English
Related papers

Related papers: Designing a Finite-Time Mixer: Optimizing Stirring…

200 papers

The shearing instability of a dilute granular mixture composed of smooth inelastic hard spheres or disks is investigated. By using the Navier-Stokes hydrodynamic equations, it is shown that the scaled transversal velocity mode exhibits a…

Statistical Mechanics · Physics 2015-06-15 J. Javier Brey , M. J. Ruiz-Montero

This study proposes an algorithm for modeling compressible flows in spherical shells in nearly incompressible and weakly compressible regimes based on an implicit direction splitting approach. The method retains theoretically expected…

Numerical Analysis · Mathematics 2021-04-21 Roman Frolov , Peter Minev , Aziz Takhirov

Maxima of the scalar dissipation rate in turbulence appear in form of sheets and correspond to the potentially most intensive scalar mixing events. Their cross-section extension determines a locally varying diffusion scale of the mixing…

Chaotic Dynamics · Physics 2007-05-23 Dan Kushnir , Joerg Schumacher , Achi Brandt

In this article, we propose a novel scalar-transport model for the simulation of scalar quantities in two-phase flows with a phase-field method (diffuse-interface method). In a two-phase flow, the scalar quantities typically have disparate…

Fluid Dynamics · Physics 2020-11-24 Suhas S. Jain , Ali Mani

We show exponential mixing of passive scalars advected by a solution to the stochastic Navier-Stokes equations with finitely many (e.g. four) forced modes satisfying a hypoellipticity condition. Our proof combines the asymptotic strong…

Analysis of PDEs · Mathematics 2024-08-06 William Cooperman , Keefer Rowan

The Fourier analysis of the \emph{p}-multigrid acceleration technique is considered for a dual-time scheme applied to the advection-diffusion equation with various cycle configurations. It is found that improved convergence can be achieved…

Numerical Analysis · Mathematics 2020-08-13 Will Trojak , Freddie D. Witherden

The performance of time series forecasting has recently been greatly improved by the introduction of transformers. In this paper, we propose a general multi-scale framework that can be applied to the state-of-the-art transformer-based time…

Machine Learning · Computer Science 2023-02-08 Amin Shabani , Amir Abdi , Lili Meng , Tristan Sylvain

We consider the questions of efficient mixing and un-mixing by incompressible flows which satisfy periodic, no-flow, or no-slip boundary conditions on a square. Under the uniform-in-time constraint $\|\nabla u(\cdot,t)\|_p\leq 1$ we show…

Analysis of PDEs · Mathematics 2014-07-17 Yao Yao , Andrej Zlatos

Data collection costs can vary widely across variables in data science tasks. Two-phase designs can be employed to save data collection costs. This paper considers the two-phase studies where inexpensive variables are collected for all…

Methodology · Statistics 2025-12-04 Ruoyu Wang , Qihua Wang , Wang Miao

In this paper, we propose novel algorithms integrated projection-free techniques with accelerated gradient flows to minimize bending energies for nonlinear plates with non-convex metric constraints. We discuss the stability and constraint…

Numerical Analysis · Mathematics 2025-06-18 Guozhi Dong , Hailong Guo , Shuo Yang

Generative models based on dynamical equations such as flows and diffusions offer exceptional sample quality, but require computationally expensive numerical integration during inference. The advent of consistency models has enabled…

Machine Learning · Computer Science 2025-06-04 Nicholas M. Boffi , Michael S. Albergo , Eric Vanden-Eijnden

In machine learning and data mining, linear models have been widely used to model the response as parametric linear functions of the predictors. To relax such stringent assumptions made by parametric linear models, additive models consider…

Machine Learning · Statistics 2017-10-18 Sheng Chen , Arindam Banerjee

Separated flows past complex geometries are modelled by discrete vortex techniques. The flows are assumed to be rotational and inviscid, and a new technique is described to determine the streamfunctions for linear shear profiles. The…

chao-dyn · Physics 2007-05-23 P. Franzese , L. Zannetti

This paper presents a novel adjoint solver for differentiable fluid simulation based on bidirectional flow maps. Our key observation is that the forward fluid solver and its corresponding backward, adjoint solver share the same flow map as…

Graphics · Computer Science 2025-11-04 Zhiqi Li , Jinjin He , Barnabás Börcsök , Taiyuan Zhang , Duowen Chen , Tao Du , Ming C. Lin , Greg Turk , Bo Zhu

The Cahn-Hilliard equation describes phase separation in binary liquids. Here we study this equation with spatially-varying sources and stirring, or advection. We specialize to symmetric mixtures and time-independent sources and discuss…

Fluid Dynamics · Physics 2017-10-26 Lennon O Naraigh , Jean-Luc Thiffeault

We use numerical simulations to examine two-dimensional particle mixtures that strongly phase separate in equilibrium. When the system is externally driven in the presence of quenched disorder, plastic flow occurs in the form of meandering…

Statistical Mechanics · Physics 2009-11-13 A. Libal , C. Reichhardt , C. J. Olson Reichhardt

Efficient mixing of fluids is essential in many practical applications to achieve homogeneity. For microscopic systems, however, both diffusion and turbulence are ineffective methods to achieve chaotic mixing due to the low Reynolds number,…

Soft Condensed Matter · Physics 2025-09-18 Yue Wang , Jonas Berx

Aligned superhydrophobic surfaces with the same texture orientation reduce drag in the channel and generate secondary flows transverse to the direction of the applied pressure gradient. Here we show that a transverse shear can be easily…

The mixing of an impurity into a flowing fluid is an important process in many areas of science, including geophysical processes, chemical reactors, and microfluidic devices. In some cases, for example periodic flows, the concepts of…

Chaotic Dynamics · Physics 2016-02-29 G. Voth , G. Haller , J. P. Gollub

We present efficient deep learning techniques for approximating flow and transport equations for both single phase and two-phase flow problems. The proposed methods take advantages of the sparsity structures in the underlying discrete…

Numerical Analysis · Mathematics 2020-01-08 Yating Wang , Guang Lin