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Sensitive parameter identification and uncertainty quantification for the stability of pipeline conveying fluid

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Abstract

Several uncertainty quantification and sensitivity analysis methods are used to determine the most sensitive geometric and material input parameters of a cantilevered pipeline conveying fluid when uncertainty is introduced to the system at the onset of instability. The full nonlinear equations of motion are modeled using the extended Hamilton’s principle and then discretized using Galerkin’s method. A parametric study is first performed, and the Morris elementary effects are calculated to obtain a preliminary understanding of how the onset speed changes when each parameter is introduced to a ± 5% uncertainty. Then, four different input uncertainty distributions, mainly, uniform and Gaussian distribution, are chosen to investigate how input distributions affect uncertainty in the output. A convergence analysis is used to determine the number of samples needed to maintain simulation accuracy while saving the most computational time. Then, Monte Carlo simulations are run, and the output distributions for each input distribution at ± 1%, ± 3% and ± 5% input uncertainty range are found and discussed. Additionally, the Pearson correlation coefficients are evaluated for different uncertainty ranges. A final Monte Carlo study is performed in which single parameters are held constant while all others still have uncertainty. Overall, the flow speed at the onset of instability is the most sensitive to changes in the outer diameter of the pipe.

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Acknowledgements

The authors T. Alvis and A. Abdelkefi would like to thank Sandia National Laboratories for their funding of this project. The authors S. Ceballes and A. Abdelkefi would like to acknowledge the National Science Foundation Graduate Research Fellowship Program for funding support. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the US Department of Energy or the United States Government.

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Appendix

Appendix

$$\beta = \sqrt[4]{{\frac{m + M}{{EI}}\omega^{2} }}$$
(5)
$$C_{ij} = EI\eta \int_{0}^{L} {\beta^{4} \phi_{j} \phi_{i} dS = } \delta_{ij} \eta \omega^{2}$$
(6)
$$\left( {Cu} \right)_{ij} = 2M\int_{0}^{L} {\phi^{\prime}_{j} \phi_{i} }$$
(7)
$$K_{ij} = \int_{0}^{L} {\left[ {EI\beta^{4} \phi_{j} - \left( {m + M} \right)g\left( {L - S} \right)\phi^{\prime\prime}_{j} + \left( {m + M} \right)\phi^{\prime}_{j} } \right]\phi_{i} dS}$$
(8)
$$\left( {Ku} \right)_{ij} = M\int\limits_{0}^{1} {\phi^{\prime\prime}_{j} \phi_{i} ds}$$
(9)
$$C = C_{ij} + U\left( {C_{u} } \right)_{ij}$$
(10)
$$K = K_{ij} + U^{2} \left( {K_{u} } \right)_{ij}$$
(11)

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Alvis, T., Ceballes, S. & Abdelkefi, A. Sensitive parameter identification and uncertainty quantification for the stability of pipeline conveying fluid. Int J Mech Mater Des 18, 327–351 (2022). https://doi.org/10.1007/s10999-021-09579-1

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