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Simulating turbulent flows is crucial for a wide range of applications, and machine learning-based solvers are gaining increasing relevance. However, achieving temporal stability when generalizing to longer rollout horizons remains a…

Machine Learning · Computer Science 2024-12-12 Georg Kohl , Li-Wei Chen , Nils Thuerey

In this paper, we present a novel approach to model the fluid/solid interaction forces in a direct solver of the Navier-Stokes equations based on the volume of fluid interface tracking method. The key ingredient of the model is the explicit…

Fluid Dynamics · Physics 2015-06-16 Kyle Mahady , Shahriar Afkhami , Lou Kondic

A simple model accounting for the ejection of heavy particles from the vortical structures of a turbulent flow is introduced. This model involves a space and time discretization of the dynamics and depends on only two parameters: the…

Chaotic Dynamics · Physics 2009-11-13 Jeremie Bec , Raphael Chetrite

Soft matter materials and polymers are widely used in the controlled delivery of drugs. Simulation and modeling provide insight at the atomic scale enabling a level of control unavailable to experiments. We present a workflow protocol for…

Soft Condensed Matter · Physics 2022-03-08 James P. Andrews , Estela Blaisten-Barojas

Active transport such as fluid flow is sought in molecular communication to extend coverage, improve reliability, and mitigate interference. Flow models are often over-simplified, assuming one-dimensional diffusion with constant drift.…

Emerging Technologies · Computer Science 2024-10-30 Wayan Wicke , Tobias Schwering , Arman Ahmadzadeh , Vahid Jamali , Adam Noel , Robert Schober

We apply a recently proposed novel thermostating mechanism to an interacting many-particle system where the bulk particles are moving according to Hamiltonian dynamics. At the boundaries the system is thermalized by deterministic and…

chao-dyn · Physics 2009-10-31 C. Wagner , R. Klages , G. Nicolis

Accurately resolving the coupled momentum transfer between the liquid and solid phases of complex fluids is a fundamental problem in multiphase transport processes, such as hydraulic fracture operations. Specifically we need to characterize…

Fluid Dynamics · Physics 2021-12-22 Célio Fernandes , Salah Faroughi , Ricardo Ribeiro , Ana Isabel , Gareth McKinley

This paper provides open-source code that works as a viscometer of particle-based simulations of three-dimensional fluid-particle interaction systems, targetting slurry or suspension flow in chemical engineering. The smoothed dissipative…

Computational Physics · Physics 2020-02-11 Satori Tsuzuki

This paper introduces Gauge Flow Models, a novel class of Generative Flow Models. These models incorporate a learnable Gauge Field within the Flow Ordinary Differential Equation (ODE). A comprehensive mathematical framework for these…

Machine Learning · Computer Science 2026-03-04 Alexander Strunk , Roland Assam

We derive the pressure tensor and the heat flux to accompany the new macroscopic conservation equations that we developed previously in a volume-based kinetic framework for gas flows. This kinetic description allows for expansion or…

Fluid Dynamics · Physics 2007-05-23 S. Kokou Dadzie , Jason M. Reese

In Large-Eddy simulation of particle-laden flow, the effect of the unresolved scales on the particles needs to be modelled. In this work we analyse three very promising models, namely the approximate deconvolution method (ADM) which was…

Fluid Dynamics · Physics 2015-05-18 Ch. Gobert , M. Manhart

Detailed studies of the intriguing field-dependent dynamics and transport properties of confined flowing ferrofluids require efficient mesoscopic simulation methods that account for fluctuating ferrohydrodynamics. Here, we propose such a…

Soft Condensed Matter · Physics 2024-06-19 Patrick Ilg

A four-way coupling scheme for the direct numerical simulation of particle-laden flows is developed and analyzed. It employs a novel adaptive multi-relaxation time lattice Boltzmann method to simulate the fluid phase efficiently. The…

Computational Physics · Physics 2020-03-04 Christoph Rettinger , Ulrich Rüde

Energy distributions of high frequency linear wave fields are often modelled in terms of flow or transport equations with ray dynamics given by a Hamiltonian vector field in phase space. Applications arise in underwater and room acoustics,…

Computational Physics · Physics 2014-08-12 David Chappell , Gregor Tanner , Niels Sondergaard , Dominik Loechel

The mathematical approaches for modeling dynamic traffic can roughly be divided into two categories: discrete packet routing models and continuous flow over time models. Despite very vital research activities on models in both categories,…

Computer Science and Game Theory · Computer Science 2022-12-09 Leon Sering , Laura Vargas Koch , Theresa Ziemke

We consider in this paper a challenging problem of simulating fluid flows, in complex multiscale media possessing multi-continuum background. As an effort to handle this obstacle, model reduction is employed. In \cite{rh2}, homogenization…

Numerical Analysis · Mathematics 2022-05-31 Jun Sur Richard Park , Siu Wun Cheung , Tina Mai , Viet Ha Hoang

Understanding the fundamental mechanisms of sediment transport, particularly those during the formation and evolution of bedforms, is of critical scientific importance and has engineering relevance. Traditional approaches of sediment…

Fluid Dynamics · Physics 2016-06-22 Rui Sun , Heng Xiao

Numerical simulations are a valuable research and layout tool for fluid flow problems, yet repeated evaluations of parametrized problems, necessary to solve optimization problems, can be very costly. One option to speed up this process is…

Fluid Dynamics · Physics 2025-02-28 Marian Staggl , Wolfgang Sanz , Paul Pieringer

We conduct a numerical study of relativistic viscous fluid dynamics in the Density Frame for one-dimensional fluid flows. The Density Frame is a formulation of relativistic viscous hydrodynamics that is first-order in time, requires no…

Learning probabilistic surrogates for partial differential equations remains challenging in data-scarce regimes: neural operators require large amounts of high-fidelity data, while generative approaches typically sacrifice resolution…

Computation · Statistics 2025-12-18 Sahil Bhola , Karthik Duraisamy
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