## 4 Conclusions  
In this work, different machine learning models were used to predict structural dynamics outputs of the NREL 5 MW turbine blade. Three benchmark datasets were generated using the OpenFAST turbine simulation tool for three different turbulence intensities. The datasets were analyzed, and the features used for predictions were the inflow wind speeds, the blade's azimuth position, and the turbine's control parameters.  
Ten different ML models were examined for accurate predictions. The linear models' accuracies were not satisfactory and were not reliable for further investigations. Nonlinear models, as well as neural network models, could not give the required accuracy for all the desired outputs. However, ensemble models, specifically the decision tree and random forest models, could give high accuracy for all the outputs and all turbulence classes. For instance, the random forest model could predict all outputs under all turbulence classes with accuracies over 98%.  
The random forest model was chosen to proceed with generalization tests to examine whether it can predict the outputs for different inflow conditions. Each turbulence class was used to train the model to predict the four outputs: blade tip deflections and blade root shear forces in the flapwise and edgewise directions. The model was then used to predict the outputs for the two remaining datasets. The model was effective in predicting the outputs even for conditions on which it was not trained. The accuracies in the flapwise direction were not high; however, it could be used in preliminary calculations, with accuracies above 79%, which is higher than the linear models trained on their outputs.  
The edgewise outputs, on the other hand, could be predicted with accuracies higher than 94% for flow conditions the model was not trained on. This indicates more confidence in generalizing the random forest model to any inflow conditions. This also shows the higher dependency of the edgewise outputs on other features not directly related to the flow conditions, precisely the azimuth position of the blade.  
Further investigations to assess the quality of the model could include testing for laminar flow conditions and testing the model to predict outputs for a different utility-scale wind turbine of different sizes and capacities and different blade structural properties.  
This research received no external funding.  
The data presented in the study are openly available in a GitHub repository at [[https://github.com/Amr-Ismaiel12/NREL-5](https://github.com/Amr-Ismaiel12/NREL-5) MW-aeroelastic-dataset]([https://github.com/Amr-Ismaiel12/NREL-5](https://github.com/Amr-Ismaiel12/NREL-5) MW-aeroelastic-dataset).  
The author declares no conflicts of interest.  
Figure A3: Time-marching edgewise deflections and residuals distribution for the random forest model trained on turbulence class A.  
Figure A4: Time-marching edgewise deflections and residuals distribution for the random forest model trained on turbulence class C.  
Figure A7: Time-marching flapwise forces and residuals distribution for the random forest model trained on turbulence class B.  
Figure A9: Random forest generalization results—Actual vs. predicted blade root edgewise forces.