๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Superstatistical Lagrangian stochastic modeling

โœ Scribed by A.M Reynolds

Publisher
Elsevier Science
Year
2004
Tongue
English
Weight
290 KB
Volume
340
Category
Article
ISSN
0378-4371

No coin nor oath required. For personal study only.

โœฆ Synopsis

A simple superstatistical Lagrangian stochastic model (Phys. Fluids 15 (2003) L1; Phys. Rev. Lett. 91 (2003) 84503) that accounts explicitly for uctuations in the rate of dissipation of turbulent kinetic energy has been shown to be in remarkably close agreement with recently acquired data for unconditional Lagrangian acceleration statistics. In this paper, a more elaborate version of the model is shown to predict correctly the observed conditional dependency of Lagrangian acceleration statistics on velocity. The modeling approach is then extended to the simulation of large/heavy-particle accelerations in turbulence. Model predictions for the distribution of accelerations of 450 m diameter particles with near-neutral buoyancy are shown to be in excellent agreement with experimental data. Tsallis statistics are found to describe accurately model predictions for distributions of heavy-particle velocities and accelerations.

๐Ÿ“œ SIMILAR VOLUMES

A Lagrangian Stochastic Model for Heavy
A Lagrangian Stochastic Model for Heavy Particle Deposition
โœ Andrew Michael Reynolds ๐Ÿ“‚ Article ๐Ÿ“… 1999 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 87 KB

A Lagrangian stochastic model for the deposition of heavy particles from turbulent flows is presented. Heavy particles are treated as tracer particles moving in a virtual fluid having heavy particle velocity statistics. These velocity statistics are deduced from the particle momentum equation. The m

A Lagrangian, stochastic modeling framew
A Lagrangian, stochastic modeling framework for multi-phase flow in porous media
โœ Manav Tyagi; Patrick Jenny; Ivan Lunati; Hamdi A. Tchelepi ๐Ÿ“‚ Article ๐Ÿ“… 2008 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 955 KB

Many of the complex physical processes relevant for compositional multi-phase flow in porous media are well understood at the pore-scale level. In order to study CO 2 storage in sub-surface formations, however, it is not feasible to perform simulations at these small scales directly and effective mo

On the Formulation of Lagrangian Stochas
On the Formulation of Lagrangian Stochastic Models for Heavy-Particle Trajectories
โœ Andrew Michael Reynolds ๐Ÿ“‚ Article ๐Ÿ“… 2000 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 95 KB

The modeling approach of B. L. Sawford and F. H. Guest ("8th Symposium of Turbulence and Diffusion; San Diego, CA," pp. 96-99. Am. Meteorol. Soc., Boston, MA, 1990) is extended to encompass the formulation of Lagrangian stochastic models for fluid velocities along heavy-particle trajectories in inho