End-to-end Wind Turbine Wake Modelling with Deep Graph Representation Learning
This addresses wind farm optimization and control by providing a flexible, generic deep learning approach for computational fluid dynamics simulations, though it is incremental as it builds on existing graph neural network methods.
The paper tackled wind turbine wake modeling by proposing a surrogate model using a graph neural network that operates on unstructured meshes, achieving accurate 3D flow field predictions validated against high-fidelity data for various conditions and demonstrating generalization to unseen data and farm-scale power prediction.
Wind turbine wake modelling is of crucial importance to accurate resource assessment, to layout optimisation, and to the operational control of wind farms. This work proposes a surrogate model for the representation of wind turbine wakes based on a state-of-the-art graph representation learning method termed a graph neural network. The proposed end-to-end deep learning model operates directly on unstructured meshes and has been validated against high-fidelity data, demonstrating its ability to rapidly make accurate 3D flow field predictions for various inlet conditions and turbine yaw angles. The specific graph neural network model employed here is shown to generalise well to unseen data and is less sensitive to over-smoothing compared to common graph neural networks. A case study based upon a real world wind farm further demonstrates the capability of the proposed approach to predict farm scale power generation. Moreover, the proposed graph neural network framework is flexible and highly generic and as formulated here can be applied to any steady state computational fluid dynamics simulations on unstructured meshes.