Particle reconstruction of volumetric particle image velocimetry with the strategy of machine learning

Three-dimensional particle reconstruction with limited two-dimensional projections is an under-determined inverse problem that the exact solution is often difficult to be obtained. In general, approximate solutions can be obtained by iterative optimization methods. In the current work, a practical p...

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Bibliographic Details
Published inAdvances in aerodynamics Vol. 3; no. 1; pp. 1 - 14
Main Authors Gao, Qi, Pan, Shaowu, Wang, Hongping, Wei, Runjie, Wang, Jinjun
Format Journal Article
LanguageEnglish
Published Singapore Springer Nature Singapore 23.09.2021
Springer Nature B.V
SpringerOpen
Subjects
Aerospace Technology and Astronautics
Algorithms
Convolutional neural network
Engineering
Ghosts
Inverse problems
Machine learning
Neural networks
Particle reconstruction
Volumetric particle image velocimetry
Particle reconstruction
Convolutional neural network
Volumetric particle image velocimetry
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Summary:Three-dimensional particle reconstruction with limited two-dimensional projections is an under-determined inverse problem that the exact solution is often difficult to be obtained. In general, approximate solutions can be obtained by iterative optimization methods. In the current work, a practical particle reconstruction method based on a convolutional neural network (CNN) with geometry-informed features is proposed. The proposed technique can refine the particle reconstruction from a very coarse initial guess of particle distribution that is generated by any traditional algebraic reconstruction technique (ART) based methods. Compared with available ART-based algorithms, the novel technique makes significant improvements in terms of reconstruction quality, robustness to noise, and at least an order of magnitude faster in the offline stage.
ISSN:2524-6992
2524-6992
DOI:10.1186/s42774-021-00087-6