Periodic Reporting for period 1 - FLOW (Atmospheric Flow, Loads and pOwer for Wind energy)
Reporting period: 2023-01-01 to 2023-12-31
The objective of the FLOW project is to develop new and innovative prediction methods for production statistics and load performance of modern GW-scale and 400-metre tall offshore and onshore wind energy systems. Our project will develop more accurate methods as regards the present state of the art (SOTA) and with high confidence, thereby reducing uncertainties, increasing productivity and grid stability, lowering Levelised Cost of Energy (LCoE), while establishing an open-source knowledge hub that will benefit the entire renewable energy sector and enable joint optimization between developers and OEMs. To reach these ambitions, FLOW will improve the knowledge of atmospheric flow physics and the interplay between wind farm (microscale) and large-scale (mesoscale) processes such as: wind farm global blockage, farm-farm interaction, and topographic and wake-added turbulence in complex terrain. Based on these principles, FLOW will develop and validate simulation tools that can be readily adopted by industry to lower economic uncertainties and enhance system reliability and power production, with wide economic and societal impacts. The proposed modelling framework will make extensive use of public experimental datasets to validate and train models within a FAIR data hub. The New European Wind Atlas (NEWA) database is the foundation to grow this innovative open-source ecosystem that links experimental data, flow models, and validation datasets for benchmarking and training. Industry adoption will be facilitated through a computationally efficient modular framework that allows scalability in a production environment. FLOW’s industrial partners, comprised of VESTAS, SGRE, and EDF, will verify compliance with operational processes and test the framework using private datasets to extend the validation range and demonstrate the added value of our results at a wide European scale.
The first year of the FLOW project focused on generation of high-fidelity simulation data, and experimental data from field campaigns using remote sensing lidars. These activities provide data for the model validations at later stages of the project. Another focus area was the modelling activities progressing the WRF mesoscale modelling, engineering modelling tools, and theoretical turbulence models adapted for regimes relevant for taller turbines. In addition, an Application Programming Interface (API) for integration of the modelling tools used, i.e. Pywake, Foxes, Wayve and Code_saturne was developed as well as a knowledge hub cataloguing the data. The hub development is done I dialogue with the sibling projects Meridional and Aire under the same call.
The experimental campaigns done under the auspice of the FLOW project and the extensive generation of high-fidelity data sets focusing on low level jets as well as shallow boundary layers provide unique validation data for modelling activities within FLOW and beyond through the FLOW hub access point which is crucial for ensuring further uptake and success. Another uptake-enabler is the common API for the various sub tools, and a key resource for success is the availability of industrial wind turbine operational data for massive model validation. The turbulence model extensions developed in FLOW have the potential to alter the current inflow wind field models used in the IEC standards on wind turbine design regarding the inclusion of a “super statistical” covariance structure of the wind fields as well as the incorporation of large-scale anisotropic wind field structures in the classical Mann turbulence model.