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Preserving Fluid Sheets with Adaptively Sampled Anisotropic Particles

机译:用自适应采样的各向异性粒子保存流体板

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This paper presents a particle-based model for preserving fluid sheets of animated liquids with an adaptively sampled Fluid-Implicit-Particle (FLIP) method. In our method, we preserve fluid sheets by filling the breaking sheets with particle splitting in the thin regions, and by collapsing them in the deep water. To identify the critically thin parts, we compute the anisotropy of the particle neighborhoods, and use this information as a resampling criterion to reconstruct thin liquid surfaces. Unlike previous approaches, our method does not suffer from diffusive surfaces or complex remeshing operations, and robustly handles topology changes with the use of a meshless representation. We extend the underlying FLIP model with an anisotropic position correction to improve the particle spacing, and adaptive sampling to efficiently perform simulations of larger volumes. Due to the Lagrangian nature of our method, it can be easily implemented and efficiently parallelized. The results show that our method can produce visually complex liquid animations with thin structures and vivid motions.
机译:本文提出了一种基于粒子的模型,用于通过自适应采样的流体隐含粒子(FLIP)方法来保留动画液体的流体表。在我们的方法中,我们通过在破碎的薄片中填充细小区域的裂痕并在深水中塌陷来保存流体薄片。为了确定临界薄部分,我们计算了粒子邻域的各向异性,并将此信息用作重采样标准以重建薄液体表面。与以前的方法不同,我们的方法不会受到扩散表面或复杂的重新网格化操作的困扰,并且可以使用无网格表示来可靠地处理拓扑更改。我们使用各向异性位置校正扩展了基础FLIP模型,以改善粒子间距,并通过自适应采样有效地执行较大体积的模拟。由于我们方法的拉格朗日性质,因此可以轻松实现并高效并行化。结果表明,我们的方法可以产生视觉上复杂的液体动画,具有薄的结构和生动的动作。

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