Particle image velocimetry combining unsupervised learning and optical flow model

Abstract

Particle Image Velocimetry (PIV) is a multi-point, transient, contactless flow field measurement technique which can obtain accurate data information for the flow field. Particle image velocimetry technology based on deep learning has been widely used, with higher measurement accuracy and testing efficiency than traditional algorithms. Algorithms based on supervised learning need real data for network training. However, obtaining real flow field data is difficult, so unsupervised learning methods are more suitable for network training. The LiteFlowNet3 network has only four layers of flow inference, leading to limited reconstruction accuracy, especially for complex flow field. This paper adds one layer of flow inference based on the original network, while increasing the last layer of the loss. The network is trained using an unsupervised learning method, focusing on improving reconstruction accuracy for real-flow field training. The experimental results show that the improved model can reduce the average endpoint error by up to 30.89 % and 13.33 % compared to the original model and the unsupervised LiteFlowNet. In addition, by building a two-dimensional PIV system to simulate the advection and vortex field, real particle image pairs were obtained and put into the trained model for testing, proving the feasibility for obtaining velocity fields in real flow field.