For many design applications in offshore engineering, including offshore
wind turbine foundations, engineers need accurate statistics for
kinematic and dynamic quantities, such as hydrodynamic forces, whose
statistics depend on the stochastic sea surface elevation. Nonlinear
phenomena in the wave--structure interaction require high-fidelity
simulations to be analyzed accurately. However, accurate quantification
of statistics requires a massive number of simulations, and the
computational cost is prohibitively expensive. To avoid that cost, this
study presents a machine learning framework to develop a reliable
surrogate model that minimizes the need for computationally expensive
numerical simulations, which is implemented for the monopile foundation
of an offshore wind turbine. This framework consists of two parts. The
first focuses on dimensionality reduction of stochastic irregular wave
episodes and the resulting hydrodynamic force time series. The second of
the framework focuses on the development of a Gaussian process
regression surrogate model which learns a mapping between the wave
episode and the force-on-structure. This surrogate uses a Bayesian
active learning method that sequentially samples the wave episodes
likely to contribute to the accurate prediction of extreme hydrodynamic
forces in order to design subsequent CFD numerical simulations.
Additionally, the study implements a spectrum transfer technique to
combine CFD results from quiescent and extreme waves. The principal
advantage of this framework is that the trained surrogate model is
orders of magnitude faster to evaluate than the classical modeling
methods, while built-in uncertainty quantification capabilities allows
for efficient sampling of the parameter using with the CFD tools
traditionally employed.