The aim of this work is to provide a reduced-order model to describe the dissipative behavior of nonlinear vertical sloshing involving Rayleigh–Taylor instability by means of a feed forward neural network. A 1-degree-of-freedom system is taken into account as representative of fluid–structure interaction problem. Sloshing has been replaced by an equivalent mechanical model, namely a boxed-in bouncing ball with parameters suitably tuned with performed experiments. A large data set, consisting of a long simulation of the bouncing ball model with pseudo-periodic motion of the boundary condition spanning different values of oscillation amplitude and frequency, is used to train the neural network. The obtained neural network model has been included in a Simulink® environment for closed-loop fluid–structure interaction simulations showing promising performances for perspective integration in complex structural system.
Nonlinear reduced-order model for vertical sloshing by employing neural networks / Pizzoli, Marco; Saltari, Francesco; Mastroddi, Franco; Martinez-Carrascal, Jon; González-Gutiérrez, Leo M.. - In: NONLINEAR DYNAMICS. - ISSN 0924-090X. - 107:2(2021), pp. 1469-1478. [10.1007/s11071-021-06668-w]
Nonlinear reduced-order model for vertical sloshing by employing neural networks
Pizzoli, Marco;Saltari, Francesco;Mastroddi, Franco
;
2021
Abstract
The aim of this work is to provide a reduced-order model to describe the dissipative behavior of nonlinear vertical sloshing involving Rayleigh–Taylor instability by means of a feed forward neural network. A 1-degree-of-freedom system is taken into account as representative of fluid–structure interaction problem. Sloshing has been replaced by an equivalent mechanical model, namely a boxed-in bouncing ball with parameters suitably tuned with performed experiments. A large data set, consisting of a long simulation of the bouncing ball model with pseudo-periodic motion of the boundary condition spanning different values of oscillation amplitude and frequency, is used to train the neural network. The obtained neural network model has been included in a Simulink® environment for closed-loop fluid–structure interaction simulations showing promising performances for perspective integration in complex structural system.File | Dimensione | Formato | |
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Note: https://doi.org/10.1007/s11071-021-06668-w
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