WEBINAR RECORDING

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.

First name

Last name

Company name

Job title

Country

Industry type

How did you hear about us

By completing this form, I confirm that I have read the privacy statement and understood and accept the terms of use.

I would like to receive informational emails with related content in the future from DNV, for example but not limited to invitations to webinars, seminars, newsletters, or access to research that DNV thinks is relevant to me. I can unsubscribe or change my email preferences at any time using the links in the footer of the emails I receive from DNV.

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.