Hybrid Flow-Field Modeling via Parameter-Space Intermediate Flow Estimation and Deep Learning for Wind-Farm Yaw Wakes

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Abstract
This study proposes a hybrid flow-field modeling framework that integrates physics-based simulation, parameter-space interpolation, and deep learning for rapid and high-accuracy prediction of wind farm wakes under uniform yaw conditions. First, a steady-state CFD yaw-wake simulation is developed to produce physically consistent and high-quality dataset. Second, this paper rigorously proves the Lipschitz continuity of steady yaw wake solutions and their image representations with respect to the input parameters. Based on this theoretical basis, we propose the Parameter-Space Intermediate Flow Estimation method (PS-IFE) for steady wake image augmentation, which recasts video frame interpolation into the parameter space to generate intermediate-condition steady wakes. Finally, a lightweight deep neural network (DNN) is trained on the augmented dataset for data-driven uniform-yaw wake modeling. In a 55 wind farm with 7D spacing using the NREL 5 MW turbine, PS-IFE achieves augmentation errors as low as RMSE: 0.063 m/s and MAPE: 0.584%, outperforming conventional augmentation algorithms with tailored superiority. Sensitivity analysis further recommends m/s for reliable interpolation path design. The resulting data-driven yaw wake model attains RMSE: 0.127 m/s and MAPE: 1.041%, about 75% lower than the Bastankhah model, while maintaining millisecond-level inference time. The proposed framework maintains physical consistency while achieving both high accuracy and high efficiency, with strong scalability to other complex flow-field modeling scenarios.

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