To meet global green energy targets, the bottom founded offshore wind industry is looking for ways to economically expand markets to deeper waters. A reduction of the hydrodynamic load is necessary to achieve this. One option is to perforate the monopile around the splash zone. Here the related work of Q. Star is continued through the implementation of a 2D Navier-Stokes based LES CFD model with VMS closure in Gridap.jl. The numerical simulations are gathered to create a dataset on which a machine learning surrogate model is trained. The analysis does not include secondary design aspects such as manufacturing, installation, noise mitigation, scour, corrosion, acidification, or marine growth. Neither does it require secondary fluid phenomena such as 3D simulations, FSI, VIV, and fatigue.
Analysis of 2D perforated cylinders, built up parametrically using the wall thickness, angle of attack, diameter, inflow velocity, number of perforations and porosity as input parameters shows that the first two are of negligible influence, and number three and four can, at least for their mean values, be modelled accurately using Morison equations. A non-scaled diameter does prove important in reducing the random nature of frequency-based effects. The number of perforations and the porosity provide complex interactions both from a design-force mean and variability perspective, as well as for the frequencies and vibrations they generate. A 2D LES-VMS model gives the perfect trade-off between cost and accuracy, predicting a possible drag reduction of more than 50% compared to a closed cylinder with large diameter.
Different machine learning surrogate models are analysed with the goal of massively speeding up the analysis in the future. This is achieved by a factor of 350 thousand using Random Forests, Gaussian Processes and Neural Networks. Although the Gaussian Processes preliminary show the best accuracy, below 6% error for millisecond predictions, further work on Neural Networks could give them the advantage in future analyses.