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April 18, 2026IET conference proceedings.0 citations

Hybrid Yawed Wake Modeling Using Steady-State Wind Data and Deep Learning

Hybrid yawed wake modeling: fusing steady-state wind field data and deep learning

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Authors

KLKunwei LinXTXiaoyu TangWSWeiting Song

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Overview

Demonstrates a hybrid method predicting wake dynamics in wind farms, suggesting improved power output and structural stability.

Key Points

  • The aim is to develop an efficient model for predicting yaw wake behavior in wind farms to optimize power output.
  • Combines steady-state CFD data with a deep learning algorithm.
  • Creates a database of multi-turbine yaw conditions using an actuator disk model and RANS k-ɛ turbulence model.
  • Trains a deep neural network to rapidly predict inflow velocities for various yaw configurations.
  • Achieves a balance between accuracy and computational efficiency.
  • Supports real-time yaw control in wind farms.
  • Improves predictions compared to conventional modeling techniques.

Cite This Study

Lin et al. (2026) studied this question.

synapsesocial.com/papers/69e3216540886becb6540b15https://doi.org/10.1049/icp.2026.0488
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