Research Papers: Materials Technology

Empirical Procedures for Long-Term Prediction of Fatigue Damage for an Instrumented Marine Riser

[+] Author and Article Information
C. Shi, L. Manuel

Department of Civil, Architectural,
and Environmental Engineering,
University of Texas,
Austin, TX 78712

M. A. Tognarelli

BP America Production Co.,
Houston, TX 77079

1Corresponding author.

2Present address: Technip USA Inc., Houston, TX 77079.

Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received August 2, 2013; final manuscript received May 7, 2014; published online June 6, 2014. Assoc. Editor: Celso P. Pesce.

J. Offshore Mech. Arct. Eng 136(3), 031402 (Jun 06, 2014) (10 pages) Paper No: OMAE-13-1075; doi: 10.1115/1.4027654 History: Received August 02, 2013; Revised May 07, 2014

Slender marine risers used in deepwater applications can experience vortex-induced vibration (VIV). It is becoming increasingly common for field monitoring campaigns to be undertaken wherein data loggers such as strain sensors and/or accelerometers are installed on such risers to aid in VIV-related fatigue damage estimation. Such damage estimation relies on the application of empirical procedures that make use of the collected data. This type of damage estimation can be undertaken for different current profiles encountered. The empirical techniques employed make direct use of the measurements and key components in the analyszes (such as participating riser modes selected for use in damage estimation) are intrinsically dependent on the actual current profiles. Fatigue damage predicted in this manner is in contrast to analytical approaches that rely on simplifying assumptions on both the flow conditions and the response characteristics. Empirical fatigue damage estimates conditional on current profile type can account explicitly even for complex response characteristics, participating riser modes, etc. With significant amounts of data, it is possible to establish “short-term” fatigue damage rate distributions conditional on current type. If the relative frequency of different current types is known from metocean studies, the short-term fatigue distributions can be combined with the current distributions to yield integrated “long-term” fatigue damage rate distributions. Such a study is carried out using data from the Norwegian Deepwater Programme (NDP) model riser subject to several sheared and uniform current profiles and with assumed probabilities for different current conditions. From this study, we seek to demonstrate the effectiveness of empirical techniques utilized in combination with field measurements to predict the long-term fatigue damage and the fatigue failure probability.

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Shi, C., Manuel, L., Tognarelli, M., and Botros, T., 2012, “On the Vortex-Induced Vibration Response of a Model Riser and Location of Sensors for Fatigue Damage Prediciton,” ASME J. Offshore Mech. Arct. Eng., 134(3), p. 031802. [CrossRef]
Shi, C., Park, J., Manuel, L., and Tognarelli, M., 2014, “A Data-Driven Mode Identification Algorithm for Riser Fatigue Damage Assessment,” ASME J. Offshore Mech. Arct. Eng., 136(3), p. 031702. [CrossRef]
Braaten, H., and Lie, H., 2004, “NDP Riser High Mode VIV Tests,” Norwegian Marine Technology Research Institue, Main Report No. 512394.00.01.
Trim, A., Braaten, H., Lie, H., and Tognarelli, M., 2005, “Experimental Investigation of Vortex-induced Vibration of Long Marine Risers,” J. Fluids Struct., 21, pp. 335–361. [CrossRef]
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Blom, G., 1958, Statistical Estimates and Transformed Beta-Variables, John Wiley and Sons, New York.
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Fig. 1

Flowchart showing algorithm for empirical prediction of long-term fatigue damage and failure probability for an instrumented riser

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Fig. 2

Locations of the eight input strain sensors and the key location (assumed to be that of the location of strain sensor no. 5)

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Fig. 3

DR estimates for: (a) Uniform current events and (b) sheared current events

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Fig. 6

Estimated and measured long-term fatigue damage rate per year for various locations over the entire length of the riser in the case of 23 sensors

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Fig. 7

Estimated and measured long-term fatigue damage rate per year for various locations over the entire length of the riser in the case of eight sensors and assuming group G1 contains six events

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Fig. 8

Estimated long-term fatigue damage rate per year over the entire length of the riser in the case of eight sensors—a comparison for when group G1 includes six events versus 24 events

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Fig. 5

Estimated and measured long-term fatigue damage rate per year for various locations over the entire length of the riser in the case of eight sensors

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Fig. 4

Statistical tests for a normal distribution assumption for ln(DR): (a) PPCC test for G1; (b) KS test for G1; (c) PPCC test for G2; (d) KS test for G2; (e) PPCC test for G3; (f) KS test for G3; (g) PPCC test for G4; and (h) KS test for G4



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