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Research Papers: Structures and Safety Reliability

Airgap Statistics for a Tension Leg Platform

[+] Author and Article Information
Oleg Gaidai, Carl Trygve Stansberg

MARINTEK,
Trondheim NO-7491, Norway

Arvid Naess

CeSOS and Department of
Mathematical Sciences,
NTNU,
Trondheim NO-7491, Norway

Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received September 30, 2013; final manuscript received July 3, 2014; published online October 21, 2014. Assoc. Editor: Lance Manuel.

J. Offshore Mech. Arct. Eng 137(1), 011602 (Oct 21, 2014) (5 pages) Paper No: OMAE-13-1092; doi: 10.1115/1.4028734 History: Received September 30, 2013; Revised July 03, 2014

The paper discusses a method for estimating extreme value statistics of the airgap for floating offshore platforms subjected to random seas. It is an adaptation of a recently developed method, which is based on the mean upcrossing rate (MUR) function for univariate time series combined with an optimization procedure that allows prediction at extreme response levels by extrapolation. Extensive model tests were performed in a large wave basin for a tension leg platform (TLP) operating in the Norwegian Sea. Among several critical parameters, the airgap was measured at a number of locations under the platform deck. The wave in deck impact is a critical safety issue with respect to the deck damage and occurrence of extreme tether tensions. The authors have utilized experimental data to look at critical airgaps under the deck in random waves. Conclusions are drawn about extreme airgap statistics, and consequently about the wave impact probability in severe seas.

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References

Naess, A., Gaidai, O., and Haver, S., 2007, “Efficient Estimation of Extreme Response of Drag-dominated Offshore Structures by Monte Carlo Simulation,” Ocean Eng., 34(16), pp. 2188–2197. [CrossRef]
Naess, A., Stansberg, C. T., Gaidai, O., and Baarholm, R., 2009, “Statistics of Extreme Events in Airgap Measurements,” ASME J. Offshore Mech. Arct. Eng.131(4), p. 041107. [CrossRef]
Izadparast, A. H., and Niedzwecki, J. M., 2010, “Probability Distributions of Wave Run-up on a TLP Model,” Mar. Struct., 23(2), pp. 164–186. [CrossRef]
Forristall, G. Z., 2006, “Maximum Wave Heights Over an Area and the Airgap Problem,” ASME Paper No. OMAE 2006–92022.
Krogstad, H. E., Liu, J., Socquet-Juglard, H., Dysthe, K. B., and Trulsen, K., 2004, “Spatial Extreme Value Analysis of Nonlinear Simulations of Random Surface Waves,” ASME Paper No. OMAE–2004–51336. [CrossRef]
Naess, A., and Batsevych, O., 2010, “Space-Time Extreme Value Statistics of a Gaussian Random Field,” Probab. Eng. Mech., 25(5), pp. 372–379. [CrossRef]
Naess, A., and Gaidai, O., 2011, “Prediction of Airgap Statistics for Fixed Offshore Platforms,” ASME Paper No. OMAE 2011–49588. [CrossRef]
Bitner-Gregersen, E. M., 2011, “Reliability Assessment of TLP Air-gap in Non-linear Waves,” ASME Paper No. OMAE 2011–50153.
DaSilva, O., and Knecht, H., 2011, “Airgap on Semi Submersibles: a Practical Guide on the Implementation of a Stochastic Approach,” ASME Paper No. OMAE 2011–49278. [CrossRef]
Forristall, G. Z., 2011, “Maximum Ccrest Heights Under a Model TLP Deck,” ASME Paper No. OMAE 2011–49837.
Stansberg, C. T., Baarholm, R., Kristiansen, T., Hansen, E. W. M., and Rørtveit, G., 2005, “Extreme Wave Amplification and Impact Loads on Offshore Structures,” Proceedings ofOTC 2005, Houston, TX, Feb. 5, Paper No. OTC-17487. [CrossRef]
Stansberg, C. T., Baarholm, R., Berget, K., and Phadke, A. C., 2010, “Prediction of Wave Impact in Extreme Weather,” Proceedings ofOTC 2010, Houston, TX, May 3–6, Paper No. OTC-20573. [CrossRef]
NORSOK Standard, N-003 Rev. 4, 2007, Actions and Action Effects, Norwegian Technology Standards Institution, Oslo, Norway.
Naess, A., and Gaidai, O., 2008, “Monte Carlo Methods for Estimating the Extreme Response of Dynamical Systems,” ASCE J. Eng. Mech., 134(8), pp. 628–636. [CrossRef]
Naess, A., and Gaidai, O., 2009, “Estimation of Extreme Values From Sampled Time Series,” Struct. Saf., 31(4), pp. 325–334. [CrossRef]
Naess, A., Gaidai, O., and Batsevych, A., 2010, “Prediction of Extreme Response Statistics of Narrow-Band Random Vibrations,” J. Eng. Mech., 136(3), pp. 290–298. [CrossRef]

Figures

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Fig. 1

Heidrun TLP as seen from the side

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Fig. 2

Snapshot of model testing of the Heidrun TLP in a high sea state in the MARINTEK ocean basin

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Fig. 3

TLP deck and measurement locations, selected only under the deck

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Fig. 4

Distribution of maximal wave height per measurement location, see Fig. 3. Only true under-deck locations are kept. Dataset from 20 storms.

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Fig. 8

Prediction for station #11, 20 storms dataset. 95% confidence bands are dashed lines. Prediction P=10−4 and CI = (0.5, 1.6) × 10−4.

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Fig. 7

Free field wave elevation upcrossing rate, 20 storms dataset. 95% confidence bands are dashed lines.

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Fig. 6

Prediction, 20 storms dataset. 95% confidence bands are dashed lines.

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Fig. 5

Prediction, 2 storms dataset. 95% confidence bands are dashed lines.

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