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The thermal expansion of a fluid combined with a temperature-dependent viscosity introduces nonlinearities in the Navier-Stokes equations unrelated to the convective momentum current. The couplings generate the possibility for net fluid…

Soft Condensed Matter · Physics 2009-11-13 Franz M. Weinert , Jonas A. Kraus , Thomas Franosch , Dieter Braun

In ideal fluids, Clebsch potentials occur as paired canonical variables associated with the Hamiltonian description of the Euler equations. This paper explores the properties of the incompressible Navier-Stokes equations when the velocity…

Fluid Dynamics · Physics 2021-01-19 Naoki Sato

In this article, we discuss gradient robust discretizations for the simulation of non-linear incompressible Navier-Stokes problem and the optimal control of such flow. We consider several formulations of the flow problem that are equivalent…

Optimization and Control · Mathematics 2026-03-13 Constanze Neutsch , Winnifried Wollner

Mechanical effects that span multiple physical scales -- such as the influence of vanishing molecular viscosity on large-scale flow structures under specific conditions -- play a critical role in real fluid systems. The spin angular…

Fluid Dynamics · Physics 2025-10-14 Satori Tsuzuki

This paper introduces the fundamental continuum theory governing momentum transport in isotropic nanofluidic flows. The theory is an extension to the classical Navier-Stokes equation, which includes coupling between translational and…

Soft Condensed Matter · Physics 2017-01-04 J. S. Hansen , Jeppe C. Dyre , Peter J. Daivis , B. D. Todd , Henrik Bruus

In many physical situations involving diverse length scales, waves or rays representing them travel through media characterized by spatially smooth, random, modest refactive index variations. "Primary" diffraction (by individual…

Classical Physics · Physics 2021-12-14 Eric J Heller , Ragnar Fleischmann , Tobias Kramer

We study the input-output response of a streamwise constant projection of the Navier-Stokes equations for plane Couette flow, the so-called 2D/3C model. Study of a streamwise constant model is motivated by numerical and experimental…

Introduction: the Navier-Stokes equations are essential in fluid dynamics, describing the motion of fluids like liquids and gases. Solving these equations, especially in complex flows and high-Reynolds-number regimes, is a significant…

Fluid Dynamics · Physics 2024-12-05 Sebastian Ali Sacasa-Cespedes

An oxidation process is simulated for a bundle of metal tubes in a cross-flow. A fluid flow is governed by the incompressible Navier-Stokes equations. To describe the transport of oxygen, the corresponding convection-diffusion equation is…

Computational Engineering, Finance, and Science · Computer Science 2017-08-15 Alexander G. Churbanov , Oleg Iliev , Valery F. Strizhov , Petr N. Vabishchevich

Maritime vessel maneuvers, characterized by their inherent complexity and indeterminacy, requires vessel trajectory prediction system capable of modeling the multi-modality nature of future motion states. Conventional stochastic trajectory…

Computer Vision and Pattern Recognition · Computer Science 2024-10-15 Changlin Li , Yanglei Gan , Tian Lan , Yuxiang Cai , Xueyi Liu , Run Lin , Qiao Liu

Diffusion of point-like non interacting particles in a two-dimensional (2D) channel of varying cross section is considered. The particles are biased by a constant force in the transverse direction. We apply our recurrence mapping procedure,…

Statistical Mechanics · Physics 2015-05-28 Pavol Kalinay

For the rendering of multiple scattering effects in participating media, methods based on the diffusion approximation are an extremely efficient alternative to Monte Carlo path tracing. However, in sufficiently transparent regions,…

Graphics · Computer Science 2014-04-01 David Koerner , Jamie Portsmouth , Filip Sadlo , Thomas Ertl , Bernd Eberhardt

This paper presents a rigorous derivation of an effective model for fluid flow through a thin elastic porous membrane separating two fluid bulk domains. The microscopic setting involves a periodically structured porous membrane composed of…

Analysis of PDEs · Mathematics 2025-08-07 Markus Gahn , Maria Neuss-Radu

In the hydrodynamic theory, the non-equilibrium dynamics of a many-body system is approximated, at large scales of space and time, by irreversible relaxation to local entropy maximisation. This results in a convective equation corrected by…

Statistical Mechanics · Physics 2025-12-02 Friedrich Hübner , Leonardo Biagetti , Jacopo De Nardis , Benjamin Doyon

The statistical nature of discrete fluid molecules with random thermal motion so far has not been considered in mainstream fluid mechanics based on Navier-Stokes equations, wherein fluids have been treated as a continuum breaking into many…

Fluid Dynamics · Physics 2023-07-17 Haibing Peng

This paper presents an innovative framework for analyzing the regularity of solutions to the stochastic Navier-Stokes equations by integrating Sobolev-Besov hybrid spaces with fractional operators and quantum-inspired dynamics. We propose…

Hydrodynamics provides a universal description of the emergent collective dynamics of vastly different many-body systems, based solely on their symmetries and conservation laws. Here we harness this universality, encoded in the…

Statistical Mechanics · Physics 2025-12-16 P. I. Hurtado , J. J. del Pozo , P. L. Garrido

In this paper we investigate the stress concentration problem that occurs when two convex rigid particles are closely immersed in a fluid flow. The governing equations for the fluid flow are the stationary incompressible Navier-Stokes…

Analysis of PDEs · Mathematics 2024-11-26 Haigang Li , Peihao Zhang

In this paper, we consider dynamics defined by the Navier-Stokes equations in the Oberbeck-Boussinesq approximation in a two dimensional domain. This model of fluid dynamics involve fundamental physical effects: convection, and diffusion.…

Mathematical Physics · Physics 2019-01-08 Sergey Vakulenko

Neural network representations of simple models, such as linear regression, are being studied increasingly to better understand the underlying principles of deep learning algorithms. However, neural representations of distributional…