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相关论文: Random field reconstruction of inhomogeneous turbu…

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We develop and analyze a random field model for the reconstruction of turbulent velocity fluctuations from inhomogeneous characteristic flow quantities provided by RANS simulations that is accessible to both a rigorous analytical validation…

We investigate the statistical properties, based on numerical simulations and analytical calculations, of a recently proposed stochastic model for the velocity field of an incompressible, homogeneous, isotropic and fully developed turbulent…

流体动力学 · 物理学 2016-04-28 Rodrigo M. Pereira , Christophe Garban , Laurent Chevillard

Fully developed turbulence is analised with the lattice model employing vortex tube representation which is introduced recently by the authors. Several characteric features observed in experiments and direct numeric integrations are…

凝聚态物理 · 物理学 2007-05-23 Y-h. Taguchi , Hideki Takayasu

We present here a new stochastic modelling in the constitution of fluid flow reduced-order models. This framework introduces a spatially inhomogeneous random field to represent the unresolved small-scale velocity component. Such a…

流体动力学 · 物理学 2017-09-20 Valentin Resseguier , Etienne Mémin , Dominique Heitz , Bertrand Chapron

Motivated by the modeling of the spatial structure of the velocity field of three-dimensional turbulent flows, and the phenomenology of cascade phenomena, a linear dynamics has been recently proposed able to generate high velocity gradients…

Selected recent contributions involving fluctuating velocity fields to the rapidly developing domain of stochastic field theory are reviewed. Functional representations for solutions of stochastic differential equations and master equations…

统计力学 · 物理学 2016-11-22 Michal Hnatič , Juha Honkonen , Tomáš Lučivjanský

We present a numerical investigation of stochastic transport in ideal fluids. According to Holm (Proc Roy Soc, 2015) and Cotter et al. (2017), the principles of transformation theory and multi-time homogenisation, respectively, imply a…

流体动力学 · 物理学 2018-09-28 Colin J. Cotter , Dan Crisan , Darryl D. Holm , Wei Pan , Igor Shevchenko

We present a status report on a discrete approach to the the near-equilibrium statistical theory of three-dimensional turbulence, which generalizes earlier work by no longer requiring that the vorticity field be a union of discrete vortex…

数值分析 · 数学 2025-10-20 G. I. Barenblatt , A. J. Chorin

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…

流体动力学 · 物理学 2010-09-14 Sergio Chibbaro , Jean-Pierre Minier

This paper exposes a novel exploratory formalism, which end goal is the numerical simulation of the dynamics of a cloud of particles weakly or strongly coupled with a turbulent fluid. Giventhe large panel of expertise of the list of…

偏微分方程分析 · 数学 2019-10-21 Ludovic Goudenège , Adam Larat , Julie Llobell , Marc Massot , David Mercier , Olivier Thomine , Aymeric Vié

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

We introduce a family of stochastic models motivated by the study of nonequilibrium steady states of fluid equations. These models decompose the deterministic dynamics of interest into fundamental building blocks, i.e., minimal vector…

概率论 · 数学 2025-05-07 Andrea Agazzi , Jonathan C. Mattingly , Omar Melikechi

Turbulence is a complex system exhibiting both universal statistical features and prominent coherent structures. We model turbulence using coherent vortices distributed within a multi-scale statistical framework, termed `woven turbulence'.…

流体动力学 · 物理学 2025-12-04 Zishuo Han , Weiyu Shen , Yue Yang

A new model for the "rapid" part of the velocity/pressure-gradient correlation in the Reynolds averaged Navier-Stokes equations is suggested. It is shown that in an inhomogeneous incompressible turbulent flow, the model that is linear in…

流体动力学 · 物理学 2007-05-23 Svetlana V. Poroseva

Modelling incompressible ideal fluids as a finite collection of vortex filaments is important in physics (super-fluidity, models for the onset of turbulence) as well as for numerical algorithms used in computer graphics for the real time…

可精确求解与可积系统 · 物理学 2007-10-10 Ulrich Pinkall , Boris Springborn , Steffen Weissmann

Turbulent cascades characterize the transfer of energy injected by a random force at large scales towards the small scales. In hydrodynamic turbulence, when the Reynolds number is large, the velocity field of the fluid becomes irregular and…

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…

流体动力学 · 物理学 2020-05-01 Alessio Innocenti , Nicolas Mordant , Nick Stelzenmuller , Sergio Chibbaro

This thesis deals with the investigation of a H(div)-conforming hybrid discontinuous Galerkin discretization for incompressible turbulent flows. The discretization method provides many physical and solving-oriented properties, which may be…

计算工程、金融与科学 · 计算机科学 2020-09-25 Xaver Mooslechner

The impact of turbulent fluctuations on the forces exerted by a fluid on a towed spherical particle is investigated by means of high-resolution direct numerical simulations. The measurements are carried out using a novel scheme to integrate…

流体动力学 · 物理学 2016-11-21 Holger Homann , Jérémie Bec , Rainer Grauer

Accurate simulation of turbulent flow with separation is an important but challenging problem. In this paper, a data-driven Reynolds-averaged turbulence modeling approach, field inversion and machine learning is implemented to modify the…

流体动力学 · 物理学 2022-06-02 Chongyang Yan , Haoran Li , Yufei Zhang , Haixin Chen
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