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We developed a new explicit cloud microphysical model, based on direct numerical simulation (DNS) with Lagrangian particle tracking. The model employs a vertically-elongated quasi-1D computational domain extending from the ground to the…

Atmospheric and Oceanic Physics · Physics 2026-04-14 Masaya Iwashima , Ryo Onishi

We present TURB-Lagr, a new open database of 3d turbulent Lagrangian trajectories, obtained by Direct Numerical Simulations (DNS) of the original Navier-Stokes equations in the presence of a homogeneous and isotropic forcing. The aim is to…

Fluid Dynamics · Physics 2024-09-24 Luca Biferale , Fabio Bonaccorso , Michele Buzzicotti , Chiara Calascibetta

In this paper, the results of an international collaborative test case relative to the production of a Direct Numerical Simulation and Lagrangian Particle Tracking database for turbulent particle dispersion in channel flow at low Reynolds…

This article describes some common issues encountered in the use of Direct Numerical Simulation (DNS) turbulent flow data for machine learning. We focus on two specific issues; 1) the requirements for a fair validation set, and 2) the…

Sampling synthetic turbulent fields as a computationally tractable surrogate for direct numerical simulations (DNS) is an important practical problem in various applications, and allows to test our physical understanding of the main…

Fluid Dynamics · Physics 2025-11-19 Timo Schorlepp , Katharina Kormann , Jeremiah Lübke , Tobias Schäfer , Rainer Grauer

We present a Direct Numerical Simulation (DNS) study of buoyancy-driven bubbly flows in the presence of large scale driving that generates turbulence. On increasing the turbulence intensity: (a) the bubble trajectories become more curved,…

Fluid Dynamics · Physics 2023-06-22 Vikash Pandey , Dhrubaditya Mitra , Prasad Perlekar

A priori tests of turbulence models for the compressible Reynolds-Averaged Navier--Stokes (RANS) are performed by using Direct Numerical Simulations (DNS) data of zero-pressure-gradient flat-plate turbulent boundary layers. The DNS database…

Fluid Dynamics · Physics 2023-10-17 Sciacovelli L. , Cannici A. , Passiatore D. , Cinnella P

Two direct numerical simulation (DNS) databases are investigated to understand the effect of the outer-layer turbulence on the inner layer's structures and energy transfer mechanisms. The first DNS database is the non-equilibrium…

Fluid Dynamics · Physics 2024-07-25 Taygun R. Gungor , Yvan Maciel , Ayse G. Gungor

Simulating turbulence is critical for many societally important applications in aerospace engineering, environmental science, the energy industry, and biomedicine. Large eddy simulation (LES) has been widely used as an alternative to direct…

Fluid Dynamics · Physics 2023-12-13 Shengyu Chen , Tianshu Bao , Peyman Givi , Can Zheng , Xiaowei Jia

A fully-resolved direct-numerical-simulation (DNS) approach for investigating flexible bodies forced by a turbulent incoming flow is designed to study the flapping motion of a flexible flag at moderate Reynolds number. The incoming…

Fluid Dynamics · Physics 2021-08-25 Stefano Olivieri , Francesco Viola , Andrea Mazzino , Marco E. Rosti

We analyze the statistics of turbulent velocity fluctuations in the time domain. Three cases are computed numerically and compared: (i) the time traces of Lagrangian fluid particles in a (3D) turbulent flow (referred to as the "dynamic"…

Statistical Mechanics · Physics 2007-05-23 L. Chevillard , S. G. Roux , E. Leveque , N. Mordant , J. -F. Pinton , A. Arneodo

A technique for measuring the mean impulse response function of stationary homogeneous isotropic turbulence is proposed. Such measurement is carried out here on the basis of Direct Numerical Simulation (DNS). A zero-mean white-noise volume…

Fluid Dynamics · Physics 2015-05-20 Marco Carini , Maurizio Quadrio

Small-scale turbulence can be comprehensively described in terms of velocity gradients, which makes them an appealing starting point for low-dimensional modeling. Typical models consist of stochastic equations based on closures for…

Fluid Dynamics · Physics 2024-03-01 Maurizio Carbone , Vincent J. Peterhans , Alexander S. Ecker , Michael Wilczek

Lagrangian turbulence lies at the core of numerous applied and fundamental problems related to the physics of dispersion and mixing in engineering, bio-fluids, atmosphere, oceans, and astrophysics. Despite exceptional theoretical,…

The direct numerical simulation (DNS) of the Taylor--Couette flow in the fully turbulent regime is described. The numerical method extends the work by Quadrio & Luchini (Eur. J. Mech. B / Fluids, v.21, pp.413--427, 2002), and is based on a…

Fluid Dynamics · Physics 2009-11-13 Davide Pirro , Maurizio Quadrio

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

Homogeneous and isotropic turbulent fields obtained from two DNS databases (with $\mbox{Re}_\lambda$ equal to 150 and 418) were seeded with point particles that moved with the local fluid velocity to obtain Lagrangian pressure histories.…

Fluid Dynamics · Physics 2020-01-29 Mehedi Bappy , Pablo M. Carrica , Gustavo C. Buscaglia

This review focuses on Direct numerical simulations (DNS) of turbulent flows laden with droplets or bubbles. DNS of these flows are more challenging than those of flows laden with solid particles due to the surface deformation in the…

Fluid Dynamics · Physics 2018-04-10 Said Elghobashi

Direct numerical simulation of turbulence at realistic Reynolds numbers is still beyond current computational capability, necessitating models that reduce the number of resolved spatial scales. Motivated by phenomenology and recent…

Direct numerical simulations (DNS) are an indispensable tool for understanding the fundamental physics of turbulent flows. Because of their steep increase in computational cost with Reynolds number ($R_{\lambda}$), well-resolved DNS are…

Computational Physics · Physics 2020-08-26 Komal Kumari , Diego A. Donzis
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