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计算机工程 ›› 2011, Vol. 37 ›› Issue (15): 10-13. doi: 10.3969/j.issn.1000-3428.2011.15.003

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稳定且维护细节的液体与烟雾模拟算法

董泽建2,杨 猛1,3,刘晓君2   

  1. (1. 中国科学院计算技术研究所智能信息处理重点实验室,北京 100190; 2. 山东丝绸纺织职业学院信息工程系,山东 淄博 255300;3. 中国科学院研究生院,北京 100190)
  • 收稿日期:2011-01-14 出版日期:2011-08-05 发布日期:2011-08-05
  • 作者简介:董泽建(1981-),男,硕士研究生,主研方向:计算机 辅助设计,计算机图形学;杨 猛,博士研究生;刘晓君,硕士 研究生

Stable and Detail-preserving Liquid and Smoke Simulation Algorithm

DONG Ze-jian  2, YANG Meng  1,3, LIU Xiao-jun  2   

  1. (1. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; 2. Department of Information Engineering, Shandong Silk Textile Vocational College, Zibo 255300, China; 3. Graduate University of Chinese Academy of Sciences, Beijing 100190, China)
  • Received:2011-01-14 Online:2011-08-05 Published:2011-08-05

摘要:

半拉格朗日方法在欧拉模型的液体与烟雾模拟中,因均值运算过多而存在数值粘性的问题,且表面提取算法会抹平液体表面的细节。为此,提出一种稳定且维护细节的液体与烟雾模拟算法。采用精确的数值方法求解对流项,在流体的边界及内部分布隐式的无质量粒子,利用简单的插值策略以及粒子和网格的双向影响,保证数值的稳定性,减少数值粘性,并结合基于显式曲面机制的表面追踪方法来实现丰富的视觉效果。实验结果证明该算法具有维护细节的能力。

关键词: 流体模拟, 对流项, 数值粘性, 半拉格朗日方法

Abstract:

In liquid and smoke simulation using Euler model, the common Semi-Lagrangian Method(SLM) has numerical viscosity due to excessive averaging steps, and the surface extracting method will further smear out details of water surface. This paper proposes a stable and detail-preserving liquid and smoke simulation algorithm. It employs numerical method with higher numerical accuracy to solve advection term. The algorithm distributes massless implicit particles near the surface and in the fluid volume, using simple interpolating strategy and utilizing the bidirectional influence between particles and grid, achieving the target of guaranteeing numerical stability and reducing numerical viscosity. It combines with surface tracking method based on explicit surface to achieve rich visual effects. Experimental results demonstrate the algorithm has the capability of preserving details.

Key words: fluid simulation, advection term, numerical viscosity, Semi-Lagrangian Method(SLM)

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