Improved resonance reliability and global sensitivity analysis of multi-span pipes conveying fluid based on active learning Kriging model

Abstract In this paper, the improved resonance reliability and global sensitivity analysis of multi-span pipes conveying fluid is proposed via active learning Kriging model. The natural frequency of multi-span functionally graded materials pipes conveying fluid is calculated using the dynamic stiffness method. A new improved resonance performance function is proposed which is suitable for both broadband and multi-frequency resonance failure. This paper extends resonance reliability to the global sensitivity analysis, and proposes a moment-independent global sensitivity index based on the resonance failure probability. An importance ranking of random variables is obtained, which provides vital guidance and advice for the optimal design of pipe anti-resonance. A new resonance reliability analysis method via the active learning Kriging model is established, which greatly improves the application of pipelines resonance reliability analysis in engineering practice. Based on active learning Kriging model, the effects of fluid velocity, pressure and volume fraction of functionally graded material on resonance reliability are analyzed. The results demonstrate that the proposed method has high performance for the anti-resonance analysis of multi-span functionally graded materials pipes conveying fluid.

[1]  Hong-Liang Dai,et al.  A review of recent researches on FGM cylindrical structures under coupled physical interactions, 2000–2015 , 2016 .

[2]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

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

[4]  Zhenzhou Lu,et al.  A new method for evaluating Borgonovo moment-independent importance measure with its application in an aircraft structure , 2014, Reliab. Eng. Syst. Saf..

[5]  Yongming Liu,et al.  Bayesian Network inference for probabilistic strength estimation of aging pipeline systems , 2018 .

[6]  H. Ouyang,et al.  Vibration analysis of a complex fluid-conveying piping system with general boundary conditions using the receptance method , 2018, International Journal of Pressure Vessels and Piping.

[7]  Jianguo Zhang,et al.  Resonance Reliability Sensitivity Analysis for Torsional Vibration of Gear-Rotor Systems With Random Parameters , 2017 .

[8]  Gregoire Nicolis,et al.  Stochastic resonance , 2007, Scholarpedia.

[9]  Yongshou Liu,et al.  System reliability analysis through active learning Kriging model with truncated candidate region , 2018, Reliab. Eng. Syst. Saf..

[10]  Michael D. Todd,et al.  Frequency response of a fixed–fixed pipe immersed in viscous fluids, conveying internal steady flow , 2015 .

[11]  Tianzhi Yang,et al.  Quantum effects on thermal vibration of single-walled carbon nanotubes conveying fluid , 2017 .

[12]  Nicolas Gayton,et al.  AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation , 2011 .

[13]  Li Luyi,et al.  Moment-independent importance measure of basic variable and its state dependent parameter solution , 2012 .

[14]  Emanuele Borgonovo,et al.  A new uncertainty importance measure , 2007, Reliab. Eng. Syst. Saf..

[15]  A. Pishevar,et al.  Reliability analysis of pipe conveying fluid with stochastic structural and fluid parameters , 2016 .

[16]  Tarun Kant,et al.  A critical review of recent research on functionally graded plates , 2013 .

[17]  Zhufeng Yue,et al.  The dynamic reliability analysis of pipe conveying fluid based on a refined response surface method , 2015 .

[18]  A. Setoodeh,et al.  Nonlinear dynamic analysis of FG micro-pipes conveying fluid based on strain gradient theory , 2014 .

[19]  Bo Fang,et al.  Fractional Dynamics of Fluid-Conveying Pipes Made of Polymer-Like Materials , 2018 .

[20]  J. N. Reddy,et al.  Nonlinear analysis of microstructure-dependent functionally graded piezoelectric material actuators , 2014 .

[21]  Ebrahim Esmailzadeh,et al.  Forced vibration of fluid conveying carbon nanotubes considering thermal effect and nonlinear foundations , 2017 .

[22]  M. P. Paı¨doussis.  Some unresolved issues in fluid-structure interactions , 2005 .

[23]  Zijun Zhang,et al.  Dynamic behaviors of multi-span viscoelastic functionally graded material pipe conveying fluid , 2017 .

[24]  Gang Yang,et al.  MrDIRECT: a multilevel robust DIRECT algorithm for global optimization problems , 2015, J. Glob. Optim..

[25]  Jack P. C. Kleijnen,et al.  Regression and Kriging metamodels with their experimental designs in simulation: A review , 2017, Eur. J. Oper. Res..

[26]  U. Lee,et al.  Spectral element modelling and analysis of a pipeline conveying internal unsteady fluid , 2006 .

[27]  V. S. Sundar,et al.  Updating reliability models of statically loaded instrumented structures , 2013 .

[28]  Yiren Yang,et al.  Resonance analyses of a pipeline-riser system conveying gas-liquid two-phase flow with flow-pattern evolution , 2018 .

[29]  Zijun Zhang,et al.  Stability analysis of multi-span viscoelastic functionally graded material pipes conveying fluid using a hybrid method , 2017 .

[30]  Sankaran Mahadevan,et al.  Efficient surrogate models for reliability analysis of systems with multiple failure modes , 2011, Reliab. Eng. Syst. Saf..

[31]  Yishang Zhang,et al.  Probability and convex set hybrid reliability analysis based on active learning Kriging model , 2015 .

[32]  Yue Zhufeng,et al.  Transient response analysis of multi-span pipe conveying fluid , 2013 .

[33]  J. R. Banerjee,et al.  Dynamic stiffness formulation for structural elements: A general approach , 1997 .

[34]  Mohammad Talha,et al.  Recent development in modeling and analysis of functionally graded materials and structures , 2015 .

[35]  Sondipon Adhikari,et al.  A Response Surface Modelling Approach for Resonance Driven Reliability Based Optimization of Composite Shells , 2016 .