Document Type : Original/Review Paper

Authors

• Hamed Modanloujouybari ¹
• Yasser Baleghi ²

¹ Department of Electrical Engineering, Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran

² Department of Electrical & Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran

Abstract

Accurate estimation of velocity fields from Particle Image Velocimetry (PIV) data is essential for fluid-flow analysis and modeling. PIV relies on Image Processing techniques such as cross-correlation and optical flow to estimate the magnitude and direction of fluid motion; however, traditional approaches often struggle in the presence of noise, sharp velocity gradients, and complex flow structures. Recent deep-learning-based methods have shown promising performance, but purely data-driven models may generate physically inconsistent predictions because they do not explicitly incorporate the governing laws of fluid dynamics. In this study, a physics-informed framework based on Recurrent All-Pairs Field Transforms (RAFT), a state-of-the-art deep neural network for dense optical flow estimation, is proposed for velocity-field reconstruction from PIV image sequences. The proposed approach introduces a novel loss function that combines a Charbonnier data term with divergence-free, vorticity-based, and edge-aware smoothness constraints to improve the physical consistency and robustness of the estimated flow fields. The method was evaluated on five benchmark PIV datasets and consistently outperformed the original RAFT model. The average endpoint error (AEE) was reduced from 16.07 to 14.19, corresponding to an improvement of approximately 11.7%, with the largest gains observed in turbulent and complex flow conditions. These results demonstrate that incorporating fluid-dynamics knowledge into deep-learning-based optical flow estimation improves the accuracy, stability, and generalization capability of PIV velocity-field reconstruction.

Keywords

• Particle Image Velocimetry
• Cross-Correlation
• Optical Flow
• Deep Learning
• Physics-informed Cost Function

Main Subjects

• H.5. Image Processing and Computer Vision