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We study the long time behavior of an advection-diffusion equation with a random shear flow which depends on a stationary Ornstein-Uhlenbeck (OU) process in parallel-plate channels enforcing the no-flux boundary conditions. We derive a…

Analysis of PDEs · Mathematics 2020-12-15 Lingyun Ding , Richard M. McLaughlin

We study the effect of impermeable boundaries on the symmetry properties of a random passive scalar field advected by random flows. We focus on a broad class of nonlinear shear flows multiplied by a stationary, Ornstein-Uhlenbeck (OU) time…

Fluid Dynamics · Physics 2021-07-07 R. Camassa , L. Ding , Z. Kilic , R. M. McLaughlin

The dispersion of a passive scalar in a fluid through the combined action of advection and molecular diffusion is often described as a diffusive process, with an effective diffusivity that is enhanced compared to the molecular value.…

Fluid Dynamics · Physics 2015-06-18 P. H. Haynes , J. Vanneste

We study passive scalar mixing by parallel shear flows in the presence of weak molecular diffusion. We recover the sharp uniform-in-diffusivity mixing rate for shear flows with finitely many critical points, recently proven in [1]. Our…

Analysis of PDEs · Mathematics 2026-03-11 Kyle L. Liss , Kunhui Luan

We develop a theory of enhanced diffusivity and skewness of the longitudinal distribution of a diffusing tracer advected by a periodic time-varying shear flow in a straight channel. Although applicable to general fluid flow, we restrict the…

Fluid Dynamics · Physics 2023-04-20 Lingyun Ding , Robert Hunt , Hunter Woodie , Richard M. McLaughlin

The dispersion of a diffusive scalar in a fluid flowing through a network has many applications including to biological flows, porous media, water supply and urban pollution. Motivated by this, we develop a large-deviation theory that…

Fluid Dynamics · Physics 2016-09-14 Alexandra Tzella , Jacques Vanneste

We develop a framework for studying the enhanced dissipation of passive scalars advected by shear flows based on analyzing the particle trajectories of the stochastic differential equation associated with the governing drift-diffusion…

Analysis of PDEs · Mathematics 2024-10-10 Victor Gardner , Kyle L. Liss , Jonathan C. Mattingly

We report on measurements of self-diffusion coefficients in discrete numerical simulations of steady, homogeneous, collisional shearing flows of nearly identical, frictional, inelastic spheres. We focus on a range of relatively high solid…

Soft Condensed Matter · Physics 2021-04-01 Riccardo Artoni , Michele Larcher , James Jenkins , Patrick Richard

Diffusion-driven flow is a boundary layer flow arising from the interplay of gravity and diffusion in density-stratified fluids when a gravitational field is non-parallel to an impermeable solid boundary. This study investigates…

Fluid Dynamics · Physics 2024-09-23 Lingyun Ding

Sampling a probability distribution with an unknown normalization constant is a fundamental problem in computational science and engineering. This task may be cast as an optimization problem over all probability measures, and an initial…

Machine Learning · Statistics 2024-09-12 Yifan Chen , Daniel Zhengyu Huang , Jiaoyang Huang , Sebastian Reich , Andrew M. Stuart

We provide an explicit rigorous derivation of a diffusion limit - a stochastic differential equation with additive noise - from a deterministic skew-product flow. This flow is assumed to exhibit time-scale separation and has the form of a…

Dynamical Systems · Mathematics 2015-05-27 I. Melbourne , A. M. Stuart

We analyze the main features of granular shear flow through experimental measurements in a Couette geometry and a comparison to a locally Newtonian, continuum model of granular flow. The model is based on earlier hydrodynamic models,…

Statistical Mechanics · Physics 2021-05-26 L. Bocquet , W. Losert , D. Schalk , T. C. Lubensky , J. P. Gollub

In this paper, we consider queueing systems where the dynamics are non-stationary and state-dependent. For performance analysis of these systems, fluid and diffusion models have been typically used. Although they are proven to be…

Probability · Mathematics 2016-09-08 Young Myoung Ko , Natarajan Gautam

We study the long time behavior (homogenization) of a diffusion in random medium with time and space dependent coefficients. The diffusion coefficient may degenerate. In Stochastic Process. Appl. (2007) (to appear), an invariance principle…

Probability · Mathematics 2008-08-26 Rémi Rhodes

Spontaneous stochasticity refers to the emergence of intrinsic randomness in deterministic systems under singular limits, a phenomenon conjectured to be fundamental in turbulence. Armstrong and Vicol \citep{AV23,AV24} recently constructed a…

Fluid Dynamics · Physics 2025-04-23 W. Ruffenach , E. Simonnet , N. Valade

Diffusivity is a key quantity in describing velocity fluctuations in granular materials. These fluctuations are the basis of many thermodynamic and hydrodynamic models which aim to provide a statistical description of granular systems. We…

Soft Condensed Matter · Physics 2009-11-10 Brian Utter , R. P. Behringer

We present results on a meso-scale model for amorphous matter in athermal, quasi-static (a-AQS), steady state shear flow. In particular, we perform a careful analysis of the scaling with the lateral system size, $L$, of: i) statistics of…

Soft Condensed Matter · Physics 2019-10-30 Botond Tyukodi , Damien Vandembroucq , Craig E Maloney

This work investigates the long-time asymptotic behavior of a diffusing passive scalar advected by fluid flow in a straight channel with a periodically varying cross-section. The goal is to derive an asymptotic expansion for the scalar…

Fluid Dynamics · Physics 2026-04-09 Lingyun Ding

We study two types of intrinsic uncertainties, statistical errors and system size effects, in estimating shear viscosity via equilibrium molecular dynamics simulations and compare them with the corresponding uncertainties in evaluating the…

Chemical Physics · Physics 2018-09-05 Kang-Sahn Kim , Myung Hoon Han , Changho Kim , Zhen Li , George Em Karniadakis , Eok Kyun Lee

We provide new convergence guarantees in Wasserstein distance for diffusion-based generative models, covering both stochastic (DDPM-like) and deterministic (DDIM-like) sampling methods. We introduce a simple framework to analyze…

Machine Learning · Computer Science 2025-11-14 Eliot Beyler , Francis Bach
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