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Adaptively stepped SPH for fluid animation based on asynchronous time integration

  • Recent advances in Pattern Recognition and Artificial Intelligence
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Abstract

We present a novel adaptive stepping scheme for SPH fluids, in which particles have their own time steps determined from local conditions, e.g. courant condition. These individual time steps are constrained for global convergence and stability. Fluid particles are then updated asynchronously. The approach naturally allocates computing resources to visually complex regions, e.g. regions with intense collisions, thereby reducing the overall computational time. The experiments show that our approach is more efficient than the standard method and the method with globally adaptive time steps, especially in highly dynamic scenes.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (Nos. 61272357, 61300074, 61572075) and Fundamental Research Funds for the Central Universities (FRF-BR-15-058A).

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Authors and Affiliations

  1. School of Computer and Communication Engineering, University Science and Technology Beijing, Beijing, China

    Xiaojuan Ban, Xiaokun Wang, Liangliang He, Yalan Zhang & Lipeng Wang

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  1. Xiaojuan Ban
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  2. Xiaokun Wang
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  3. Liangliang He
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  4. Yalan Zhang
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  5. Lipeng Wang
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Correspondence to Xiaokun Wang.

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Ban, X., Wang, X., He, L. et al. Adaptively stepped SPH for fluid animation based on asynchronous time integration. Neural Comput & Applic 29, 33–42 (2018). https://doi.org/10.1007/s00521-016-2286-8

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