Multi-domain physics-informed neural networks for dynamic yaw-induced wind farm wake reconstruction

Abstract

Wind turbine wakes significantly influence wind farm performance, particularly under active yaw control used for wake redirection. Accurate wake reconstruction requires resolving the large spatial extent and multi-scale variability of wind-farm flows, making single-network training computationally demanding. A multi-domain formulation addresses this challenge by decomposing the global domain into smaller subregions, allowing localized learning while remaining compatible with sparse offshore measurements. Accordingly, a multi-domain physics-informed neural network (md-PINN) framework is developed for reconstructing unsteady wake fields of offshore wind turbines subjected to active yaw control. We begin by developing a standard PINN to reconstruct the wake behind a single wind turbine, incorporating Navier–Stokes constraints to enforce physical consistency. Building on this, a domain decomposition strategy is applied to divide the wind farm into multiple subdomains, each modeled using an independent PINN, thus forming the md-PINN framework. Validation against large eddy simulation (LES) data shows that the md-PINN framework accurately reconstructs velocity fields, wake trajectories, wake widths, and power outputs in downstream regions affected by complex wake interactions, while exhibiting improved robustness compared to a single full-domain PINN. In addition, the proposed framework naturally supports parallel training of sub-models, leading to a significant reduction in computational cost and enhanced scalability for sparse-data-based wake reconstruction in large wind farms.