Stability Analysis of Fluid-Conveying Beams using Artificial Intelligence

This paper employs artificial intelligence in predicting the stability of pipes conveying fluid. Field data was collected for different pipe structures and usage. Adaptive Neuro-Fuzzy Inference System (ANFIS) model is implemented to predict the stability of the pipe using the fundamental natural frequency at different flow velocities as the index of stability. Results reveal that the neuro-fuzzy model compares relatively well with the conventional finite element method. It was also established that a pipe conveying fluid is most stable when the pipe is clamped at both ends but least stable when it is a cantilever.

[1]  Mohammed M. Zahra,et al.  Modeling of Vibration Monitoring of Steam Turbine in Nuclear Power Plant using Modular Artificial Neural Network , 2014 .

[2]  Kameswara Rao Chellapilla,et al.  Vibrations of Fluid-Conveying Pipes Resting on Two-parameter Foundation , 2008 .

[3]  Wan-Suk Yoo,et al.  Finite Element Analysis of Forced Vibration for a Pipe Conveying Harmonically Pulsating Fluid , 2005 .

[4]  M. P. Païdoussis,et al.  Nonlinear dynamics of extensible fluid-conveying pipes, supported at both ends , 2009 .

[5]  Noel Jordan Jameson,et al.  Flow Induced Vibration and Critical Velocities , 2011 .

[6]  Ivan Grant,et al.  FLOW INDUCED VIBRATIONS IN PIPES, A FINITE ELEMENT APPROACH , 2010 .

[7]  Begoña Mediano Valiente,et al.  Modelling of a clamped-pinned pipeline conveying fluid for vibrational stability analysis , 2014 .

[8]  Christian Soize,et al.  Dynamic stability of a pipe conveying fluid with an uncertain computational model , 2014 .

[9]  B. Tavassoli,et al.  Initial test and design of a soft sensor for flow estimation using vibration measurements , 2011, The 2nd International Conference on Control, Instrumentation and Automation.

[10]  Andreas Zilian,et al.  Modelling of Fluid-Structure Interaction – Effects of Added Mass, Damping and Stiffness , 2014 .

[11]  Gao Hang-shan,et al.  Free vibration analysis of micropipe conveying fluid by wave method , 2012 .

[12]  H. Abdul Razak,et al.  Application of artificial neural network on vibration test data for damage identification in bridge girder , 2011 .

[13]  Shih-Lin Hung,et al.  A neural network-based approach for detection of structural damage , 2005 .