Abstract
We are interested in reconstructing winds flowing through a turbine on a second-by-second basis over a 10 min window. Previously, we developed a machine learning algorithm that takes in a snapshot of wind speed measurements and generates ensembles of three-dimensional wind field estimates. Here, we use these estimates as initial conditions in large-eddy simulations and reconstruct atmospheric and turbine response dynamic quantities in a synthetic field campaign. In doing so, we establish a baseline for model validation that future time-aware data assimilation techniques will be compared to. In turbine-free case studies, ground truth wind speeds consistently fall within our estimated wind speed distribution for the first 100 s after the simulation start. In simulations with turbines, the wind estimates show a small bias of 0.10 m s-1 and good correlation of 0.80 during the first 100 s. During this window, our estimates of the Blade 1 bending moment and generator power typically span the ground truth, with the estimate of the former performing better overall. In summary, this approach shows promise as a stand-alone technique for reconstructing real-world inflow and turbine dynamics in 1-2 min windows and as a foundation for future time-aware data assimilation techniques.
Original language | American English |
---|---|
Article number | Article No. 012018 |
Number of pages | 10 |
Journal | Journal of Physics: Conference Series |
Volume | 2505 |
Issue number | 1 |
DOIs | |
State | Published - 2023 |
Event | 8th Wake Conference 2023 - Visby, Sweden Duration: 20 Jun 2023 → 22 Jun 2023 |
Bibliographical note
Publisher Copyright:© Published under licence by IOP Publishing Ltd.
NREL Publication Number
- NREL/JA-5000-85335
Keywords
- diffusion model
- large eddy simulation
- turbine inflow