A momentum preserving frictional contact algorithm based on affine particle-in-cell grid transfers

Tupek, Michael R.; Koester, Jacob; Mosby, Matthew

doi:10.48550/arxiv.2108.02259

Cited by 1 publication

(1 citation statement)

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“…In the Euler-Lagrange hybrid model, the pressure gradient forces acting on grid points are determined by the surrounding particle pressures [27]. In Figure 3, green squares represent the centers of grid points, while colored dots represent Lagrange particles.…”

Section: (I) Deformation Calculationmentioning

confidence: 99%

Applicability Study of Euler–Lagrange Integration Scheme in Constructing Small-Scale Atmospheric Dynamics Models

Wei,

Liu,

Guo

et al. 2024

Atmosphere

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The atmospheric flow field and weather processes exhibit complex and variable characteristics at small scales, involving interactions between terrain features and atmospheric physics. To investigate the mechanisms of these process further, this study employs a Lagrangian particle motion model combined with a Euler background field approach to construct a small-scale atmospheric flow field model. The model streamlines the modeling process by combining the benefits of the Lagrangian dynamics model and the Eulerian integration scheme. To verify the effectiveness of the Euler–Lagrange hybrid model, experiments using the Fluent wind field model were conducted for comparison. The results show that both models have their advantages in handling terrain-induced wind fields. The Fluent model excels in simulating the general characteristics of wind fields under specific terrain, while the Euler–Lagrange hybrid model is better at capturing the upstream and downstream disturbances of the terrain on the atmospheric flow field. These findings provide powerful tools for in-depth diagnostic analysis of atmospheric flow simulation and convective precipitation processes. Notably, the Euler–Lagrange hybrid model demonstrates excellent computational efficiency, with an average computation time of approximately 2 s per time step in a Python environment, enabling rapid simulation of 40 time steps within approximately 90 s.

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Section: (I) Deformation Calculationmentioning

confidence: 99%