Towards Multi-Fidelity Deep Learning of Wind Turbine Wakes

Suraj Pawar, Ashesh Sharma, Ganesh Vijayakumar, Christopher Bay, Shashank Yellapantula, Omer San

Research output: Contribution to journalArticlepeer-review

10 Scopus Citations

Abstract

Engineering wake models that accurately predict wake in a computationally efficient manner are very important for tasks such as layout optimization and control of wind farms. In this paper, we explore an application of deep learning (DL) to learn the wake model from hierarchies of physics-based approaches ranging from analytical models to an approximate form of the Reynolds-averaged Navier–Stokes equations. We first illustrate the application of principal component analysis to obtain a lower-dimensional representation that allows a computationally tractable training and deployment of DL models. Then, the DL model is trained to learn the mapping from input parameter space to the principal components, which are then used to reconstruct the three-dimensional flow field. Additionally, we investigate a composite framework consisting of two neural networks to learn the correlation between low- and high-fidelity data with Gauss and curl models treated as proxies for low- and high-fidelity models, respectively. The prediction from both DL models matches well with the high-fidelity data with a maximum relative percentage error for the kinetic energy flux of <1%. This work opens up possibilities for data-efficient construction of surrogate models for wake prediction that can be used to study the influence of wind speed and yaw angles on wind farm power production.

Original languageAmerican English
Pages (from-to)867-879
Number of pages13
JournalRenewable Energy
Volume200
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Ltd

NREL Publication Number

  • NREL/JA-5000-83844

Keywords

  • Deep learning
  • Dimensionality reduction
  • Multi-fidelity data fusion
  • Wake prediction
  • Wind energy

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