北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2023, Vol. 46 ›› Issue (2): 109-115.

• 模式识别与图像处理 • 上一篇    下一篇

面向单目视频的非牛顿流体仿真重建

张雅斓1,2,班晓娟3,董子瑞3   

  1. 1. 北京科技大学顺德创新学院
    2. 北京科技大学智能科学与技术学院
    3. 北京科技大学
  • 收稿日期:2022-05-28 修回日期:2022-08-22 出版日期:2023-04-28 发布日期:2023-05-14
  • 通讯作者: 张雅斓 E-mail:zhangyl@ustb.edu.cn
  • 基金资助:
    面向多元素场景的高效数据驱动流体仿真

Simulation Reconstruction of Non-Newtonian Fluids for Monocular Video

  • Received:2022-05-28 Revised:2022-08-22 Online:2023-04-28 Published:2023-05-14

摘要: 在流体仿真中,本构模型参数难以准确预估,导致仿真结果与真实视频视觉效果不一致。为缓解此问题,提出一种面向单目视频的非牛顿流体仿真重建方法。模型训练阶段以非牛顿流体仿真视频为输入,学习单帧流体仿真图像的最佳低维潜在空间表示;然后在该潜在域中进行帧间预测,采用卷积长短时记忆网络预测未来帧的潜在向量表示;最后基于逐帧潜在表示编码和帧间时序特征预测本构模型重建参数。模型验证阶段以非牛顿流体单目真实视频为输入,预测流体本构模型参数,实现基于Cross模型的非牛顿流体仿真重建。实验结果表明,面向视频的仿真重建方法能够比基于流变仪测量的重建方法获得与真实视频更吻合的流体流动现象,在不同时刻均有更高的像素准确率和像素精度率,具有更符合实际流动的视觉效果。

关键词: 视频时空特征, 数据驱动模型, 仿真重建, 非牛顿流体

Abstract: In fluid simulation, the parameters of the constitutive model are difficult to predict accurately, which leads to the inconsistency between the simulation results and the visual effects of real videos. To solve this problem, a real video-oriented non-Newtonian fluid simulation reconstruction method is proposed. The model training phase takes non-Newtonian fluid simulation videos as input and learns the best low-dimensional latent space representation of a single frame of fluid simulation images. Inter-frame prediction is then performed in this latent domain, employing a convolutional long-short-term memory network to predict latent vector representations of future frames. Finally, the reconstruction parameters of the constitutive model are predicted based on the frame-by-frame latent representation encoding and inter-frame temporal features. In the model verification stage, the real video of the non-Newtonian fluid is used as the input to predict the parameters of the fluid constitutive model, and realize the simulation and reconstruction of the non-Newtonian fluid based on the Cross model.The experimental results show that the video-oriented simulation reconstruction method can obtain the fluid flow phenomenon more consistent with the real video than the reconstruction method based on the rheometer measurement. The proposed method has higher pixel accuracy and pixel accuracy at different times, and has a visual effect that is more in line with the actual flow.

Key words: video spatiotemporal features, data-driven model, simulation reconstruction, non-Newtonian fluid

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