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Related papers: Fluid Deformation in Random Unsteady Flow

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The log-homotopy particle flow filter resolves the Bayesian update by transporting particles along a continuous trajectory in pseudo-time. However, the governing partial differential equation for the flow velocity is fundamentally…

Systems and Control · Electrical Eng. & Systems 2026-05-18 Olivér Törő , Domonkos Csuzdi , Tamás Bécsi

The characterization of intermittency in turbulence has its roots in the K62 theory, and if no proper definition is to be found in the literature, statistical properties of intermittency were studied and models were developed in attempt to…

Fluid Dynamics · Physics 2021-07-14 Roxane Letournel , Ludovic Goudenège , Rémi Zamansky , Aymeric Vié , Marc Massot

The kinematic flow pattern in slow deformation of a model dense granular medium is studied at high resolution using \emph{in situ} imaging, coupled with particle tracking. The deformation configuration is indentation by a flat punch under…

Soft Condensed Matter · Physics 2017-03-22 Koushik Viswanathan , Anirban Mahato , Tejas G. Murthy , Tomasz Koziara , Srinivasan Chandrasekar

The stochastic motion of a two-dimensional vesicle in linear shear flow is studied at finite temperature. In the limit of small deformations from a circle, Langevin-type equations of motion are derived, which are highly nonlinear due to the…

Soft Condensed Matter · Physics 2009-11-13 Reimar Finken , Antonio Lamura , Udo Seifert , Gerhard Gompper

In this work, a stochastic representation based on a physical transport principle is proposed to account for mesoscale eddy effects on the large-scale oceanic circulation. This stochastic framework arises from a decomposition of the…

Geophysics · Physics 2022-07-26 Long Li , Bruno Deremble , Noé Lahaye , Etienne Mémin

The present work studies the isotropic and homogeneous turbulence for incompressible fluids through a specific Lyapunov analysis, assuming that the turbulence is due to the bifurcations associated to the velocity field. The analysis…

Fluid Dynamics · Physics 2010-10-12 Nicola de Divitiis

We compute the leading-order inertial corrections to the instantaneous force acting on a rigid body moving with a time-dependent slip velocity in a linear flow field, assuming that the variation of the undisturbed flow at the body scale is…

Fluid Dynamics · Physics 2025-10-01 Fabien Candelier , Bernhard Mehlig , Jacques Magnaudet

To predict flow behavior of entangled polymer melt, we have developed multiscale simulation composed of Lagrangian fluid particle simulation and coarse-grained polymer dynamics simulation. We have introduced a particle deformation in the…

Soft Condensed Matter · Physics 2014-01-31 Takahiro Murashima

Developing reduced order models for the transport of solid particles in turbulence typically requires a statistical description of the particle-turbulence interactions. In this work, we utilize a statistical framework to derive continuum…

Fluid Dynamics · Physics 2024-06-21 Andrew P. Grace , David Richter , Tim Berk , Andrew D. Bragg

Flows with deformable interfaces are commonly controlled by applying an external field or modifying the boundaries that interact with the fluid, but realizing such solutions can be demanding or impractical in various scenarios. Here, we…

Soft Condensed Matter · Physics 2025-03-28 Hugo França , Maziyar Jalaal

We show that the Lagrangian flow associated with the stochastic 3D primitive equations (PEs) with non-degenerate noise is chaotic, i.e., the corresponding top Lyapunov exponent is strictly positive almost surely. This result builds on the…

Probability · Mathematics 2025-05-08 Antonio Agresti

To facilitate the understanding and to quantitatively assess the material transport in fluids, a modern characterisation method has emerged in fluid dynamics in the last decades footed in dynamical systems theory. It allows to examine the…

A large number of models which address the dynamics of particle-laden turbulent flows have been developed based on the assumption of local isotropy and use the Kolmogorov constant that correlates the spectral distribution of turbulent…

Fluid Dynamics · Physics 2022-10-26 Naveen Rohilla , Partha S Goswami

This paper establishes a unified element-based framework for formation control by introducing the concept of the deformation gradient from continuum mechanics. Unlike traditional methods that rely on geometric constraints defined on graph…

Systems and Control · Electrical Eng. & Systems 2026-04-07 Kun Cao , Lihua Xie

We consider the relationship between Eulerian modal decompositions and Lagrangian coherent structures (LCSs). The model sensitivity framework developed by Kasz\'as and Haller (2020) is used to express data-driven modal representations of…

Fluid Dynamics · Physics 2025-12-24 Morgan R. Jones , Charles Klewicki , Oliver Khan , Steven L. Brunton , Mitul Luhar

We examine the process of particle capture by large deformable drops in turbulent channel flow. We simulate the solid-liquid-liquid three-phase flow with an Eulerian-Lagrangian method based on Direct Numerical Simulation of turbulence…

Fluid Dynamics · Physics 2020-11-05 Arash Hajisharifi , Cristian Marchioli , Alfredo Soldati

We introduce ImitationFlow, a novel Deep generative model that allows learning complex globally stable, stochastic, nonlinear dynamics. Our approach extends the Normalizing Flows framework to learn stable Stochastic Differential Equations.…

Machine Learning · Computer Science 2020-10-27 Julen Urain , Michelle Ginesi , Davide Tateo , Jan Peters

Three dimensional unsteady flow of fluids in the Lagrangian description is considered as an autonomous dynamical system in four dimensions. The condition for the existence of a symplectic structure on the extended space is the frozen field…

solv-int · Physics 2009-10-30 H. Gumral

The deformation and break-up of Newtonian/viscoelastic droplets are studied in confined shear flow. Our numerical approach is based on a combination of Lattice-Boltzmann models (LBM) and finite difference schemes, the former used to model…

Soft Condensed Matter · Physics 2016-11-15 A. Gupta , M. Sbragaglia

The generative paradigm has become increasingly important in machine learning and deep learning models. Among popular generative models are normalizing flows, which enable exact likelihood estimation by transforming a base distribution…

Machine Learning · Computer Science 2025-03-18 Naoufal El Bekri , Lucas Drumetz , Franck Vermet
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