Preserving Fluid Sheets with Adaptively Sampled Anisotropic Particles
Preserving Fluid Sheets with Adaptively Sampled Anisotropic Particles
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DOI：
10.1109/tvcg.2012.87
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发表时间：
2012-08
影响因子：
5.2
通讯作者：
R. Ando;N. Thürey;R. Tsuruno
中科院分区：
文献类型：
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作者：
R. Ando;N. Thürey;R. Tsuruno
本文提出了一种基于粒子的模型，采用自适应采样流体隐式粒子（FLIP）方法来保持动画液体的流体面。在我们的方法中，我们通过在薄区域填充粒子分裂的破裂片，并通过在深水中使其塌陷来保持流体片。为了识别临界薄的部分，我们计算的颗粒邻域的各向异性，并使用此信息作为一个rescovery标准，以重建薄的液体表面。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，并鲁棒地处理拓扑结构的变化与使用的无网格表示。我们扩展了底层FLIP模型，使用各向异性位置校正来改善粒子间距，并使用自适应采样来有效地执行更大体积的模拟。由于我们的方法的拉格朗日性质，它可以很容易地实现和有效地并行化。实验结果表明，该方法可以生成结构精简、运动逼真的复杂流体动画。
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.