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Lagrangian properties obtained from a Particle Tracking Velocimetry experiment in a turbulent flow at intermediate Reynolds number are presented. Accurate sampling of particle trajectories is essential in order to obtain the Lagrangian…

流体动力学 · 物理学 2015-05-13 Jacob Berg , Soren Ott , Jakob Mann , Beat Luthi

By tracking tracer particles at high speeds and for long times, we study the geometric statistics of Lagrangian trajectories in an intensely turbulent laboratory flow. In particular, we consider the distinction between the displacement of…

数据分析、统计与概率 · 物理学 2015-05-30 Nicholas T. Ouellette , Eberhard Bodenschatz , Haitao Xu

The prediction of aircraft aerodynamic quantities of interest remains among the most pressing challenges for computational fluid dynamics. The aircraft aerodynamics are inherently turbulent with mean-flow three-dimensionality, often…

流体动力学 · 物理学 2021-05-25 Adrián Lozano-Durán , Hyunji Jane Bae

In this study, we investigate the behaviour of sedimenting solid particles and the influence of microscopic particle dynamics on the collective motion of a sedimenting cloud. Departing from conventional direct numerical simulations (DNS),…

流体动力学 · 物理学 2025-10-28 Nejc Vovk , Jana Wedel , Paul Steinmann , Jure Ravnik

The probability density function (PDF) of accelerations in turbulence is derived analytically with the help of the multifractal analysis based on generalized entropy, i.e., the Tsallis or the R\'{e}nyi entropy. It is shown that the derived…

统计力学 · 物理学 2007-05-23 T. Arimitsu , N. Arimitsu

Lagrangian Coherent Structures (LCS) are flow features which are defined to objectively characterize complex fluid behavior over a finite time regardless of the orientation of the observer. Fluidic applications of LCS include geophysical,…

流体动力学 · 物理学 2023-10-18 Tanner D. Harms , Steven L. Brunton , Beverley J. McKeon

The distribution of particle accelerations in turbulence is intermittent, with non-Gaussian tails that are quite different for light and heavy particles. In this article we analyse a closure scheme for the acceleration fluctuations of light…

流体动力学 · 物理学 2016-07-08 S. Vajedi , K. Gustavsson , B. Mehlig , L. Biferale

The advantage of particle Lagrangian methods in computational fluid dynamics is that advection is accurately modeled. However, this complicates the calculation of space derivatives. If a mesh is employed, it must be updated at each time…

流体动力学 · 物理学 2017-01-27 Daniel Duque , Pep Español

We employ novel digital Fresnel reflection holography to capture the 3D flows within the viscous sublayer of a smooth-wall turbulent channel flow at Re{\tau}=400. The measurements reveal unsteady and diverse flow patterns in the sublayer…

流体动力学 · 物理学 2021-04-23 Santosh Kumar Sankar , Xinyi Huang , Xiang Yang , Jiarong Hong

Finite-size impurities suspended in incompressible flows distribute inhomogeneously, leading to a drastic enhancement of collisions. A description of the dynamics in the full position-velocity phase space is essential to understand the…

混沌动力学 · 物理学 2009-11-10 J. Bec , A. Celani , M. Cencini , S. Musacchio

Neural networks of simple structures are used to construct a turbulence model for large-eddy simulation (LES). Data obtained by direct numerical simulation (DNS) of homogeneous isotropic turbulence are used to train neural networks. It is…

流体动力学 · 物理学 2020-12-04 Satoshi Miyazaki , Yuji Hattori

In large-eddy simulations, subgrid-scale (SGS) processes are parameterized as a function of filtered grid-scale variables. First-order, algebraic SGS models are based on the eddy-viscosity assumption, which does not always hold for…

Lagrangian stochastic models are widely used to predict and analyze turbulent dispersion in complex environments, such as in various terrestrial and marine canopy flows. However, due to a lack of empirical data, it is still not understood…

流体动力学 · 物理学 2020-09-09 Ron Shnapp , Yardena Bohbot-Raviv , Alex Liberzon , Eyal Fattal

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…

流体动力学 · 物理学 2024-06-06 Sergio Hoyas , Ricardo Vinuesa , Peter Schmid , Hassan Nagib

This paper describes a study based on computational fluid dynamics (CFD) and deep neural networks that focusing on predicting the flow field in differently distorted U-shaped pipes. The main motivation of this work was to get an insight…

机器学习 · 计算机科学 2020-10-02 Gergely Hajgató , Bálint Gyires-Tóth , György Paál

The modelling of fluid particle accelerations in homogeneous, isotropic turbulence in terms of second-order stochastic models for the Lagrangian velocity is considered. The basis for the Reynolds model (A. M. Reynolds, \textit{Phys. Rev.…

软凝聚态物质 · 物理学 2007-05-23 A. G. Lamorgese , S. B. Pope , P. K. Yeung , B. L. Sawford

We investigate sources of error in acceleration statistics from Lagrangian Particle Tracking (LPT) data and demonstrate techniques to eliminate or minimise bias errors introduced during processing. Numerical simulations of particle tracking…

流体动力学 · 物理学 2018-11-14 John M. Lawson , Eberhard Bodenschatz , Cristian C. Lalescu , Michael Wilczek

We use theory and Direct Numerical Simulations (DNS) to explore the average vertical velocities and spatial distributions of inertial particles settling in a wall-bounded turbulent flow. The theory is based on the exact phase-space equation…

流体动力学 · 物理学 2021-06-09 Andrew D Bragg , David H Richter , Guiquan Wang

Direct numerical simulations two-way coupled with inertial particles are used to investigate the particle distribution and two-way coupling effect of low-inertia ($St_{LSM}=0.0625$, $St_{VLSM}=0.009$) and high-inertia ($St_{LSM}=0.475$,…

流体动力学 · 物理学 2019-06-06 Guiquan Wang , David Richter

Magnetic Resonance Imaging (MRI) experiments have been performed in conjunction with Direct Numerical Simulations (DNS) to study neutrally buoyant particle-laden pipe flows. The flows are characterized by the suspension liquid Reynolds…

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