Offshore Wind Turbine Nonlinear Wave Loads and Their Statistics
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v009t12a042-omae2017-61184.pdf
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926.27 KB
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Checksum (MD5)
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Author(s)
Sclavounos, Paul D
Zhang, Yu
Ma, Yu
Larson, David F. H
Date Issued
September 2017
Journal
International Conference on Ocean, Offshore and Arctic Engineering
Publisher
American Society of Mechanical Engineers
Citation
Sclavounos, Paul D., Yu Zhang, Yu Ma, and David F. Larson. “Offshore Wind Turbine Nonlinear Wave Loads and Their Statistics.” International Conference on Ocean, Offshore and Arctic Engineering, Volume 9: Offshore Geotechnics; Torgeir Moan Honoring Symposium (September 2017): OMAE2017-61184, V009T12A042 © 2017 ASME
Version
Final published version
Abstract
The development is presented of an analytical model for the prediction of the stochastic nonlinear wave loads on the support structure of bottom mounted and floating offshore wind turbines. Explicit expressions are derived for the time-domain nonlinear exciting forces in a seastate with significant wave height comparable to the diameter of the support structure based on the fluid impulse theory. The method is validated against experimental measurements with good agreement. The higher order moments of the nonlinear load are evaluated from simulated force records and the derivation of analytical expressions for the nonlinear load statistics for their efficient use in design is addressed. The identification of the inertia and drag coefficients of a generalized nonlinear wave load model trained against experiments using Support Vector Machine learning algorithms is discussed.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Operations Research Center
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1115/OMAE2017-61184
