Acta Scientific Computer Sciences

Research Article Volume 3 Issue 8

A Data Model for In-stream Forces on a Cylinder Using Neural Networks and Linear Prediction Filters

Erdem Aktosun1,2, Nikolaos I Xiros1* and Jason M Dahl2

1Naval Architecture and Marine Eng., University of New Orleans, USA
2Ocean Engineering, University of Rhode Island, USA

*Corresponding Author: Nikolaos I Xiros, Naval Architecture and Marine Eng., University of New Orleans, USA.

Received: May 20, 2021; Published: July 29, 2021

Abstract

Hydrodynamic force and error estimation model are developed based on experimental data. Force model is developed to model the dynamic forces with Artificial Neural Network (ANN) on an oscillating circular cylinder for flow conditions where Vortex-Induced-Vibrations (VIV) are known to occur and data error estimation model is developed for this existing neural network time dependent hydrodynamic force model. The force and error estimation model are developed to use in potential control systems to improve VIV based energy harvesting. The dynamic model is empirical, utilizing force measurements obtained for a large set of forced motion experiments, spanning a range of parametric values that prescribe the kinematics of the cylinder motion. The model includes the dynamics of a circular cylinder undergoing forced combined in-line and cross-flow motion in a free stream. The experiments were conducted in a fully automated towing tank where parameters of in-line amplitude of motion, cross-flow amplitude of motion, reduced velocity, and phase difference between in-line and cross-flow motion were varied over nearly 10,000 experiments. All experiments were carried out at a constant Reynolds number of 7620. A feed forward neural network is trained using the force database to develop a time dependent model of forces on the cylinder for given kinematic conditions. The time series error between the measured and feed forward Artificial Neural Network (ANN) model is found for the lift and drag force time histories. An autoregressive (AR) error predictor is developed from the existing neural network time dependent model of forces on the cylinder for given kinematic conditions. This error predictor is developed based on the error between the measured signal and artificial neural network model and can be used to improve predictions from the model.

Keywords: Vortex Induced Vibrations; Neural Networks; Data Analysis; Autoregressive Filter; Linear Prediction

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Citation

Citation: Nikolaos I Xiros., et al. “A Data Model for In-stream Forces on a Cylinder Using Neural Networks and Linear Prediction Filters". Acta Scientific Computer Sciences 3.8 (2021): 50-64.

Copyright

Copyright: © 2021 Nikolaos I Xiros., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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