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Accurately predicting the future fluid is vital to extensive areas such as meteorology, oceanology, and aerodynamics. However, since the fluid is usually observed from the Eulerian perspective, its moving and intricate dynamics are…

Machine Learning · Computer Science 2024-11-05 Qilong Ma , Haixu Wu , Lanxiang Xing , Shangchen Miao , Mingsheng Long

We present a new magnetohydrodynamic (MHD) code for the simulation of wave propagation in the solar atmosphere, under the effects of electrical resistivity, but not dominant, and heat transference in a uniform 3D grid. The code is based on…

Solar and Stellar Astrophysics · Physics 2017-08-02 Anamaría Navarro , F. D. Lora-Clavijo , Guillermo A. González

We present and test a new, special-relativistic formulation of Smoothed Particle Hydrodynamics (SPH). Our approach benefits from several improvements with respect to earlier relativistic SPH formulations. It is self-consistently derived…

High Energy Astrophysical Phenomena · Physics 2015-05-13 S. Rosswog

Stochastic hydrodynamics provides a dynamical framework for the evolution of fluctuations in heavy-ion collisions, but poses significant challenges in numerical simulations. We present an algorithm for the simulation of non-relativistic…

Nuclear Theory · Physics 2026-02-03 Mattis Harhoff , Sören Schlichting , Lorenz von Smekal

Hydrodynamic equations for a one-component plasma are derived as a generalization of the Euler equations to include the effects of the long-range Coulomb interaction. By using a variational principle, these equations self-consistently unify…

Plasma Physics · Physics 2024-03-20 Daniels Krimans , Seth Putterman

We present a computational framework for modeling large-scale particle-laden flows in complex domains with the goal of enabling simulations in medical-image derived patient specific geometries. The framework is based on a volume-filtered…

Fluid Dynamics · Physics 2023-11-28 Abhilash Reddy Malipeddi , C. Alberto Figueroa , Jesse Capecelatro

This study aims to apply statistical learning, specifically Bayesian inference, to the (3+1)D SMASH-vHLLE-hybrid model using initial conditions generated by the SMASH transport code itself, with the objective of constraining model…

Nuclear Theory · Physics 2025-05-19 Niklas Götz , Iurii Karpenko , Hannah Elfner

We consider the compressible Euler equations with potential temperature transport, a system widely used in atmospheric modelling to describe adiabatic, inviscid flows. In the low Mach number regime, the equations become stiff and pose…

Numerical Analysis · Mathematics 2025-08-22 K. R. Arun , Rahuldev Ghorai

We propose a novel Particle Flow Map (PFM) method to enable accurate long-range advection for incompressible fluid simulation. The foundation of our method is the observation that a particle trajectory generated in a forward simulation…

Graphics · Computer Science 2024-05-17 Junwei Zhou , Duowen Chen , Molin Deng , Yitong Deng , Yuchen Sun , Sinan Wang , Shiying Xiong , Bo Zhu

In this paper, a three-dimensional numerical solver is developed for suspensions of rigid and soft particles and droplets in viscoelastic and elastoviscoplastic (EVP) fluids. The presented algorithm is designed to allow for the first time…

Flows in which the primary features of interest do not rely on high-frequency acoustic effects, but in which long-wavelength acoustics play a nontrivial role, present a computational challenge. Integrating the entire domain with…

Numerical Analysis · Mathematics 2018-08-09 Emmanuel Motheau , Max Duarte , Ann Almgren , John B. Bell

We propose a low Mach number, Godunov-type finite volume scheme for the numerical solution of the compressible Euler equations of gas dynamics. The scheme combines Klein's non-stiff/stiff decomposition of the fluxes (J. Comput. Phys.…

Numerical Analysis · Mathematics 2014-12-05 Sebastian Noelle , Georgij Binev , K. R. Arun , Maria Lukáčová-Medviďová , Claus-Dieter Munz

Large displacement optical flow is an integral part of many computer vision tasks. Variational optical flow techniques based on a coarse-to-fine scheme interpolate sparse matches and locally optimize an energy model conditioned on colour,…

Computer Vision and Pattern Recognition · Computer Science 2022-06-28 Qiao Chen , Charalambos Poullis

The aim of this paper is to introduce a new computational fluid dynamics method to be called unsmoothed particle hydrodynamics SPH$-i$ which makes few assumptions and makes no assumption beyond the Navier-Stokes equations. The most…

Fluid Dynamics · Physics 2018-08-01 Kalale Chola

We present novel numerical schemes for ideal magnetohydrodynamic (MHD) simulations aimed at enhancing stability against numerical shock instability and improving the accuracy of low-speed flows in multidimensions. Stringent benchmark tests…

Fluid Dynamics · Physics 2024-01-22 Takashi Minoshima

We present a moving control volume (CV) approach to computing hydrodynamic forces and torques on complex geometries. The method requires surface and volumetric integrals over a simple and regular Cartesian box that moves with an arbitrary…

Numerical Analysis · Mathematics 2017-09-13 Nishant Nangia , Hans Johansen , Neelesh A. Patankar , Amneet Pal Singh Bhalla

In this paper, we present a general approach to obtain numerical schemes with good mesh properties for problems with moving boundaries, that is for evolving submanifolds with boundaries. This includes moving domains and surfaces with…

Numerical Analysis · Mathematics 2016-09-13 Charles M. Elliott , Hans Fritz

We describe a newly developed hydrodynamic code for studying accretion disk processes. The numerical method uses a finite volume, nonlinear, Total Variation Diminishing (TVD) scheme to capture shocks and control spurious oscillations. It is…

Astrophysics · Physics 2008-12-18 L. R. Mudryk , N. W. Murray

Modeling and direct numerical simulation of particle-laden flows have a tremendous variety of applications in science and engineering across a vast spectrum of scales from pollution dispersion in the atmosphere, to fluidization in the…

In this paper, we propose a novel Gaussian process-based moving horizon estimation (MHE) framework for unknown nonlinear systems. On the one hand, we approximate the system dynamics by the posterior means of the learned Gaussian processes…

Systems and Control · Electrical Eng. & Systems 2025-07-01 Tobias M. Wolff , Victor G. Lopez , Matthias A. Müller
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