Imagine trying to predict exactly how much wind hits a giant sail that is 400 meters tall, while other sails nearby are blocking the breeze. This project builds a digital map and a set of tools to predict these wind patterns more accurately. It helps engineers know exactly how much energy they will get and how much wear and tear the machines will face.
Current wind prediction methods lack the accuracy needed for massive GW-scale farms and ultra-tall turbines, leading to financial risk and grid instability.
A modular simulation toolset and an API integrating Pywake, Foxes, and Wayve, supported by a FAIR data hub based on the New European Wind Atlas.
If you are a developer dealing with unpredictable energy yields in complex terrains — this project developed simulation tools that reduce economic uncertainties and increase productivity. This helps in lowering the Levelised Cost of Energy (LCoE).
If you are an OEM dealing with structural fatigue in 400-metre tall turbines — this project developed prediction methods for load performance. This allows for joint optimization between the manufacturer and the site developer.
If you are a grid operator dealing with unstable power inputs from GW-scale wind farms — this project developed more accurate production statistics. This leads to increased grid stability.
The project aims to lower the Levelised Cost of Energy (LCoE) by reducing uncertainties and increasing the productivity of wind systems.
Yes, it is specifically designed for GW-scale and 400-metre tall offshore and onshore wind energy systems.
The project is establishing an open-source knowledge hub and a FAIR data hub to benefit the entire renewable energy sector.
Industry adoption is facilitated through a computationally efficient modular framework and an Application Programming Interface (API) for tools like Pywake and Foxes.
The project period runs from 2023-01-01 to 2026-12-31, with current work focusing on simulation and experimental data generation.
The consortium is heavily weighted toward industrial application, with 6 industry partners (55% ratio) including major players like Vestas, SGRE, GE, and EDF. This strong industry presence, combined with 4 universities and 1 research center across 7 countries, ensures that the resulting simulation tools are compatible with actual operational processes and private datasets.
Contact Danmarks Tekniske Universitet (DTU) regarding the FLOW open-source hub.
Contact us to find out how to integrate these open-source wind models into your asset management.
