Visual inference of flow flux via free surface PDE model and image sequence assimilation. Issue 1 (4th January 2019)

• Record Type: Journal Article
• Authors: Guo, Shan; Xu, Chao; Yiu, Ka Fai Cedric
• Is Part Of: IET cyber-systems and robotics. Volume 1:Issue 1(2019)
• Page Start: 20
• Page End: 27
• Publication Date: 2019-01-04
• Subjects: numerical analysis -- data assimilation -- partial differential equations -- image sequences -- shallow water equations -- Kalman filters; free‐surface flow -- images sequences -- ensemble Kalman filter framework -- assimilation process -- accurate flow field -- assimilation steps -- visual inference -- flow flux -- image sequence assimilation -- hydraulic engineering -- environmental monitoring -- complex characters -- changeable characters -- precise evolution procedure; Robotics -- Periodicals; Cybernetics -- Periodicals; Cybernetics; Robotics; Periodicals; 629
• DOI: 10.1049/iet-csr.2018.0002
• Languages: English
• ISSNs: 2631-6315

Abstract

Abstract : 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.