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Poster Communications Year : 2021

Real-time unsteady air flow prediction to reduces mechanic load variations and wind turbine maintenance costs

Abstract

For actively controlling aerodynamic systems – like Wind Turbine (WT) blades -- it can be necessary to estimate in real-time and predict the air flow around those systems. We propose here a new method which combines machine learning, physical models and measurements for this purpose. Very good numerical results have been obtained on wake flows.
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Dates and versions

hal-03278863 , version 1 (06-07-2021)

Identifiers

  • HAL Id : hal-03278863 , version 1

Cite

Valentin Resseguier, Matheus Ladvig, Agustin Martin Picard, Etienne Mémin, Dominique Heitz, et al.. Real-time unsteady air flow prediction to reduces mechanic load variations and wind turbine maintenance costs. SEANERGY 2021 - International leading event on offshore wind and marine renewable energy, Sep 2021, Nantes, Saint-Nazaire, France. pp.1. ⟨hal-03278863⟩
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