Characterization of Extreme Wave Conditions for Wave Energy Converter Design and Project Risk Assessment

Neary, Vincent S.; Ahn, Seongho; Seng, Bibiana E.; Allahdadi, Mohammad Nabi; Wang, Taiping; Yang, Zhaoqing; He, Ruoying

doi:10.3390/jmse8040289

Title: Characterization of Extreme Wave Conditions for Wave Energy Converter Design and Project Risk Assessment

Journal Article · Sat Apr 18 00:00:00 EDT 2020 · Journal of Marine Science and Engineering

DOI:https://doi.org/10.3390/jmse8040289· OSTI ID:1638491

^[1]; Ahn, Seongho ^[1];

^[2]; Allahdadi, Mohammad Nabi ^[3];

^[4];

^[4]; He, Ruoying ^[3]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Water Power Technologies
Pennsylvania State Univ., State College, PA (United States)
North Carolina State Univ., Raleigh, NC (United States)
Pacific Northwest National Lab. (PNNL), Seattle, WA (United States)

Best practices and international standards for determining n-year return period extreme wave (sea states) conditions allow wave energy converter designers and project developers the option to apply simple univariate or more complex bivariate extreme value analysis methods. The present study compares extreme sea state estimates derived from univariate and bivariate methods and investigates the performance of spectral wave models for predicting extreme sea states at buoy locations within several regional wave climates along the US East and West Coasts. Two common third-generation spectral wave models are evaluated, a WAVEWATCH III® model with a grid resolution of 4 arc-minutes (6–7 km), and a Simulating WAves Nearshore model, with a coastal resolution of 200–300 m. Both models are used to generate multi-year hindcasts, from which extreme sea state statistics used for wave conditions characterization can be derived and compared to those based on in-situ observations at National Data Buoy Center stations. Comparison of results using different univariate and bivariate methods from the same data source indicates reasonable agreement on average. Discrepancies are predominantly random. Large discrepancies are common and increase with return period. There is a systematic underbias for extreme significant wave heights derived from model hindcasts compared to those derived from buoy measurements. This underbias is dependent on model spatial resolution. However, simple linear corrections can effectively compensate for this bias. A similar approach is not possible for correcting model-derived environmental contours, but other methods, e.g., machine learning, should be explored.

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Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Water Power Technologies Office; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC05-76RL01830; NA0003525

OSTI ID:: 1638491

Report Number(s):: PNNL-SA-152704

Journal Information:: Journal of Marine Science and Engineering, Vol. 8, Issue 4; ISSN 2077-1312

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 18 works

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