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Watch our webinar: Addressing offshore wake prediction biases

Validation of wake prediction and enhancing turbine energy prediction accuracy with innovative modelling approaches in WindFarmer and high-fidelity RANS CFD.

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Learn more about DNV’s latest response to wake predictions bias concerns and discover new modelling approaches to improve wake and blockage prediction accuracy.

Key learnings:

During the webinar we are delving into wake predictions concerns, exploring new modelling approaches and examining independent validation results. We are also covering the results from Ørsted’s WakeTester framework, which shows a bias towards energy underpredictions in wake interactions.  In the second part of our webinar, we are introducing a CFD case study, showcasing the use of the Weather Research and Forecasting (WRF) model to define boundary conditions and enhance accuracy in predicting cluster wakes and blockages. We are featuring CFD.ML, a rapid turbine interaction model that utilizes machine learning techniques to create a surrogate for high-fidelity CFD simulations.