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Strongly nonlinear models of internal wave propagation for incompressible stratified Euler fluids are investigated numerically and analytically to determine the evolution of a class of initial conditions of interest in laboratory…

Fluid Dynamics · Physics 2017-03-28 Shengqian Chen

We present a body-free simulation framework for three-dimensional turbulent cylinder wakes, in which the upstream cylinder is not explicitly resolved. Instead, the incompressible Navier--Stokes equations are solved in a simplified…

This article presents a multi-physics methodology for the numerical simulation of physical systems that involve the non-linear interaction of multi-phase reactive fluids and elastoplastic solids, inducing high strain-rates and high…

Computational Physics · Physics 2021-06-04 Tim Wallis , Philip T. Barton , Nikolaos Nikiforakis

Results of numerical simulations obtained by a staggered finite difference scheme together with an efficient immersed boundary method are presented to understand the effects of the shape of three-dimensional obstacles on the transition of a…

Fluid Dynamics · Physics 2015-03-31 Paolo Orlandi , Sergio Pirozzoli , Matteo Bernardini

As an experimental model to mimic the flow of bio-fluids in the cell and the flow in tiny blood capillaries, we study the co-moving shear flow of dilute polymeric solutions. An inflection point shear flow profile is created by parallel…

Fluid Dynamics · Physics 2025-10-09 Jimreeves David M

Direct numerical simulations have proven of inestimable help to our understanding of the transition to turbulence in wall-bounded flows. While the dynamics of the transition from laminar flow to turbulence via localised spots can be…

Pattern Formation and Solitons · Physics 2014-12-17 Paul Manneville , Joran Rolland

We present simulations of coherent structures in compressible flows near the transition to turbulence using the Dissipative Particle Dynamics (DPD) method. The structures we find are remarkably consistent with experimental observations and…

In this paper we present a new strategy to model the subgrid-scale scalar flux in a three-dimensional turbulent incompressible flow using physics-informed neural networks (NNs). When trained from direct numerical simulation (DNS) data,…

Fluid Dynamics · Physics 2021-03-03 Hugo Frezat , Guillaume Balarac , Julien Le Sommer , Ronan Fablet , Redouane Lguensat

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

This paper explores the application of SPH to a Direct Numerical Simulation (DNS) of decaying turbulence in a two-dimensional no-slip wall-bounded domain. In this bounded domain, the inverse energy cascade, and a net torque exerted by the…

Fluid Dynamics · Physics 2015-03-17 Martin Robinson , Joseph J. Monaghan

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

We investigate three-dimensional turbulence in a stably stratified fluid driven by a vertically sheared Kolmogorov flow using direct numerical simulations of the Boussinesq equations. As stratification increases, mean profiles evolve toward…

Fluid Dynamics · Physics 2025-12-16 Alessandro Sozza , Andrea Maffioli

Simultaneous Particle Image Velocimetry (PIV) measurements of stratified turbulent air/water flow in a horizontal pipe have been performed using water droplets as tracers in the gas-phase. The use of water droplets as tracers ensures that…

Fluid Dynamics · Physics 2017-01-03 A. A. Ayati , J. Kolaas , A. Jensen , G. W. Johnson

Interfacial fluctuations in a two-phase binary fluid mixture reveal signatures of underlying physical processes that occur within each phase and on a range of spatial and temporal scales. In this study, we investigate a model binary fluid…

Fluid Dynamics · Physics 2026-03-04 Samuel Z Khiangte , Triparna Sanyal , Sumantra Sarkar , Nairita Pal

Turbulent flows are fundamental in engineering and the environment, but their chaotic and three-dimensional (3-D) nature makes them computationally expensive to simulate. In this work, a dimensionality reduction technique is investigated to…

Fluid Dynamics · Physics 2020-12-16 Bernat Font

In this work, low Reynolds number turbulent flow through a corrugated channel, formed by standard V-groove riblets, are investigated via direct numerical simulations (DNS). The simulations attempt to assess the variation of flow dynamics…

Fluid Dynamics · Physics 2018-03-21 J. H. Ng , R. K. Jaiman , T. T. Lim

Heated pipe flow is widely used in thermal engineering applications, but the presence of buoyancy force can cause intermittency, or multiple flow states at the same parameter values. Such changes in the flow lead to substantial changes in…

Fluid Dynamics · Physics 2025-01-22 Shijun Chu , Elena Marensi , Ashley P. Willis

We describe a fast direct numerical simulation (DNS) method that promises to directly characterise the hydraulic roughness of any given rough surface, from the hydraulically smooth to the fully rough regime. The method circumvents the…

Fluid Dynamics · Physics 2015-05-28 Daniel Chung , Leon Chan , Michael MacDonald , Nicholas Hutchins , Andrew Ooi

A simple scheme of forcing turbulence away from decay was introduced by Lundgren some time ago, the `linear forcing', which amounts to a force term linear in the velocity field with a constant coefficient. The evolution of linearly forced…

Fluid Dynamics · Physics 2013-05-29 E. Gravanis , E. Akylas

Turbulent fluid flows are among the most computationally demanding problems in science, requiring enormous computational resources that become prohibitive at high flow speeds. Physics-informed neural networks (PINNs) represent a radically…

Machine Learning · Computer Science 2025-10-14 Sifan Wang , Shyam Sankaran , Xiantao Fan , Panos Stinis , Paris Perdikaris
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