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This is a Quantitative Study study.

September 12, 2025

Channel-assisted Fourier neural operator for high-fidelity far-wake prediction of a wind turbine under yawed conditions

Key Points

CFNO achieves 0.89% prediction error on a high-fidelity far-wake grid, demonstrating effective wake modeling.
With a design that reduces parameters by about 23%, CFNO offers a streamlined yet accurate predictive approach.
Validated on large-eddy simulation data, CFNO stabilizes long-term predictions while running efficiently on a single GPU.
The method enables enhanced wake steering and control, highlighting its potential to improve wind energy performance.

Abstract

The channel-assisted Fourier neural operator (CFNO) for real-time, high-resolution, long-horizon prediction of yawed wind-turbine far wakes is introduced. CFNO injects yaw through a channel-assisted layer and combines a post-channel-mixing depthwise-separable spectral convolution (DSSC) with real-valued weights and residual-enhanced Fourier layers. These choices stabilize long autoregressive predictions and reduce parameters to about 23% of the Fourier neural operator (FNO) and about 50% of DSSC-FNO. CFNO is trained and validated on large-eddy simulation (LES) produced with an actuator-disk model with rotation (ADM-R), which closely reproduces far-wake behavior. It achieves 0.89% error on a 180 × 70 high-fidelity far-wake grid (up to x/D=12), exceeds an FNO baseline, and runs at millisecond-to-sub-second latency on a single GPU. These capabilities enable digital-twin-assisted wake steering and differentiable, yaw-based model-predictive control with potential to reduce wake losses, moderate cyclic loads, and stabilize aggregate power.

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