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AN ADAPTIVE SAMPLING APPROACH TO INCOMPRESSIBLE PARTICLE-BASED FLUID

机译：不可压缩的基于颗粒的流体的自适应采样方法

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摘要

I propose a particle-based technique for simulating incompressible uid thatincludes adaptive re nement of particle sampling. Each particle represents a massof uid in its local region. Particles are split into several particles for ner samplingin regions of complex ow. In regions of smooth ow, neghboring particles can bemerged. Depth below the surface and Reynolds number are exploited as our criteriafor determining whether splitting or merging should take place. For the uid dynamicscalculations, I use the hybrid FLIP method, which is computationally simple ande cient. Since the uid is incompressible, each particle has a volume proportional toits mass. A kernel function, whose e ective range is based on this volume, is used fortransferring and updating the particle's physical properties such as mass and velocity.In addition, the particle sampling technique is extended to a fully adaptive approach,supporting adaptive splitting and merging of uid particles and adaptive spatial samplingfor the reconstruction of the velocity and pressure elds. Particle splitting allowsa detailed sampling of uid momentum in regions of complex ow. Particle merging,in regions of smooth ow, reduces memory and computational overhead. Anoctree structure is used to compute inter-particle interactions and to compute thepressure eld. The octree supporting eld-based calculations is adapted to provide a ne spatial reconstruction where particles are small and a coarse reconstructionwhere particles are large. This scheme places computational resources where they aremost needed, to handle both ow and surface complexity. Thus, incompressibilitycan be enforced even in very small, but highly turbulent areas. Simultaneously, thelevel of detail is very high in these areas, allowing the direct support of tiny splashesand small-scale surface tension e ects. This produces a nely detailed and realisticrepresentation of surface motion.
机译：我提出了一种基于粒子的技术来模拟不可压缩的uid，其中包括粒子采样的自适应修正。每个粒子在其局部区域代表质量流体。粒子被分成几个粒子，以便在复杂流域的区域进行神经采样。在光滑的区域，可以合并附近的颗粒。表面以下的深度和雷诺数被用作我们确定是否应进行分裂或合并的标准。对于uid动力学计算，我使用混合FLIP方法，该方法在计算上简单高效。由于uid是不可压缩的，因此每个粒子的体积都与其质量成比例。有效范围基于此体积的核函数用于传输和更新粒子的物理特性（例如质量和速度）。此外，粒子采样技术已扩展为完全自适应的方法，支持对粒子的自适应拆分和合并。流体粒子和自适应空间采样以重建速度场和压力场。粒子分裂允许对复杂流动区域中的uid动量进行详细采样。平滑流区域中的粒子合并减少了内存和计算开销。 Anoctree结构用于计算粒子间的相互作用并计算压力场。基于八叉树支持场的计算适用于在粒子较小的情况下提供新的空间重构，在粒子较大的情况下提供粗略的重构。该方案将计算资源放在最需要的地方，以处理流量和表面复杂性。因此，即使在很小但高度动荡的区域中也可以实施不可压缩性。同时，这些区域的细节水平很高，可以直接支撑微小的飞溅和小范围的表面张力等。这产生了表面运动的精确详细的表示。

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  • 作者

    Hong Woo-Suck;

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  • 年度 2010
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  • 正文语种 en_US
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