Open AccessVisual inference of flow flux via free surface PDE model and image sequence assimilation
Author(s) -
Guo Shan,
Xu Chao,
Yiu Ka Fai Cedric
Publication year - 2019
Publication title -
iet cyber‐systems and robotics
Language(s) - English
Resource type - Journals
ISSN - 2631-6315
DOI - 10.1049/iet-csr.2018.0002
Subject(s) - data assimilation , assimilation (phonology) , ensemble kalman filter , kalman filter , computer science , inference , flow (mathematics) , free surface , flux (metallurgy) , artificial intelligence , computer vision , algorithm , extended kalman filter , mechanics , mathematics , meteorology , geometry , geography , physics , philosophy , linguistics , materials science , metallurgy
The free‐surface flows, such as flows in rivers, lakes, and tides, play an important role in hydraulic engineering and environmental monitoring. However, due to their complex and changeable characters, the precise evolution procedure is quite difficult to reconstruct. In this study, the authors propose a novel framework to reconstruct the free‐surface flow modelled by the shallow water equations by assimilating the images sequences. In particular, the ensemble Kalman filter framework is employed to implement the assimilation process. The efficiency of the proposed strategy has been verified through numerical simulations in which the accurate flow field in different situations could be obtained within limited assimilation steps.