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Turbulent flow has been extensively studied using computational fluid dynamics (CFD) simulations since turbulent flow regime is so frequently encountered in both academic and engineering applications. The high-fidelity simulation of the…

Fluid Dynamics · Physics 2024-05-21 Minghan Chu

Simulation of turbulent flows, especially at the edges of clouds in the atmosphere, is an inherently challenging task. Hitherto, the best possible computational method to perform such experiments is the Direct Numerical Simulation (DNS).…

Fluid Dynamics · Physics 2022-08-19 Moumita Bhowmik , Manmeet Singh , Suryachandra Rao , Souvik Paul

Active grids operated with random protocols are a standard way to generate large Reynolds number turbulence in wind and water tunnels. But anomalies in the decay and third-order scaling of active-grid turbulence have been reported. We…

The drag on a golf ball can be reduced by dimpling the surface. There have been few studies, primarily experimental, that provide quantitative information on the details of the drag reduction mechanisms. To illuminate the underlying…

Fluid Dynamics · Physics 2008-11-06 Clinton Smith , Nikolaos Beratlis , Elias Balaras , Kyle Squires , Masaya Tsunoda

We present a novel moving immersed boundary method (IBM) and employ it in direct numerical simulations (DNS) of the closed-vessel swirling von Karman flow in laminar and turbulent regimes. The IBM extends direct-forcing approaches by…

Fluid Dynamics · Physics 2022-02-17 M. Houssem Kasbaoui , Tejas Kulkarni , Fabrizio Bisetti

The object of this paper is to study the influence of dispersed micrometer size particles on turbulent heat transfer mechanisms in wall-bounded flows. The strategic target of the current research is to set up a methodology to size and…

Fluid Dynamics · Physics 2008-02-11 Francesco Zonta , Cristian Marchioli , Alfredo Soldati

There exists continuous demand of improved turbulence models for the closure of Reynolds Averaged Navier-Stokes (RANS) simulations. Machine Learning (ML) offers effective tools for establishing advanced empirical Reynolds stress closures on…

Fluid Dynamics · Physics 2021-04-01 Muyuan Liu , Yiren Yang , Hao Chen

Fractal grids generate turbulence by exciting many length scales of different sizes simultaneously rather than using the nonlinear cascade mechanism to obtain multi-scale structures, as it is the case for regular grids. The interest in…

Fluid Dynamics · Physics 2022-10-13 André Fuchs , Wided Medjroubi , Hannes Hochstein , Gerd Gülker , Joachim Peinke

We carry out a direct numerical simulation (DNS) study that reveals the effects of polymers on statistically steady, forced, homogeneous, isotropic fluid turbulence. We find clear manifestations of dissipation-reduction phenomena: On the…

Fluid Dynamics · Physics 2011-02-01 Prasad Perlekar , Dhrubaditya Mitra , Rahul Pandit

Direct numerical simulation (DNS) of turbulent flows is computationally expensive and cannot be applied to flows with large Reynolds numbers. Large eddy simulation (LES) is an alternative that is computationally less demanding, but is…

Fluid Dynamics · Physics 2021-09-09 Shengyu Chen , Shervin Sammak , Peyman Givi , Joseph P. Yurko1 , Xiaowei Jia

Direct numerical simulations are used to investigate the individual dynamics of large spherical particles suspended in a developed homogeneous turbulent flow. A definition of the direction of the particle motion relative to the surrounding…

Fluid Dynamics · Physics 2015-06-16 Mamadou Cisse , Holger Homann , Jeremie Bec

This paper introduces open-source computational fluid dynamics software named open computational fluid dynamic code for scientific computation with graphics processing unit (GPU) system (OpenCFD-SCU), developed by the authors for direct…

Fluid Dynamics · Physics 2022-12-21 Guanlin Dang , Shiwei Liu , Tongbiao Guo , Junyi Duan , Xinliang Li

This paper addresses the integral energy fluxes in natural and controlled turbulent channel flows, where active skin-friction drag reduction techniques allow a more efficient use of the available power. We study whether the increased…

Direct numerical simulations (DNS) stand out as formidable tools in studying turbulent flows. Despite the fact that the achievable Reynolds number remains lower than those available through experimental methods, DNS offers a distinct…

Fluid Dynamics · Physics 2024-06-06 Sergio Hoyas , Ricardo Vinuesa , Peter Schmid , Hassan Nagib

The impact of turbulent fluctuations on the forces exerted by a fluid on a towed spherical particle is investigated by means of high-resolution direct numerical simulations. The measurements are carried out using a novel scheme to integrate…

Fluid Dynamics · Physics 2016-11-21 Holger Homann , Jérémie Bec , Rainer Grauer

We study the dynamics of a flexible fiber freely moving in a three-dimensional fully-developed turbulent field and present a phenomenological theory to describe the interaction between the fiber elasticity and the turbulent flow. This…

Fluid Dynamics · Physics 2018-08-01 Marco Edoardo Rosti , Arash Alizad Banaei , Luca Brandt , Andrea Mazzino

We leverage physics-embedded differentiable graph network simulators (GNS) to accelerate particulate and fluid simulations to solve forward and inverse problems. GNS represents the domain as a graph with particles as nodes and learned…

Geophysics · Physics 2023-09-26 Krishna Kumar , Yongjin Choi

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

What do the ocean surface and a swaying flag have in common? Both are deformable surfaces exhibiting chaotic motion when exposed to turbulent flows. Whether such motion is primarily driven by flow turbulence or by nonlinear dynamics…

Fluid Dynamics · Physics 2026-05-25 Giulio Foggi Rota , Andrea Mazzino , Marco Edoardo Rosti

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