Nonlinear reduced-order model for vertical sloshing by employing neural networks

被引:0
|
作者
Marco Pizzoli
Francesco Saltari
Franco Mastroddi
Jon Martinez-Carrascal
Leo M. González-Gutiérrez
机构
[1] Sapienza University of Rome,Department of Mechanical and Aerospace Engineering
[2] Universidad Politécnica de Madrid,Naval Architecture Department
来源
Nonlinear Dynamics | 2022年 / 107卷
关键词
Sloshing; Nonlinear Dynamics; Neural Networks;
D O I
暂无
中图分类号
学科分类号
摘要
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 Sloshing has been replaced by an equivalent mechanical model, namely a boxed-in bouncing ball with parameters suitably tuned with performed experiments.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.
引用
收藏
页码:1469 / 1478
页数:9
相关论文
共 50 条
  • [21] A finite element reduced-order model based on adaptive mesh refinement and artificial neural networks
    Baiges, Joan
    Codina, Ramon
    Castanar, Inocencio
    Castillo, Ernesto
    INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, 2020, 121 (04) : 588 - 601
  • [22] Learning reduced-order models for cardiovascular simulations with graph neural networks
    Pegolotti, Luca
    Pfaller, Martin R.
    Rubio, Natalia L.
    Ding, Ke
    Brufau, Rita Brugarolas
    Darve, Eric
    Marsden, Alison L.
    COMPUTERS IN BIOLOGY AND MEDICINE, 2024, 168
  • [23] Reduced-Order Modeling of Steady and Unsteady Flows with Deep Neural Networks
    Barraza, Bryan
    Gross, Andreas
    AEROSPACE, 2024, 11 (07)
  • [24] Feedback stabilization of an oscillating vertical cylinder by POD Reduced-Order Model
    Tissot, Gilles
    Cordier, Laurent
    Noack, Bernd R.
    3RD INTERNATIONAL CONFERENCE ON MATHEMATICAL MODELING IN PHYSICAL SCIENCES (IC-MSQUARE 2014), 2015, 574
  • [25] Distributed regression in sensor networks with a reduced-order kernel model
    Honeine, Paul
    Essoloh, Mehdi
    Richard, Cedric
    Snoussi, Hichem
    GLOBECOM 2008 - 2008 IEEE GLOBAL TELECOMMUNICATIONS CONFERENCE, 2008,
  • [26] Sloshing reduced-order models for aeroelastic analyses of innovative aircraft configurations
    Colella, Marta
    Saltari, Francesco
    Pizzoli, Marco
    Mastroddi, Franco
    AEROSPACE SCIENCE AND TECHNOLOGY, 2021, 118
  • [27] Parameterized nonintrusive reduced-order model for general unsteady flow problems using artificial neural networks
    Sugar-Gabor, Oliviu
    INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, 2021, 93 (05) : 1309 - 1331
  • [28] Proper Orthogonal Decomposition Reduced-Order Model for Nonlinear Aeroelastic Oscillations
    Xie, Dan
    Xu, Min
    Dowell, Earl H.
    AIAA JOURNAL, 2014, 52 (02) : 229 - 241
  • [29] Reduced-order aerodynamic model and its application to a nonlinear aeroelastic system
    Tang, DM
    Conner, MD
    Dowell, EH
    JOURNAL OF AIRCRAFT, 1998, 35 (02): : 332 - 338
  • [30] Analysis of a reduced-order nonlinear model of a multi-physics beam
    Guillot, V.
    Savadkoohi, A. Ture
    Lamarque, C-H.
    NONLINEAR DYNAMICS, 2019, 97 (02) : 1371 - 1401