Floating offshore wind turbines (FOWTs) experience complex hydrodynamic and aerodynamic loading influenced by substructure types and stochastic environmental conditions. Accurately estimating the lifetime fatigue loads requires the analysis of thousands of operational scenarios, leading to high computational costs. Moreover, choosing the right input features driving fatigue in floating wind systems and appropriately binning them still remains an open question. We present a fast probabilistic surrogate that maps the site conditions to the loads on the wind turbine. The probabilistic aspect allows the propagation and quantification of statistical uncertainties from the stochastic input quantities to the resulting loads. A fast surrogate eliminates the need to fit a distribution to the site conditions or bin the input data. Rather, all available metocean data can be directly used as input, which automatically accounts for the joint distribution in the calculations. The surrogate model in this study uses the mixture density network (MDN) to predict the conditional distribution of the 10 min damage equivalent loads (DELs) for a 6 MW spar-type floating wind turbine. The MDN achieves high accuracy (R2>0.99) in capturing DEL means while efficiently propagating the statistical uncertainties. Furthermore, the surrogate enables quick estimation of 25-year lifetime fatigue damage across a range of potential floating wind farm sites, demonstrating its capability to facilitate rapid decision-making during preliminary site analysis.

Floating offshore wind turbines can extract energy from deep offshore locations,
typically unfit for fixed bottom designs. The complex interaction between the structural
behavior of the floating offshore wind turbine and the stochastic site conditions, however,
is an active area of research. Characterizing the relationship between the environmental
conditions and loads may help design reduced-order models, surrogate models, and physicsbased engineering models for floating wind turbines. This study uses data from the TetraSpar prototype equipped with a 3.6 MW Siemens Gamesa wind turbine. One-to-one simulations performed using an aero-servo-hydro-elastic software are included for comparison. Various tools, including linear correlation, mutual information, feature ordering using conditional independence, and sensitivity analysis using a data-driven variogram fit, are used for the assessment. This study is also helpful in validating the engineering model for future global sensitivity analysis using elementary effects or Sobol indices that require a rigid sampling of features and can, therefore, only be calculated with simulation tools. We find a good agreement between the experiments and simulations. The 10-min. damage equivalent loads on the tower show a correlation, particularly with the wind speed statistics and the significant wave height.

Heteroscedastic Gaussian process regression, based on the concept of chained Gaussian processes, is used to build surrogates to predict site-specific loads on an offshore wind turbine. Stochasticity in the inflow turbulence and irregular waves results in load responses that are best represented as random variables rather than deterministic values. Moreover, the effect of these stochastic sources on the loads depends strongly on the mean environmental conditions - for instance, at low mean wind speeds, inflow turbulence produces much less variability in loads than at high wind speeds. Statistically, this is known as heteroscedasticity. Deterministic and most stochastic surrogates do not account for the heteroscedastic noise, giving an incomplete and potentially misleading picture of the structural response. In this paper, we draw on the recent advancements in statistical inference to train a heteroscedastic surrogate model on a noisy database to predict the conditional pdf of the response. The model is informed via 10-minute load statistics of the IEA-10MW-RWT subject to both aero- and hydrodynamic loads, simulated with OpenFAST. Its performance is assessed against the standard Gaussian process regression. The predicted mean is similar in both models, but the heteroscedastic surrogate approximates the large-scale variance of the responses significantly better.

A new method is proposed to estimate a floating wind turbine's annual energy production (AEP) using frequency and time-domain design techniques. The approach demonstrated herein estimates the AEP by performing a convolution between the floating platform response and the response power operators (RPOs) that map the average power produced by the turbine as a function of the amplitude and frequency of the platform motions. One advantage of this approach is that it can be performed early in the conceptual design phase to help discover design space trade-offs between the platform and rotor design. The methodology is applied to the IEA Wind 15 MW WindCrete spar-buoy model using OpenFAST. The RPOs are obtained by prescribing single-DOF platform motions to the turbine with a given amplitude and frequency. This methodology is then validated by comparing the AEP estimation from the RPOs with the AEP estimation from fully-coupled simulations. The results indicate that the method is able to estimate the value of AEP for a realistic sea-state and regular waves. However, further validation is needed as, in the first case, the turbine is moving too little and, in the second case, the contribution of the controller may be dominant.