic roughness that represents, for the far velocity field, the typical length scale at which momentum mixing occurs close to the surface. Here, we are theoretically…

Fluid Dynamics · Physics 2023-11-07 Pan Jia , Bruno Andreotti , Philippe Claudin

This study proposes a newly-developed deep-learning-based method to generate turbulent inflow conditions for spatially-developing turbulent boundary layer (TBL) simulations. A combination of a transformer and a multiscale-enhanced…

Fluid Dynamics · Physics 2023-03-22 Mustafa Z. Yousif , Meng Zhang , Linqi Yu , Ricardo Vinuesa , HeeChang Lim

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…

This study constitutes the second phase of a research endeavor aimed at evaluating the feasibility of employing Long Short-Term Memory (LSTM) neural networks as a replacement for Reynolds-Averaged Navier-Stokes (RANS) turbulence models. In…

Fluid Dynamics · Physics 2024-11-19 Hugo D. Pasinato

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

Hydrodynamic flood modeling improves hydrologic and hydraulic prediction of storm events. However, the computationally intensive numerical solutions required for high-resolution hydrodynamics have historically prevented their implementation…

Machine Learning · Computer Science 2023-07-06 Francisco Haces-Garcia , Natalya Maslennikova , Craig L Glennie , Hanadi S Rifai , Vedhus Hoskere , Nima Ekhtari

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 have conducted a direct numerical simulation (DNS) study of dilute turbulent particulate flow in a vertical plane channel, considering thousands of finite-size rigid particles with resolved phase interfaces. The particle diameter…

Fluid Dynamics · Physics 2011-09-01 Markus Uhlmann

The precision, stability, and performance of lightweight high-strength steel structures in heavy machinery is affected by their highly nonlinear dynamics. This, in turn, makes control more difficult, simulation more computationally…

Systems and Control · Electrical Eng. & Systems 2025-03-11 Qasim Khadim , Peter Manzl , Emil Kurvinen , Aki Mikkola , Grzegorz Orzechowski , Johannes Gerstmayr
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