Surrogate models for the characterization of hydrodynamic loads on perforated monopiles

Abstract

Perforated monopiles show promise in providing a better alternative to the commonly used jacket-like substructures used in intermediate water depths in the range of 30 to 120 m. By introducing perforations near the vicinity of the splash zone the wave loads on the monopile can be mitigated and the fatigue damage reduced, which is the main advantage jackets have over monopiles. Furthermore, perforated monopiles would be easier to install and manufacture compared to jacket structures, which has the potential to reduce the Levelized Cost of Electricity (LCoE) considerably. Perforated monopiles also have the benefit of reduced (local) scour, reduced corrosion damage and providing a habitat for marine life.

To optimize the design of perforated monopiles fast novel surrogate models are needed to determine the (wave) load reduction by the introduction of perforations on the monopile. As traditional (full) order Computational Fluid Dynamics (CFD) models are slow and expensive to evaluate. Using a surrogate model a wider range of geometries can be used in the early design optimization steps. Promising designs can then be further evaluated using full models.

This thesis develops a surrogate model for the characterization of the (hydrodynamic) loads on perforated monopiles using Convolution Neural Networks (CNN). A dataset is created of 15,561 samples, which considers geometries of varying number of perforations, porosity and ‘angle of attacks’. A Finite Element Method (FEM) CFD model is made in Gridap for a ‘creeping flow’ to determine the flow fields. And determines a load Reduction Factor (RF) by the introduction of perforations. Furthermore, using a Signed Distance Function (SDF) a representation of the geometry is made for use in the CNN model.

Using PyTorch a CNN model is used to predict the RF for a certain input. The model combines multiple convolutional layers with an optional regression head (with linear layers). Five different models are created. Three for representing the geometry by using the SDF, decomposing the SDF in its x and y distance components and the binary representation. Furthermore, two models combine the SDF or distance components with the flow fields from the CFD model.

The results show that a speedup of 386 times is achieved by using the surrogate model, showing the main benefit of using a surrogate model. This is likely to improve considerably in further research when turbulence is taken into account. Furthermore, each of the five models show a good accuracy in predicting the RF. A general trend is observed that the more information is provided in the input to the CNN model the better the accuracy tends to be. The combination of implicit representation of the geometry by using distance components with the CFD flow fields shows the best accuracy with an expected maximum relative error rate of 0.94% within the parametric space of the dataset.