Abstract
This paper aims to solve the resonance failure probability and develop an effective method to estimate the effects of variables and failure modes on failure probability of axially functionally graded material (FGM) pipe conveying fluid. Correspondingly, the natural frequency of axially FGM pipes conveying fluid is calculated using the differential quadrature method (DQM). A variable sensitivity analysis (VSA) is introduced to measure the effect of each random variable, and a mode sensitivity analysis (MSA) is introduced to acquire the importance ranking of failure modes. Then, an active learning Kriging (ALK) method is established to calculate the resonance failure probability and sensitivity indices, which greatly improves the application of resonance reliability analysis for pipelines in engineering practice. Based on the resonance reliability analysis method, the effects of fluid velocity, volume fraction and fluid density of axially FGM pipe conveying fluid on resonance reliability are analyzed. The results demonstrate that the proposed method has great performance in the anti-resonance analysis of pipes conveying fluid.
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The raw/processed data required to reproduce these findings cannot be shared at this time as the data also form part of an ongoing study.
References
Zhang YW, Zhou L, Fang B, Yang TZ. Quantum effects on thermal vibration of single-walled carbon nanotubes conveying fluid. Acta Mech Solida Sin. 2017;30:550–6.
Wang L, Hong YZ, Dai HL, Ni Q. Natural frequency and stability tuning of cantilevered CNTs conveying fluid in magnetic field. Acta Mech Solida Sin. 2016;29:567–76.
Paidoussis MP. The canonical problem of the fluid-conveying pipe and radiation of the knowledge gained to other dynamics problems across Applied Mechanics. J Sound Vib. 2008;310:462–92.
Sundar VS, Manohar CS. Updating reliability models of statically loaded instrumented structures. Struct Saf. 2013;40:21–30.
Naebe M, Shirvanimoghaddam K. Functionally graded materials: a review of fabrication and properties. Appl Mater Today. 2016;5:223–45.
Bahaadini R, Dashtbayazi MR, Hosseini M, Khalili-Parizi Z. Stability analysis of composite thin-walled pipes conveying fluid. Ocean Eng. 2018;160:311–23.
Dai J, Liu Y, Liu H, Miao C, Tong G. A parametric study on thermo-mechanical vibration of axially functionally graded material pipe conveying fluid. Int. J. Mech. Mater. Des. 2019;15(14):715–726.
Hosseini M, Fazelzadeh SA. Thermomechanical stability analysis of functionally graded thin-walled cantilever pipe with flowing fluid subjected to axial load. Int J Struct Stab Dyn (Article). 2011;11:513–34.
Gan C-B, Guo S-Q, Lei H, Yang S-X. Random uncertainty modeling and vibration analysis of a straight pipe conveying fluid. Nonlinear Dyn. 2014;77:503–19.
Wang YQ, Li HH, Zhang YF, Zu JW. A nonlinear surface-stress-dependent model for vibration analysis of cylindrical nanoscale shells conveying fluid. Appl Math Model. 2018;64:55–70.
Bichon BJ, Eldred MS, Swiler LP, Mahadevan S, McFarland JM. Efficient global reliability analysis for nonlinear implicit performance functions. AIAA J. 2008;46:2459–68.
Echard B, Gayton N, Lemaire M. AK-MCS: an active learning reliability method combining Kriging and Monte Carlo Simulation. Struct Saf. 2011;33:145–54.
Echard B, Gayton N, Lemaire M, Relun N. A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models. Reliab Eng Syst Saf. 2013;111:232–40.
Gaspar B, Teixeira AP, Soares CG. Assessment of the efficiency of Kriging surrogate models for structural reliability analysis. Probab Eng Mech. 2014;37:24–34.
Lv Z, Lu Z, Wang P. A new learning function for Kriging and its applications to solve reliability problems in engineering. Comput Math Appl. 2015;70:1182–97.
Sun ZL, Wang J, Li R, Tong C. LIF: A new Kriging based learning function and its application to structural reliability analysis. Reliab Eng Syst Saf. 2017;157:152–65.
Yang XF, Liu YS, Zhang YS, Yue ZF. Probability and convex set hybrid reliability analysis based on active learning Kriging model. Appl Math Model. 2015;39:3954–71.
Helton JC, Johnson JD, Sallaberry CJ, Storlie CB. Survey of sampling-based methods for uncertainty and sensitivity analysis. Reliab Eng Syst Saf. 2006;91:1175–209.
Guo Q, Liu Y, Chen B, Yao Q. A variable and mode sensitivity analysis method for structural system using a novel active learning Kriging model. Reliab Eng Syst Saf. 2021;206:107285.
Amini Y, Heshmati M, Daneshmand F. Effects of longitudinal fins on dynamic stability of pipes conveying fluid made of functionally graded material. Mar Struct. 2021;79:103058.
Liang F, Yang XD, Bao RD, Zhang W. Frequency analysis of functionally graded curved pipes conveying fluid. Adv Mater Sci Eng. 2016;2016:7574216.
Wang ZM, Liu YZ. Transverse vibration of pipe conveying fluid made of functionally graded materials using a symplectic method. Nucl Eng Des. 2016;298:149–59.
Zhu B, Chen XC, Guo Y, Li YH. Static and dynamic characteristics of the post-buckling of fluid-conveying porous functionally graded pipes with geometric imperfections. Int J Mech Sci. 2021;189:105947.
An C, Su J. Dynamic behavior of axially functionally graded pipes conveying fluid. Math Probl Eng (Article). 2017;11:6789634.
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The funding was provided by Laboratory Fund (Grant No. SYJJ200320).
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Fan, X., Wu, N., Liu, Y. et al. Resonance System Reliability and Sensitivity Analysis Method for Axially FGM Pipes Conveying Fluid with Adaptive Kriging Model. Acta Mech. Solida Sin. 35, 1021–1029 (2022). https://doi.org/10.1007/s10338-022-00333-4
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DOI: https://doi.org/10.1007/s10338-022-00333-4