26 November 2001 Stereoscopic tracking velocimetry based on neural networks for particle tracking
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Abstract
Stereoscopic tracking velocimetry (STV) can be a very efficient diagnostics tool for detecting three-dimensional three-component flows with great experimental freedom and computational processing speed but for a restricted region. To achieve the goal of near-real-time measurement with reasonable measurement accuracy, a particle tracking algorithm has been developed, which is an essential part of STV. The developed particle tracking is based on an optimization approach, hence it is a good candidate to be solved by applying computational neural networks. In this paper, we present the new tracking algorithm and its measurement applications to the material processing involving directional solidification as well as to a pulsating free-jet flow. Preliminary comparison of experimental and numerical results is also presented. We believe that by utilizing the massive parallel-processing power of neural networks for optimization, reliable solutions in the STV application can be obtained for near-real-time data extraction and display.
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Yi Ge, Yi Ge, David Lee, David Lee, Soyoung Stephen Cha, Soyoung Stephen Cha, Dong-Jin Cha, Dong-Jin Cha, } "Stereoscopic tracking velocimetry based on neural networks for particle tracking", Proc. SPIE 4448, Optical Diagnostics for Fluids, Solids, and Combustion, (26 November 2001); doi: 10.1117/12.449377; https://doi.org/10.1117/12.449377
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