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The aim of this paper is to devise a turbulence model for the particle method Smoothed Particle Hydrodynamics (SPH) which makes few assumptions, conserves linear and angular momentum, satisfies a discrete version of Kelvin's circulation…

流体动力学 · 物理学 2009-11-16 J. J. Monaghan

We study the effect of turbulence on a sedimenting layer of particles by means of direct numerical simulations. A Lagrangian model in which particles are considered as tracers with an additional downward settling velocity is integrated…

流体动力学 · 物理学 2020-07-08 Alessandro Sozza , Gabor Drotos , Emilio Hernández-García , Cristóbal López

A model for the pseudo-turbulent Reynolds stress tensor in compressible flows through monodisperse particle clouds is developed based on data from particle resolved numerical simulations. This model extends previous models for the…

流体动力学 · 物理学 2025-05-09 Andreas Nygård Osnes , Magnus Vartdal

Inpired by recent measurements of the velocity and acceleration statistics of Lagrangian tracer particles embedded in a turbulent quantum liquid we propose a new superstatistical model for the dynamics of tracer particles in quantum…

统计力学 · 物理学 2015-06-22 Shihan Miah , Christian Beck

We present results from a numerical study of particle dispersion in the weakly nonlinear regime of Rayleigh-B\'enard convection of a fluid with Prandtl number around unity, where bi-stability between ideal straight convection rolls and weak…

流体动力学 · 物理学 2016-06-29 Simon Schütz , Eberhard Bodenschatz

On their roller coaster ride through turbulence, tracer particles sample the fluctuations of the underlying fields in space and time. Quantitatively relating particle and field statistics remains a fundamental challenge in a large variety…

流体动力学 · 物理学 2018-01-10 Cristian C. Lalescu , Michael Wilczek

Suspensions of finite-size solid particles in a turbulent pipe flow are found in many industrial and technical flows. Due to the ample parameter space consisting of particle size, concentration, density and Reynolds number, a complete…

流体动力学 · 物理学 2024-11-18 Martin Leskovec , Sagar Zade , Mehdi Niazi , Pedro Costa , Fredrik Lundell , Luca Brandt

In this paper we present a new model for modeling the diffusion and relative dispersion of particles in homogeneous isotropic turbulence. We use an Heisenberg-like Hamiltonian to incorporate spatial correlations between fluid particles,…

流体动力学 · 物理学 2012-12-18 Thomas Burgener , Dirk Kadau , Hans Jürgen Herrmann

The aim of the present paper is to introduce and to discuss inconsistencies errors that may arise when Eulerian and Lagrangian models are coupled for the simulations of turbulent poly-dispersed two-phase flows. In these hydrid models, two…

流体动力学 · 物理学 2011-04-07 Sergio Chibbaro , Jean-Pierre Minier

Suspensions with fiber-like particles in the low Reynolds number regime are modeled by two different approaches that both use a Lagrangian representation of individual particles. The first method is the well-established formulation based on…

计算工程、金融与科学 · 计算机科学 2015-03-25 Dominik Bartuschat , Ellen Fischermeier , Katarina Gustavsson , Ulrich Rüde

A Lagrangian particle cloud model is proposed that accounts for the effects of Reynolds-averaged particle and turbulent stresses and the averaged carrier-phase velocity of the sub-particle-cloud scale on the averaged motion and velocity of…

流体动力学 · 物理学 2017-04-12 Sean Davis , Gustaaf Jacobs , Oishik Sen , H. S. Udaykumar

Recent advances in random-walk particle-tracking have enabled direct simulation of mixing and reactions on particles by allowing the particles to interact with each other using a multi-point mass transfer scheme. The mass transfer scheme…

计算物理 · 物理学 2019-04-22 Nicholas B. Engdahl , Michael J. Schmidt , David A. Benson

We solve an inverse problem for fluid particle pair-statistics: we show that a time sequence of probability density functions (PDF's) of separations can be exactly reproduced by solving the diffusion equation with a suitable time-dependent…

流体动力学 · 物理学 2015-06-16 Gregory L. Eyink , Damien Benveniste

A probabilistic machine learning model is introduced to augment the $k-\omega\ SST$ turbulence model in order to improve the modelling of separated flows and the generalisability of learnt corrections. Increasingly, machine learning methods…

计算工程、金融与科学 · 计算机科学 2023-01-24 Joel Ho , Nick Pepper , Tim Dodwell

A coarse-grained particle model for incompressible Navier-Stokes (NS) equation is proposed based on spatial filtering by utilizing smoothed particle hydrodynamics (SPH) approximations. This model is similar to our previous developed SPH…

流体动力学 · 物理学 2018-02-13 X. Y. Hu , N. A. Adams

The turbulent diffusion of Lagrangian tracer particles has been studied in a flow on the surface of a large tank of water and in computer simulations. The effect of flow compressibility is captured in images of particle fields. The velocity…

混沌动力学 · 物理学 2009-11-10 John R. Cressman , Walter I. Goldburg , Joerg Schumacher

We study the effect of different forcing functions and of the local gradient Richardson number Rig on the vertical mixing of Lagrangian tracers in stably stratified turbulence under the Boussinesq approximation, and present a wave and…

流体动力学 · 物理学 2019-02-01 Nicolas E. Sujovolsky , Pablo D. Mininni

We present models for single-particle dispersion in vertical and horizontal directions of stably stratified flows. The model in the vertical direction is based on the observed Lagrangian spectrum of the vertical velocity, while the model in…

流体动力学 · 物理学 2018-03-21 Nicolas E. Sujovolsky , Pablo D. Mininni , Mark P. Rast

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…

流体动力学 · 物理学 2009-07-01 Boris Arcen , Anne Tanière

We develop a new formalism for the study of turbulence using the scale relativity framework (applied in $v$-space according to de Montera's proposal). We first review some of the various ingredients which are at the heart of the scale…

综合物理 · 物理学 2020-01-08 Laurent Nottale , Thierry Lehner
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