A Baseline for Ensemble-Based, Time-Resolved Inflow Reconstruction for a Single Turbine Using Large-Eddy Simulations and Latent Diffusion Models

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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 languageAmerican English
Article numberArticle No. 012018
Number of pages10
JournalJournal of Physics: Conference Series
Volume2505
Issue number1
DOIs
StatePublished - 2023
Event8th Wake Conference 2023 - Visby, Sweden
Duration: 20 Jun 202322 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

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