系统仿真学报 ›› 2023, Vol. 35 ›› Issue (3): 435-453.doi: 10.16182/j.issn1004731x.joss.22-1507
• • 下一篇
出版日期:
2023-03-30
发布日期:
2023-03-22
Jingyuan Zhu1(), Huimin Ma2(
), Jian Yuan1
Online:
2023-03-30
Published:
2023-03-22
Contact:
Huimin Ma
E-mail:jy-zhu20@mails.tsinghua.edu.cn;mhmpub@ustb.edu.cn
About author:
Zhu Jingyuan(1999-), male, Manchu, doctoral student, research area:computer graphics and computer vision. E-mail:jy-zhu20@mails.tsinghua.edu.cn
Supported by:
摘要:
快速准确的进行流体仿真是一个具有挑战性的任务,基于传统方法的流体仿真需要消耗大量的计算资源以获得准确的结果。深度学习方法快速发展,为基于数据的流体仿真和生成提供了可能。提出一种基于单帧的浓度场和一个序列的先验速度场的长时序流体仿真运动预测算法。这一模型专注于将通过神经网络预测的速度和密度场基于纳维-斯托克斯方程获得的仿真数据在宏观尺度上进行匹配。通过使用基于全卷积U型网络的自动编码器和基于LSTM网络的时序预测子网络,本文模型在时间演化过程中更好地保持了视觉宏观相似性,并实现了显著的计算速度提升。本文方法实现了对流体场演化的宏观分布的准确和快速的长时序运动预测。在一系列二维和三维的仿真数据的基准测试上证明了本文算法的有效性和高效性。
中图分类号:
. 学习流体仿真中的高效长时序运动预测[J]. 系统仿真学报, 2023, 35(3): 435-453.
Jingyuan Zhu, Huimin Ma, Jian Yuan. Learning-Based High-Performance Algorithm for Long-Term Motion Prediction of Fluid Flows[J]. Journal of System Simulation, 2023, 35(3): 435-453.
Scene Type | Resolution | Scene | Frames |
---|---|---|---|
Single source smoke (3D) | 643 | 600 | 100 |
Single source smoke with obstacles (3D) | 643 | 600 | 200 |
Single source smoke (2D) | 642 | 600 | 200 |
Single source smoke (2D) | 1282 | 600 | 200 |
Single source smoke with obstacles (2D) | 642 | 600 | 200 |
Single source smoke with obstacles (2D) | 1282 | 600 | 200 |
Rising smoke (2D) | 642 | 1 000 | 200 |
Rising smoke(2D) | 1282 | 1 000 | 200 |
Rotating cup (2D) | 642 | 600 | 200 |
Rotating and moving cup (2D) | 642 | 300 | 300 |
Datasets | Resolution | Long-term velocity | Long-term density | ||||
---|---|---|---|---|---|---|---|
PSNR | Cos similarity | MSE | PSNR | Cos similarity | MSE | ||
Single source smoke (3D) | 643 | 41.123 | 0.997 | 0.002 2 | 26.279 | 0.889 | 0.002 6 |
Single source smoke with obstacles (3D) | 643 | 32.289 | 0.993 | 0.006 6 | 35.717 | 0.993 | 0.000 2 |
Rotating cup (2D) | 642 | 20.430 | 0.835 | 0.008 0 | 23.244 | 0.970 | 0.002 9 |
Rotating and moving cup (2D) | 642 | 20.279 | 0.863 | 0.030 8 | 25.579 | 0.972 | 0.003 8 |
Single source smoke (2D) | 642 | 23.627 | 0.969 | 0.084 0 | 28.212 | 0.985 | 0.003 8 |
Single source smoke (2D) | 1282 | 25.024 | 0.976 | 0.264 8 | 27.377 | 0.978 | 0.006 4 |
Single source smoke with obstacles (2D) | 642 | 23.810 | 0.966 | 0.070 8 | 28.866 | 0.986 | 0.003 4 |
Single source smoke with obstacles (2D) | 1282 | 27.098 | 0.960 | 0.095 4 | 29.092 | 0.954 | 0.003 8 |
Datasets | Resolution | Prediction with original velocity fields | Prediction with predicted velocity fields | ||||
---|---|---|---|---|---|---|---|
PSNR | Cos similarity | MSE | PSNR | Cos similarity | MSE | ||
Single source smoke (3D) | 643 | 26.279 | 0.889 | 0.003 | 26.738 | 0.865 | 0.004 |
Single source smoke with obstacles (3D) | 643 | 35.717 | 0.993 | 0.000 2 | 35.340 | 0.989 | 0.000 02 |
Single source smoke (2D) | 642 | 28.621 | 0.990 | 0.002 | 17.904 | 0.842 | 0.023 |
Single source smoke (2D) | 1282 | 25.886 | 0.984 | 0.003 | 18.418 | 0.886 | 0.019 |
Rising smoke (2D) | 642 | 18.465 | 0.895 | 0.009 | 17.760 | 0.875 | 0.010 |
Rising smoke (2D) | 1282 | 19.483 | 0.897 | 0.019 | 17.717 | 0.837 | 0.029 |
Datasets | Resolution | Velocity prediction/ms | Density prediction/ms |
---|---|---|---|
Single source smoke (3D) | 643 | 25.80 | 24.14 |
Single source smoke with obstacles (3D) | 643 | 25.43 | 23.66 |
Rotating cup (2D) | 642 | 0.59 | 0.41 |
Rotating and moving cup (2D) | 642 | 0.84 | 0.52 |
Rising smoke (2D) | 642 | 0.56 | 0.66 |
Rising smoke (2D) | 1282 | 0.92 | 1.02 |
Single source smoke (2D) | 642 | 0.66 | 0.43 |
Single source smoke (2D) | 1282 | 1.06 | 0.89 |
Single source smoke with obstacles (2D) | 642 | 0.65 | 0.43 |
Single source smoke with obstacles (2D) | 1282 | 1.03 | 0.81 |
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