Researchers from Aarhus University aim to enable the use of data-driven deep learning models for wind farm flow simulations, optimisation and control. Over the past two decades, numerical simulations of wind farms using computational fluid dynamics (CFD) have received a great deal of attention.
Although high-fidelity numerical techniques can provide detailed information about wind-farm-atmosphere interactions, they are not yet feasible to be used in the design, optimisation, and real-time applications due to their high computational complexity.
A new research project led by Aarhus University aims to change that. The new research project, which is backed by the Independent Research Fund Denmark with a grant of DKK 2.8 million, aims at a paradigm shift in the development of wind farm flow models going from purely physics-based towards physics-informed data-driven models.
With this project, they propose to improve the entire wind farm modelling framework by using physics-informed machine learning to predict complex interactions between atmospheric turbulent flows, the wind farm and its environment. The new framework will collect and use big data in real environments to capture these interactions closely, which are neglected in today's engineering wake models. Their hypothesis is that this will deliver a level of accuracy comparable to high-fidelity solutions with a considerably lower computational cost. The project starts in 2021 and runs for three years.