Toward ultra-efficient high fidelity predictions of wind turbine wakes: Augmenting the accuracy of engineering models via LES-trained machine learning
arxiv(2024)
摘要
This study proposes a novel machine learning (ML) methodology for the
efficient and cost-effective prediction of high-fidelity three-dimensional
velocity fields in the wake of utility-scale turbines. The model consists of an
auto-encoder convolutional neural network with U-Net skipped connections,
fine-tuned using high-fidelity data from large-eddy simulations (LES). The
trained model takes the low-fidelity velocity field cost-effectively generated
from the analytical engineering wake model as input and produces the
high-fidelity velocity fields. The accuracy of the proposed ML model is
demonstrated in a utility-scale wind farm for which datasets of wake flow
fields were previously generated using LES under various wind speeds, wind
directions, and yaw angles. Comparing the ML model results with those of LES,
the ML model was shown to reduce the error in the prediction from 20
from the GCH model to less than 5
non-symmetric wake deflection observed for opposing yaw angles for wake
steering cases, demonstrating a greater accuracy than the GCH model. The
computational cost of the ML model is on par with that of the analytical wake
model while generating numerical outcomes nearly as accurate as those of the
high-fidelity LES.
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