Addressing offshore wake prediction biases
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.
Agenda:
1. Wake prediction bias concerns and discussion
Presenters: Tom Levick, Product Owner, WindFarmer Modelling, and Ben Williams, Global lead for Offshore EPA practice
- Major offshore developers have identified prediction biases.
- Discussion of Ørsted’s WakeTester results and the RWE – DNV long range wake validation study
2. Case studies and RANS CFD validation results
Presenter: Christiane Montavon, Principal Engineer, CFD expert
3. An introduction to DNV’s fast CFD surrogate model, CFD.ML
Presenter: Karol Mitraszewski, Scientific Developer, WindFarmer