HAMILTON'S PRINCIPLE FOR EXTERNAL VISCOUS FLUID–STRUCTURE INTERACTION

2000 ◽  
Vol 238 (1) ◽  
pp. 113-145 ◽  
Author(s):  
H. BENAROYA ◽  
T. WEI
Author(s):  
H Benaroya ◽  
T Wei ◽  
S Kuchnicki ◽  
P Dong

A variational-based approach is developed to provide a framework for the study of flow-induced vibration. While the model includes experimentally derived functions, there are no ad hoc assumptions or a priori equations that are fit to data.


2019 ◽  
Vol 212 ◽  
pp. 571-585 ◽  
Author(s):  
Yingqian Liao ◽  
Nitin Garg ◽  
Joaquim R.R.A. Martins ◽  
Yin L. Young

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881934 ◽  
Author(s):  
Bernard Xavier Tchomeni ◽  
Alfayo Alugongo

Extraction of features of a specific signal in fluid–structure interaction is among the hottest problems in the field of mechanics. Yet, a comprehensive study of such problems remains a challenge due to their high nonlinearity and multidisciplinary nature. The study presented in this article is focused on a particular engineering application of fluid–structure interaction. The governing equation of a spinning rotor submerged in an incompressible viscous fluid is modelled by means of well-established dissipative energy principle, yielding a highly coupled 3-degree-of-freedom system with strong nonlinear terms. A two-dimensional model of the Navier–Stokes equations for the incompressible flow is developed for the viscous fluid motion around the spinning rotor under high fluctuations induced by unbalance, rotor–stator rub and a crack. The extracted features through frequency spectrum, orbit patterns and rotor-coupled deflection revealed that the performances of rotor systems are highly impacted by the hydrodynamic terms which are the sources of multiple frequency response. The results showed that the complex fluid–rotor model yields good analysis of fault diagnosis, and responses at more than one parametric resonance appear and reach a point of complex feature extractions when more than one fault coexists in the system. Furthermore, a nonlinear denoising by thresholding the wavelet coefficients is performed to overcome the complexity of discretization and for effective multiple fault diagnoses.


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