FlowFormer: Toward a foundation model for full-flow-field wind farm wake modeling

The foundation model has recently attracted significant attention due to its exceptional generalisability and outstanding adaptability. However, when it comes to data-driven wind farm wake modelling, due to the high cost of data generation and the complexity of flow characteristics, dimension reduction technology is still the mainstream pre-processing procedure to alleviate the significant challenge inherent in the task itself. Existing methods are still far from achieving a foundation model with high adaptability and excellent scalability. To fill the research, we propose the FlowFormer framework to serve as a foundation model. Specifically, we design a Transformer-based framework, FlowFormer, for flow field prediction by directly taking the LES-simulated data without introducing any dimensionality reduction operations. Moreover, a semi-supervised training strategy is designed to address the problem of over-fitting caused by dimension complexity. The overall mean absolute error of the developed FlowFormer is 6.660% compared to the freestream wind speed for multi-step iterative prediction. The results for a utility-scale farm consisting of 81 turbines demonstrate the high adaptability and excellent scalability of the proposed FlowFormer. Significantly, a qualitative experiment demonstrates that FlowFormer can handle changing-yaw conditions using only fixed-yaw training data, highlighting its excellent flexibility. The demo is available at https://github.com/warwick-icse/FlowFormer .