Bulletin of the American Physical Society
71st Annual Meeting of the APS Division of Fluid Dynamics
Volume 63, Number 13
Sunday–Tuesday, November 18–20, 2018; Atlanta, Georgia
Session F26: General Fluid Dynamics: Drag Reduction, Obstacles and Constrictions
8:00 AM–10:10 AM,
Monday, November 19, 2018
Georgia World Congress Center
Room: B314
Chair: Jonathan Clausen, Sandia National Lab
Abstract ID: BAPS.2018.DFD.F26.9
Abstract: F26.00009 : Hydrodynamic object identification using artificial neural networks
9:44 AM–9:57 AM
Presenter:
Sreetej Lakkam
(Singapore University of Technology and Design)
Authors:
Sreetej Lakkam
(Singapore University of Technology and Design)
B T Balamurali
(Singapore University of Technology and Design)
Roland Bouffanais
(Singapore University of Technology and Design)
Passive object sensing has evolved in aquatic animals to enable them to recognize hydrodynamic objects. This unique capability can be used in autonomous underwater vehicles to gain better awareness of the marine environment. Here, we present a data-driven model that uses artificial neural networks to identify the shape of an obstacle placed in potential flow using data from a stationary sensor array. Specifically, the machine learning framework is used to solve the inverse problem of estimating the body shape from the measured velocity field. The ability of neural networks to deduce the complex underlying relationships without explicit mathematical description is used for parametric fitting of velocity flow data to accurately predict object shape characteristics. Synaptic weights obtained using a gradient-descent based optimization are used to obtain relations between the shape coefficients and the velocity field. Large data sets corresponding to flows with varying object shapes are generated and used to train and validate the performance of this machine learning approach. Finally, this data-driven method is easy to train owing to the analytical nature of the forward problem and is found to accurately estimate object shapes from limited and localized data acquired at a distance.
To cite this abstract, use the following reference: http://meetings.aps.org/link/BAPS.2018.DFD.F26.9
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