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Research Papers: Ocean Engineering

Data Mining Pt. Reyes Buoy for Rare Wave Groups

[+] Author and Article Information
Harleigh C. Seyffert

Naval Architecture and Marine Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: harleigh@umich.edu

Armin W. Troesch

Naval Architecture and Marine Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: troesch@umich.edu

1Corresponding author.

Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received June 18, 2015; final manuscript received October 29, 2015; published online December 15, 2015. Assoc. Editor: Yi-Hsiang Yu. The United States Government retains, and by accepting the article for publication, the publisher acknowledges that the United States Government retains, a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for United States Government purposes.

J. Offshore Mech. Arct. Eng 138(1), 011101 (Dec 15, 2015) (7 pages) Paper No: OMAE-15-1049; doi: 10.1115/1.4031973 History: Received June 18, 2015; Revised October 29, 2015

This paper addresses the existence of rare wave groups by examining time series data from the Pt. Reyes buoy. The buoy is operated by the Coastal Data Information Program (CDIP), University of California San Diego. The definition of rare wave groups, as defined by Kim and Troesch, used in this paper differs from the more commonly used wave group definition based on threshold crossings. With the time series data from the Pt. Reyes buoy, these rare wave groups are shown to be a naturally occurring phenomenon. The essential features of the data are examined, as well as the analysis methods and findings. By sifting through 17 years of wave elevation data from the Pt. Reyes buoy, this preliminary work addresses not only the question to what extent rare wave groups exist in nature but also what their probability of occurrence is.

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References

Kim, D. , and Troesch, A. , 2013, “ Statistical Estimation of Extreme Roll Response in Short Crested Irregular Seas,” Transactions of SNAME 2013, pp. 123–160.
Longuet-Higgins, M. , 1957, “ The Statistical Analysis of a Random, Moving Surface,” Philos. Trans. R. Soc. London, 249(966), pp. 321–387. [CrossRef]
Goda, Y. , 1976, “ On Wave Groups,” Offshore Structures (BOSS), Vol. 1, pp. 115–127.
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CDIP, “ Station 029,” Pt. Reyes Buoy, CA, http://cdip.ucsd.edu/?nav=recent&stn=029&xitem=info.
Otnes, R. , and Enochson, L. , 1978, Applied Time Series Analysis: Basic Techniques, Vol. 1, Wiley, New York.
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St. Dennis, M. , and Pierson, W. J. , 1953, “ On the Motions of Ships in Confused Seas,” Trans. SNAME, 61, pp. 302–316.
Alford, L. , Kim, D. , and Troesch, A. , 2011, “ Estimation of Extreme Slamming Pressures Using the Non-Uniform Fourier Phase Distributions of a Design Loads Generator,” Ocean Eng., 38(5–6), pp. 748–762. [CrossRef]
Kim, D. , Engle, A. , and Troesch, A. , 2011, “ Estimates of Long-Term Combined Wave Bending and Whipping for Two Alternative Hull Forms,” Transactions of SNAME 2011, Vol. 119.
Ochi, M. , 1990, Applied Probability and Stochastic Processes in Engineering and Physical Sciences, Wiley, New York.

Figures

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Fig. 1

Orientation of reference axes for datawell directional buoy [9]

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Fig. 2

Distribution of available time series for given significant wave height/peak modal period January 1997–December 2013

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Fig. 3

Truncated double-sided spectrum for bins 1 and 2

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Fig. 4

Average of derived process maxima for 30-min time series in bins 1 and 2, normalized by k

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Fig. 5

Probability density function (PDF) of wave elevation time series data, overlaid with Gaussian distribution, for all time series in bins 1 and 2 (N = 7.7 × 106 and N = 5.6 × 106, respectively)

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Fig. 6

Left panel: wave groups and ensemble average for all time series (in meters) with arbitrary 50 time series plotted for k = 1, 3, 6, and 9 for bins 1 and 2. Right panel: time series containing top 50 maxima of derived process for k = 1, 3, 6, and 9 and ensemble average for bins 1 and 2. to, time of maximum zk(t), shifted to 100 s without loss of generality.

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Fig. 7

Representative time series (in meters) containing maximum of derived process, for k = 1, 3, 6, and 9 and (top 50) ensemble average for bins 1 and 2. to, time of maximum zk(t), shifted to 100 s without loss of generality.

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Fig. 8

Extreme value distribution for k = 1 bin 1 with DKL: 0.0484 (ym=ẑ1/σ1 : m = 1548; N = 3350)

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