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We develop numerical methods for reaction-diffusion systems based on the equations of fluctuating hydrodynamics (FHD). While the FHD formulation is formally described by stochastic partial differential equations (SPDEs), it becomes similar…

Fluid Dynamics · Physics 2018-01-17 Changho Kim , Andy Nonaka , John B. Bell , Alejandro L. Garcia , Aleksandar Donev

We investigate hydrodynamic noise in a dilute granular gas during the homogeneous cooling state, by means of a proper application of the Direct Simulation Monte Carlo (DSMC) algorithm. The DSMC includes a source of randomization which is…

Soft Condensed Matter · Physics 2010-09-27 Giulio Costantini , Andrea Puglisi

We analyze the transport properties of a low density ensemble of identical macroscopic particles immersed in an active fluid. The particles are modeled as inelastic hard spheres (granular gas). The non-homogeneous active fluid is modeled by…

Soft Condensed Matter · Physics 2017-10-17 Francisco Vega Reyes , Antonio Lasanta

A machine-learning strategy for investigating the stability of fluid flow problems is proposed herein. The goal is to provide a simple yet robust methodology to find a nonlinear mapping from the parametric space to an indicator representing…

Fluid Dynamics · Physics 2026-01-06 David J. Silvester

We analyze the mixed frame equations of radiation hydrodynamics under the approximations of flux-limited diffusion and a thermal radiation field, and derive the minimal set of evolution equations that includes all terms that are of leading…

Astrophysics · Physics 2009-11-11 Mark R. Krumholz , Richard I. Klein , Christopher F. McKee , John Bolstad

Lagrangian particle methods based on detailed atomic and molecular models are powerful computational tools for studying the dynamics of microscale and nanoscale systems. However, the maximum time step is limited by the smallest oscillation…

Computational Physics · Physics 2019-06-26 Ansel L. Blumers , Zhen Li , George Em Karniadakis

The equations of relativistic hydrodynamics are transformed so that steps forward in time preserves local simultaneity. In these variables, the space-time coordinates of neighboring points on the mesh are simultaneous according to co-moving…

Nuclear Theory · Physics 2008-11-26 Scott Pratt

Simulating turbulent fluid flows is a computationally prohibitive task, as it requires the resolution of fine-scale structures and the capture of complex nonlinear interactions across multiple scales. This is particularly the case in direct…

Fluid Dynamics · Physics 2026-04-22 Ismaël Zighed , Nicolas Thome , Patrick Gallinari , Taraneh Sayadi

A particular type of random dynamical processes is considered, in which the stochasticity is introduced through randomly fluctuating parameters. A method of local multipliers is developed for treating the local stability of such dynamical…

Disordered Systems and Neural Networks · Physics 2015-06-25 V. I. Yukalov

We introduce a new algorithm for the simulation of Euclidean dynamical triangulations that mimics the Metropolis-Hastings algorithm, but where all proposed moves are accepted. This rejection-free algorithm allows for the factorization of…

High Energy Physics - Lattice · Physics 2023-09-22 Mingwei Dai , Walter Freeman , Jack Laiho , Marc Schiffer , Judah Unmuth-Yockey

We present an alternative method to estimate the numerical viscosity in simulations of astrophysical objects, which is based in the damping of fluid oscillations. We apply the method to general relativistic hydrodynamic simulations using a…

Solar and Stellar Astrophysics · Physics 2014-11-20 Pablo Cerdá-Durán

Hydrodynamic fluctuations in simple fluids under shear flow are demonstrated to be spatially correlated, in contrast to the fluctuations at equilibrium, using mesoscopic hydrodynamic simulations. The simulation results for the equal-time…

Soft Condensed Matter · Physics 2024-06-03 Anoop Varghese , Gerhard Gompper , Roland G. Winkler

We describe some general results that constrain the dynamical fluctuations that can occur in non-equilibrium steady states, with a focus on molecular dynamics. That is, we consider Hamiltonian systems, coupled to external heat baths, and…

Statistical Mechanics · Physics 2017-11-22 Robert L. Jack , Marcus Kaiser , Johannes Zimmer

Mathematical modeling of fluid dynamics for computer graphics requires high levels of theoretical rigor to ensure visually plausible and computationally efficient simulations. This paper presents an in-depth theoretical framework analyzing…

Fluid Dynamics · Physics 2024-11-05 Rômulo Damasclin Chaves dos Santos

We present a new mesoscale model for ionic liquids based on a low Mach number fluctuating hydrodynamics formulation for multicomponent charged species. The low Mach number approach eliminates sound waves from the fully compressible…

Fluid Dynamics · Physics 2020-09-23 Katherine Klymko , Sean P. Carney , Andrew Nonaka , Alejandro L. Garcia , John B. Bell

Fluid flow simulation is a highly active area with applications in a wide range of engineering problems and interactive systems. Meshless methods like the Moving Particle Semi-implicit (MPS) are a great alternative to deal efficiently with…

We propose and analyse a novel, fully discrete numerical algorithm for the approximation of the generalised Stokes system forced by transport noise -- a prototype model for non-Newtonian fluids including turbulence. Utilising the Gradient…

Numerical Analysis · Mathematics 2024-12-20 Jerome Droniou , Kim-Ngan Le , Jörn Wichmann

Viscoelastic flows occur widely, and numerical simulations of them are important for a range of industrial applications. Simulations of viscoelastic flows are more challenging than their Newtonian counterparts due to the presence of…

Fluid Dynamics · Physics 2022-06-06 Jack King , Steven Lind

We examine the question of whether the formal expressions of equilibrium statistical mechanics can be applied to time independent non-dissipative systems that are not in true thermodynamic equilibrium and are nonergodic. By assuming the…

Statistical Mechanics · Physics 2007-11-09 Stephen R. Williams , Denis J. Evans

Many time series are effectively generated by a combination of deterministic continuous flows along with discrete jumps sparked by stochastic events. However, we usually do not have the equation of motion describing the flows, or how they…

Machine Learning · Computer Science 2020-01-09 Junteng Jia , Austin R. Benson
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