Uncertainty in Loads for Different Constraint Patterns in Constrained-Turbulence Generation

This paper investigates the effect that adding constraints to turbulence simulations has on the uncertainty of resulting aeroelastic loads. The constrained turbulence is generated using the open-source constrained turbulence generator PyConTurb ("Python Constrained Turbulence"). A selection of constraint patterns were used to mimic the design of a met mast layout; i.e., the number of sonic anemometers and their locations throughout the rotor. A case study is presented to demonstrate in detail the effects of adding constraints before a larger numerical experiment is presented. The results of the numerical experiment indicate that adding constraints is extremely beneficial in reducing the mean absolute error of both operational parameters and loads. The reduction in mean absolute error ranged from 13% to 98%. The error in the extreme values and damage-equivalent loads were not impacted by the added constraints due to lack of gusts in the original signals and the similarity of the power spectra of the constrained and non-constrained signals, respectively.