Adaptive Multitask Neural Network for High-Fidelity Wake Flow Modeling of Wind Farms Article Swipe
Dichang Zhang
,
Christian Santoni
,
Zexia Zhang
,
Dimitris Samaras
,
Ali Khosronejad
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/en18112897
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/en18112897
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/en18112897
- https://www.mdpi.com/1996-1073/18/11/2897/pdf?version=1748706555
- OA Status
- gold
- References
- 63
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410960852
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4410960852Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/en18112897Digital Object Identifier
- Title
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Adaptive Multitask Neural Network for High-Fidelity Wake Flow Modeling of Wind FarmsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-31Full publication date if available
- Authors
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Dichang Zhang, Christian Santoni, Zexia Zhang, Dimitris Samaras, Ali KhosronejadList of authors in order
- Landing page
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https://doi.org/10.3390/en18112897Publisher landing page
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https://www.mdpi.com/1996-1073/18/11/2897/pdf?version=1748706555Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1996-1073/18/11/2897/pdf?version=1748706555Direct OA link when available
- Concepts
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Computer science, Fidelity, Scalability, Artificial neural network, Wake, Wind speed, High fidelity, Field (mathematics), Simulation, Machine learning, Real-time computing, Engineering, Aerospace engineering, Meteorology, Database, Pure mathematics, Telecommunications, Electrical engineering, Mathematics, PhysicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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