OFFSHORE WIND TURBINE NONLINEAR WAVE LOADS AND THEIR STATISTICS

• Main Authors: Sclavounos, Paul D, Zhang, Yu, Ma, Yu, Larson, David F
• Other Authors: 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
• Format: Article in Journal/Newspaper
• Language: English
• Published: ASME International 2020
• Subjects: Arctic
• Online Access: https://hdl.handle.net/1721.1/135881

© 2019 by ASME. The development 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 is presented. Explicit expressions are derived for the time-domain nonlinear exciting forces in a sea state with significant wave height comparable to the diameter of the support structure based on the fluid impulse theory (FIT). 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.