Ghost particle suppression multiplicative algebraic reconstruction technique for tomographic PIV

Abstract

The exponential distribution law of the intensity of tomographic particle image velocimetry (Tomo-PIV) reconstructed particles is validated through a probabilistic approach. Moreover, a new Tomo-PIV particle reconstruction method is proposed based on the intensity distribution law of ghost particles and the self-similarity of true particles. In this method, ghost particles are treated as reconstruction noise. Furthermore, a combination of the variational denoising method and the inverse diffusion equation with a regularization constraint is used to suppress ghost particles. This method is called the ghost particle suppression multiplicative algebraic reconstruction technique (GS-MART). The proposed algorithm was evaluated numerically on cylindrical wake simulation data, and the reconstruction quality, intensity distribution of true particles and ghost particles, and velocity calculation accuracy were analyzed under different particle densities. To validate the effectiveness of GS-MART in real flow field measurement applications, we conducted an experiment on jet flow. The findings demonstrated that the high-precision 3D particle reconstruction achieved by GS-MART significantly enhanced the accuracy of the velocity field estimation.