Highly adaptive liquid simulations on tetrahedral meshes

Highly adaptive liquid simulations on tetrahedral meshes
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DOI:
10.1145/2461912.2461982
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发表时间:
2013-07
期刊:
ACM Transactions on Graphics (TOG)
影响因子:
--
通讯作者:
R. Ando;N. Thürey;C. Wojtan
R. Ando;N. Thürey;C. Wojtan
中科院分区:
其他
文献类型:
--
作者:
R. Ando;N. Thürey;C. Wojtan

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我们介绍了一种新的方法来有效地模拟具有极端空间适应性的液体。我们的方法结合了几个关键组件,大大加快了大规模流体现象的模拟:我们利用另一种欧拉四面体网格离散化来显著降低压力求解的复杂性,同时增加了元素质量的鲁棒性,并消除了锁定的可能性。接下来,我们通过推导与我们的离散化一致的新的二阶边界条件来实现微妙的自由表面现象。我们将这种离散化与空间自适应流体隐式粒子(FLIP)方法相结合,实现了高效、鲁棒、最小耗散的模拟,可以在空间分辨率发生急剧变化的同时最大限度地减少伪影。在此过程中,我们提供了一种新的方法,可以从一组可变大小的颗粒中生成光滑和详细的表面。最后,我们探讨了用于确定空间自适应仿真分辨率的几个新的大小函数,并展示了如何将它们耦合到我们的模拟器。我们将这些元素结合在一起,生成一个模拟算法,该算法能够在高最大分辨率下创建动画,同时避免不准确的边界条件和低效的计算等常见缺陷。
We introduce a new method for efficiently simulating liquid with extreme amounts of spatial adaptivity. Our method combines several key components to drastically speed up the simulation of large-scale fluid phenomena: We leverage an alternative Eulerian tetrahedral mesh discretization to significantly reduce the complexity of the pressure solve while increasing the robustness with respect to element quality and removing the possibility of locking. Next, we enable subtle free-surface phenomena by deriving novel second-order boundary conditions consistent with our discretization. We couple this discretization with a spatially adaptive Fluid-Implicit Particle (FLIP) method, enabling efficient, robust, minimally-dissipative simulations that can undergo sharp changes in spatial resolution while minimizing artifacts. Along the way, we provide a new method for generating a smooth and detailed surface from a set of particles with variable sizes. Finally, we explore several new sizing functions for determining spatially adaptive simulation resolutions, and we show how to couple them to our simulator. We combine each of these elements to produce a simulation algorithm that is capable of creating animations at high maximum resolutions while avoiding common pitfalls like inaccurate boundary conditions and inefficient computation.