DYNAMICS OF WT - WP6

The harvesting of offshore wind energy by floating WTs requires good knowledge of turbines as well as of their behaviour when collectively operating in wind farms. For the design process of such facilities, it is essential to work with accurate wind farm models.

They are combining a WT model with a flow model; both can have different levels of fidelity depending on their position in the wind farm design process.

They need to be validated, but the direct validation of these models with realistic large scale experiments is problematic because of several constraints like accessibility, environmental situations etc.

Wind tunnel experiments on small scale models play an essential role for such model developments, as laboratory conditions provide access to many aspects and to reproducible environmental conditions and finally, such set ups are affordable.

Such experiments clearly have to be designed on the background of the realistic large scale systems, thus this WP will be very closely linked to the other WPs (WP4, WP5) providing information on inflow conditions as well as motion dynamics of floating systems. 

It should be mentioned that most of the set-ups (active grid for inflow conditions) and models (VAWTs and HAWTs with pitch control) used in this WP6 will come from former European and national projects (FP7 Innwind.eu, French ANR project EFL_2). ESRs will share models and facilities through their secondments with academic partners.
 

The partners (CNR, CNRS, ENSMA, HYDROQUEST, POLIMI, UOLD, GICON) have long track-records in WT design.

They will jointly address the following objectives:
 

• Design an experimental set-up allowing the reproduction of realistic environmental conditions (inflow and motions) in the wind tunnel for various VAWTs and HAWTs
• Develop, tune and validate numerical tools of different complexity, both for horizontal and vertical axis WTs
• Analyse reciprocal interaction between turbines and their wakes (for instance a basis to develop wind farm controllers)
• Quantify how the further developed models can contribute to reducing the LCOE