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The Lagrangian approach is natural to study issues of turbulent dispersion and mixing. We propose in this work a general Lagrangian stochastic model including velocity and acceleration as dynamical variables for inhomogeneous turbulent…

Fluid Dynamics · Physics 2020-05-01 Alessio Innocenti , Nicolas Mordant , Nick Stelzenmuller , Sergio Chibbaro

The purpose of this paper is to examine the Lagrangian stochastic modeling of the fluid velocity seen by inertial particles in a nonhomogeneous turbulent flow. A new Langevin-type model, compatible with the transport equation of the drift…

Fluid Dynamics · Physics 2009-07-01 Boris Arcen , Anne Tanière

We propose a new model of turbulence for use in large-eddy simulations (LES). The turbulent force, represented here by the turbulent Lamb vector, is divided in two contributions. The contribution including only subfilter fields is…

Fluid Dynamics · Physics 2009-11-11 Jean-Philippe Laval , Berengere Dubrulle

In this work, we discuss some points relevant for stochastic modelling of one- and two-phase turbulent flows. In the framework of stochastic modelling, also referred to PDF approach, we propose a new Langevin model including all viscosity…

Fluid Dynamics · Physics 2010-09-14 Sergio Chibbaro , Jean-Pierre Minier

The random intensity of noise approach to 1D Laval-Dubrulle-Nazarenko model is used to describe Lagrangian acceleration of a fluid particle in developed turbulence. Intensities of noises entering nonlinear Langevin equation are assumed to…

Statistical Mechanics · Physics 2007-05-23 A. K. Aringazin , M. I. Mazhitov

Recent advances in data-driven modeling have shown that diffusion models can successfully generate synthetic Lagrangian trajectories in turbulent flows. Building on this progress, we extend the method to the joint generation of pairs of…

The local statistical and geometric structure of three-dimensional turbulent flow can be described by properties of the velocity gradient tensor. A stochastic model is developed for the Lagrangian time evolution of this tensor, in which the…

Statistical Mechanics · Physics 2007-05-23 L. Chevillard , C. Meneveau

Direct estimation of Lagrangian turbulence statistics is essential for the proper modeling of dispersion and transport in highly obstructed canopy flows. However, Lagrangian flow measurements demand very high rates of data acquisition,…

Fluid Dynamics · Physics 2019-05-16 Ron Shnapp , Erez Shapira , David Peri , Yardena Bohbot-Raviv , Eyal Fattal , Alex Liberzon

Modeling statistical properties of motion of a Lagrangian particle advected by a high-Reynolds-number flow is of much practical interest and complement traditional studies of turbulence made in Eulerian framework. The strong and nonlocal…

Statistical Mechanics · Physics 2007-05-23 A. K. Aringazin , M. I. Mazhitov

We make a comparative analysis of some recent one-dimensional Langevin models of the acceleration of a Lagrangian fluid particle in developed turbulent flow. The class of models characterized by random intensities of noises (RIN models)…

Statistical Mechanics · Physics 2007-05-23 A. K. Aringazin , M. I. Mazhitov

It is well known that the fluid-particle acceleration is intimately related to the dissipation rate of turbulence, in line with the Kolmogorov assumptions. On the other hand, various experimental and numerical works have reported as well…

Fluid Dynamics · Physics 2022-08-22 Rémi Zamansky

We develop a stochastic model for the velocity gradients dynamics along a Lagrangian trajectory. Comparing with different attempts proposed in the literature, the present model, at the cost of introducing a free parameter known in…

Fluid Dynamics · Physics 2018-02-23 Rodrigo M. Pereira , Luca Moriconi , Laurent Chevillard

In probability density function (PDF) methods a transport equation is solved numerically to compute the time and space dependent probability distribution of several flow variables in a turbulent flow. The joint PDF of the velocity…

Fluid Dynamics · Physics 2010-06-04 J. Bakosi , P. Franzese , Z. Boybeyi

We propose an alternative interpretation of Markovian transport models based on the well-mixedness condition, in terms of the properties of a random velocity field with second order structure functions scaling linearly in the space time…

Chaotic Dynamics · Physics 2009-11-10 Piero Olla , Paolo Paradisi

Inertial particles in turbulent flows are characterised by preferential concentration and segregation and, at sufficient mass loading, dense particle clusters may spontaneously arise due to momentum coupling between the phases. These…

Fluid Dynamics · Physics 2019-01-30 Alessio Innocenti , Rodney O Fox , Sergio Chibbaro

The statistics of the velocity gradient tensor in turbulent flows are of both theoretical and practical importance. The Lagrangian view provides a privileged perspective for studying the dynamics of turbulence in general, and of the…

Fluid Dynamics · Physics 2016-09-21 Perry L. Johnson , Charles Meneveau

We develop a stochastic model for Lagrangian velocity as it is observed in experimental and numerical fully developed turbulent flows. We define it as the unique statistically stationary solution of a causal dynamics, given by a stochastic…

Predicting particle-laden flows requires accurate fluid force models. However, a reliable particle force model for finite-size particles in turbulent flows remains lacking. In the present work, a fluid force model for a finite-size…

Fluid Dynamics · Physics 2025-05-29 Yuqi Wang , Ruifeng Hu

The aim of the paper is to discuss the main characteristics of a complete theoretical and numerical model for turbulent polydispersed two-phase flows, pointing out some specific issues. The theoretical details of the model have already been…

Fluid Dynamics · Physics 2015-05-19 J. P. Minier , E. Peirano , S. Chibbaro

Turbulence structure resulting from multi-fluid or multi-species, variable-density isotropic turbulence interaction with a Mach 2 shock is studied using turbulence-resolving shock-capturing simulations and Eulerian (grid) and Lagrangian…

Fluid Dynamics · Physics 2020-01-08 Yifeng Tian , Farhad A. Jaberi , Daniel Livescu
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