Adaptive Multitask Neural Network for High-Fidelity Wake Flow Modeling of Wind Farms

Dichang Zhang, Christian Santoni, Zexia Zhang, Dimitris Samaras and Ali Khosronejad ()
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Dichang Zhang: Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA
Christian Santoni: Department of Civil Engineering, Stony Brook University, Stony Brook, NY 11794, USA
Zexia Zhang: Department of Civil Engineering, Stony Brook University, Stony Brook, NY 11794, USA
Dimitris Samaras: Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA
Ali Khosronejad: Department of Civil Engineering, Stony Brook University, Stony Brook, NY 11794, USA

Energies, 2025, vol. 18, issue 11, 1-20

Abstract: Wind turbine wake modeling is critical for the design and optimization of wind farms. Traditional methods often struggle with the trade-off between accuracy and computational cost. Recently, data-driven neural networks have emerged as a promising solution, offering both high fidelity and fast inference speeds. To advance this field, a novel machine learning model has been developed to predict wind farm mean flow fields through an adaptive multi-fidelity framework. This model extends transfer-learning-based high-dimensional multi-fidelity modeling to scenarios where varying fidelity levels correspond to distinct physical models, rather than merely differing grid resolutions. Built upon a U-Net architecture and incorporating a wind farm parameter encoder, our framework integrates high-fidelity large-eddy simulation (LES) data with a low-fidelity engineering wake model. By directly predicting time-averaged velocity fields from wind farm parameters, our approach eliminates the need for computationally expensive simulations during inference, achieving real-time performance ( 1.32 × 10 − 5 GPU hours per instance with negligible CPU workload). Comparisons against field-measured data demonstrate that the model accurately approximates high-fidelity LES predictions, even when trained with limited high-fidelity data. Furthermore, its end-to-end extensible design allows full differentiability and seamless integration of multiple fidelity levels, providing a versatile and scalable solution for various downstream tasks, including wind farm control co-design.

Keywords: wind farms; machine learning; transfer learning; surrogate model; multi-fidelity modeling; large eddy simulation; engineering wake model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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