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相关论文: Smoothed Particle Hydrodynamics

200 篇论文

We employ a multi-phase smoothed particle hydrodynamics (SPH) method to study droplet dynamics in shear flow. With an extensive range of Reynolds number, capillary number, wall confinement, and density/viscosity ratio between the droplet…

流体动力学 · 物理学 2023-07-07 Kuiliang Wang , Hong Liang , Chong Zhao , Xin Bian

The interaction of liquid drops and heated surfaces is of great importance in many applications. This paper describes a numerical method, based on smoothed particle hydrodynamics (SPH), for simulating n-heptane drop impact on a heated…

流体动力学 · 物理学 2018-08-14 Xiufeng Yang , Manjil Ray , Song-Charng Kong , Chol-Bum M. Kweon

To simulate the dynamics of fluid with polydisperse particles on macroscale level, one has to solve hydrodynamic equations with several relaxation terms, representing momentum transfer from fluid to particles and vice versa. For small…

To make relevant predictions about observable emission, hydrodynamical simulation codes must employ schemes that account for radiative losses, but the large dimensionality of accurate radiative transfer schemes is often prohibitive.…

天体物理仪器与方法 · 物理学 2015-06-23 James C. Lombardi , William G. McInally , Joshua A. Faber

We present a new hybrid Smoothed Particle Hydrodynamics (SPH)/N-body method for modelling the collisional stellar dynamics of young clusters in a live gas background. By deriving the equations of motion from Lagrangian mechanics we obtain a…

天体物理仪器与方法 · 物理学 2015-06-12 D. A. Hubber , R. J. Allison , R. Smith , S. P. Goodwin

Natural convection is of great importance in many engineering applications. This paper presents a smoothed particle hydrodynamics (SPH) method for natural convection. The conservation equations of mass, momentum and energy of fluid are…

流体动力学 · 物理学 2019-04-16 Xiufeng Yang , Song-Charng Kong

Smoothed particle hydrodynamics (SPH) employs an artificial viscosity to properly capture hydrodynamical shock waves. In its original formulation, the resulting numerical viscosity is large enough to suppress structure in the velocity field…

天体物理学 · 物理学 2009-11-13 K. Dolag , F. Vazza , G. Brunetti , G. Tormen

Silent data corruptions (SDCs) hinder the correctness of long-running scientific applications on large scale computing systems. Selective particle replication (SPR) is proposed herein as the first particle-based replication method for…

分布式、并行与集群计算 · 计算机科学 2020-05-19 Aurélien Cavelan , Rubén M. Cabezón , Florina M. Ciorba

We present pkdgrav3, a high-performance, fully parallel tree-SPH code designed for large-scale hydrodynamic simulations including self-gravity. Building upon the long development history of pkdgrav, the code combines an efficient…

地球与行星天体物理 · 物理学 2026-05-14 Thomas Meier , Douglas Potter , Christian Reinhardt , Joachim Stadel

We present TRAPHIC, a novel radiative transfer scheme for Smoothed Particle Hydrodynamics (SPH) simulations. TRAPHIC is designed for use in simulations exhibiting a wide dynamic range in physical length scales and containing a large number…

天体物理学 · 物理学 2009-06-23 Andreas H. Pawlik , Joop Schaye

We present the methodology and performance of the new Lagrangian hydrodynamics code MAGMA2, a Smoothed Particle Hydrodynamics code that benefits from a number of non-standard enhancements. By default it uses high-order smoothing kernels and…

天体物理仪器与方法 · 物理学 2020-09-02 Stephan Rosswog

Smoothed Dissipative Particle Dynamics (SDPD) is a mesoscopic method which allows to select the level of resolution at which a fluid is simulated. In this work, we study the consistency of the resulting thermodynamic properties as a…

统计力学 · 物理学 2016-10-19 G. Faure , J. Roussel , J. -B. Maillet , G. Stoltz

In this paper we investigate the use of the vector potential as a means of maintaining the divergence constraint in the numerical solution of the equations of Magnetohydrodynamics (MHD) using the Smoothed Particle Hydrodynamics (SPH)…

天体物理仪器与方法 · 物理学 2015-05-14 Daniel J. Price

In this paper we present solutions to three short comings of Smoothed Particles Hydrodynamics (SPH) encountered in previous work when applying it to Giant Impacts. First we introduce a novel method to obtain accurate SPH representations of…

地球与行星天体物理 · 物理学 2017-03-29 Christian Reinhardt , Joachim Stadel

This paper describes an energy-preserving and globally time-reversible code for weakly compressible smoothed particle hydrodynamics (SPH). We do not add any additional dynamics to the Monaghan's original SPH scheme at the level of ordinary…

数值分析 · 数学 2022-12-14 Ondrej Kincl , Michal Pavelka

In this paper we introduce the concept of Direct Statistical Simulation (DSS) for astrophysical flows. This technique may be appropriate for problems in astrophysical fluids where the instantaneous dynamics of the flows are of secondary…

太阳与恒星天体物理 · 物理学 2011-01-17 S. M. Tobias , K. Dagon , J. B. Marston

We present diffSPH, a novel open-source differentiable Smoothed Particle Hydrodynamics (SPH) framework developed entirely in PyTorch with GPU acceleration. diffSPH is designed centrally around differentiation to facilitate optimization and…

流体动力学 · 物理学 2025-07-30 Rene Winchenbach , Nils Thuerey

This paper presents a novel method for smoothed particle hydrodynamics (SPH) with thin-walled structures. Inspired by the direct forcing immersed boundary method, this method employs a moving least square method to guarantee the smoothness…

流体动力学 · 物理学 2023-10-10 ZhuoLin Wang , Zichao Jiang , Yi Zhang , Gengchao Yang , Trevor Hocksun Kwan , Yuhui Chen , Qinghe Yao

The incompressible smoothed particle hydrodynamics method (ISPH) is a numerical method widely used for accurately and efficiently solving flow problems with free surface effects. However, to date there has been little mathematical…

数值分析 · 数学 2019-07-03 Y. Imoto

This paper proposes the first free-stream boundary condition in a purely Lagrangian framework for weakly-compressible smoothed particle hydrodynamics (WCSPH). The boundary condition is implemented based on several numerical techniques,…

流体动力学 · 物理学 2023-07-04 Shuoguo Zhang , Wenbin Zhang , Chi Zhang , Xiangyu Hu