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Research Papers: Piper and Riser Technology

Evaluation of Slug Flow-Induced Flexural Loading in Pipelines Using a Surrogate Model

[+] Author and Article Information
Ibrahim A. Sultan

School of Science,
Information Technology and Engineering,
University of Ballarat,
Ballarat, VIC 3350, Australia

Gareth L. Forbes

Department of Mechanical Engineering,
Curtin University,
Perth, WA 6102, Australia

Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received January 10, 2012; final manuscript received March 31, 2013; published online June 6, 2013. Assoc. Editor: Colin Leung.

J. Offshore Mech. Arct. Eng 135(3), 031703 (Jun 06, 2013) (8 pages) Paper No: OMAE-12-1004; doi: 10.1115/1.4024207 History: Received January 10, 2012; Revised March 31, 2013

Slug flow induces vibration in pipelines, which may, in some cases, result in fatigue failure. This can result from dynamic stresses, induced by the deflection and bending moment in the pipe span, growing to levels above the endurance limits of the pipeline material. As such, it is of paramount importance to understand and quantify the size of the pipeline response to slug flow under given speed and damping conditions. This paper utilizes the results of an optimization procedure to devise a surrogate closed-form model, which can be employed to calculate the maximum values of the pipeline loadings at given values of speed and damping parameters. The surrogate model is intended to replace the computationally costly numerical procedure needed for the analysis. The maximum values of the lateral deflection and bending moment, along with their locations, have been calculated using the optimization method of stochastic perturbation and successive approximations (SPSA). The accuracy of the proposed surrogate model will be validated numerically, and the model will be subsequently used in a numerical example to demonstrate its applicability in industrial situations. An accompanying spreadsheet with this worked example is also given.

Copyright © 2013 by ASME
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References

Reda, A. M., and Forbes, G. L., 2011, “The Effect of Distribution for a Moving Force,” Proceedings of ACOUSTICS 2011, Australian Acoustical Society, Gold Coast, Queensland, Australia.
Reda, A. M., Forbes, G. L., and Sultan, I. A., 2012, “Characterization of Dynamic Slug Induced Loads,” Proceedings of the 31st International Conference on Ocean, Offshore and Arctic Engineering, Rio, Brazil, July 1–6, Paper No. OMAE 2012-83218.
Reda, A. M., Forbes, G. L., and Sultan, I. A., 2011, “Characterization of Slug Flow Conditions in Pipelines for Fatigue Analysis,” Proceedings of the 30th International Conference on Ocean, Offshore and Arctic Engineering, Rotterdam, The Netherlands, June 19–24, Paper No. OMAE 2011-49583.
Sultan, I. A., 2007, “A Surrogate Model for Interference Prevention in the Limaçon-to- Limaçon Machines,” Int. J. Comput.-Aided Eng. Softw., 24(5), pp. 437–449. [CrossRef]
Bates, R. A., Fontana, R., Randazzo, C., Vaccarino, E., and Wynn, H. P., 2000, “Empirical Modelling of Diesel Engine Performance for Robust Engineering Design,” Statistics for Engine Optimization, S. P.Edwards, D. M.Grove, and H. P.Wynn, eds., Wiley, New York, pp. 163–173.
Gilsinn, D. E., Brandy, H. T., and Ling, A., 2002, “A Spline Algorithm for Modelling Cutting Errors on Turning Centres,” J. Intell. Manuf., 13, pp. 391–401. [CrossRef]
Spall, J. C., 1998, “Implementation of the Simultaneous Perturbation Algorithm for Stochastic Optimization,” IEEE Trans. Aerosp. Electron. Syst., 34(3), pp. 817–823. [CrossRef]
Kothandaraman, G., and Rotea, M. A., 2005, “Simultaneous-Perturbation Stochastic-Approximation Algorithm for Parachute Parameter Estimation,” J. Aircr., 42(5), pp. 1229–1235. [CrossRef]
Jeang, A., Chen., T.-K., and Hwan, C.-L., 2002, “A Statistical Dimension and Tolerance Design for Mechanical Assembly Under Thermal Impact,” Int. J. Adv. Manuf. Technol., 20, pp. 907–915. [CrossRef]
Carley, K. M., Kamneva, N. Y., and Reminga, J., 2004, “Response Surface Methodology,” Carnegie Mellon University, CASOS Technical Report No. CMU-ISRI-04-136.
Robinson, T. J., Borror, C. M., and Myers, R., 2003, “Robust Parameter Design: A Review,” Qual. Reliab. Eng. Int., 20, pp. 81–101. [CrossRef]
Sultan, I. A., Reda, A. M., and Forbes, G. L., 2012, “A Surrogate Model for Evaluation of Maximum Normalised Load Factor in Moving Load Model for Pipeline Spanning Due to Slug Flow,” Proceedings of the 31st International Conference on Ocean, Offshore and Arctic Engineering, Rio, Brazil, July 1–6, Paper No. OMAE2012-83746.
Frýba, L., 1999, Vibration of Solids and Structures Under Moving Loads, Thomas Telford Ltd., Prague, Czech Republic, pp. 13–19.

Figures

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Fig. 1

Steps adopted to construct the design equations of DLF for displacement and bending moment

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Fig. 2

Simply supported beam subjected to a moving point force

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Fig. 3

Relationship between normalized μy and α at selected range of β

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Fig. 4

Relationship between Normalized φy and α at selected range of β

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Fig. 5

A schematic diagram showing the maximum deflection and force location at α = 0.7

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Fig. 6

Relationship between normalized μM and α at selected range of β

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Fig. 7

Relationship between normalized φM and α at selected range of β

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Fig. 8

Relationship between yn_max and α at selected range of β

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Fig. 9

Relationship between Mn_max and α at selected range of β

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Fig. 10

Relationship between normalized ymax and α at selected range of β calculated using exact and surrogate models

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Fig. 11

Relationship between normalized Mmax and α at selected range of β calculated using exact and surrogate models

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Fig. 12

Probability distribution of the deflection relative error for randomly generated data points for low speed applications

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Fig. 13

Probability distribution of the bending moment relative error for randomly generated data points for low speed applications

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Fig. 14

Probability distribution of the deflection relative error for randomly generated data points for high speed applications

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Fig. 15

Probability distribution of the bending moment relative error for randomly generated data points for high speed applications

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