Monday, 24 April 2017

Update on research - Prologue to the probabilistic analysis of Offshore Wind Turbines (OWT)

Hello !

Here we are again, this time to talk a bit about work.

As you may know from previous posts I have been working on characterizing probabilistically the OWT towers, specifically for the fatigue analysis.

The fatigue analysis recomended for the design of OWT towers usually involves a very high number of simulations and some statistical distributions.
What is done is to run multiple simulations that reproduce the loads on the OWT; apply a methodology to count the loads that happen in every simulation; use the well know fatigue curves and linear damage sumation and then work on reproducing the best the complete lifetime of the turbine.
Obviously, it is quite unfeasible to make simulations for the full 10, 20 or many L years of simulations. So, what is usually done is to, using all the loads the we can obtain, extrapolate the loads for the period of time we want to design. This is assuming that the high load ranges will have the most impact on the fatigue life.

It is easy to understand that ideally the L years of life should be assessed completely, but that is a hard task. Even not "running" all the L years of loads accomplishing the design to fatigue is a heavy task. Now imagine if you want to run it for a probabilistic approach? Not easy. That would mean, for instance, simulating multiple turbines and see the variations in the extrapolation if you want to focus only on the loads. Naturally, there are other uncertainties that have also some influence in the expected life.

I have been working to implement a new methodology to assess the fatigue of the OWT and that is specifically working with Kriging surrogate models. The Kriging surrogate models are an amazing tool originnally developed for  geostatistics that interpolates function in a Gaussian process. Is true, I was amazed the first time I ran into them. Of course, their Gaussian characteristic which accounts for some uncertainty and the possibility to interpolate functions made them quite popular for reliability. Therefore, recently their usage spread into the reliability world quite significantly.

As I believe they are a very interesting tool, I will keep a full post for them, and that will be the next one.  For now this was a small introduction to present them.

Regards and see you very soon. This time as the topic is already introduced I won't be able to escape ;)