Journal
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Volume 69, Issue 6, Pages 3538-3554Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2019.2932649
Keywords
Convolutional neural network (CNN); deep learning; fluid motion estimation; particle image velocimetry (PIV); turbulent boundary layer
Funding
- National Natural Science Foundation of China [61473253]
- Foundation for Innovative Research Groups of the National Natural Science Foundation of China [61621002]
- Fundamental Research Funds for the Central Universities [2018XZZX001-09, 2019QNA4056]
Ask authors/readers for more resources
Particle image velocimetry (PIV), as a common technology for analyzing the global flow motion from images, plays a significant role in experimental fluid mechanics. In this article, we investigate the deep learning-based techniques for such a fluid motion estimation problem. The aim of this novel technique is to extract 2-D velocity fields from fluid images efficiently and accurately. First, we introduce the convolutional neural network (CNN) called LiteFlowNet, which is proposed for end-to-end optical flow estimation. Enhanced configurations of LiteFlowNet are adopted for PIV estimation in order to refine the small-scale vortex structures. Furthermore, as the supervised learning strategy is considered, a data set including particle images and the ground-truth fluid motions is generated to train the parameters of the networks. A number of fluidic images, from synthetic turbulent flow to laboratory boundary layer flow, are investigated in this article. Experimental results indicate that the proposed estimator can provide accuracy approaching that of state-of-the-art methods and high efficiency toward real-time estimation.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available