Convergence and efficiency of global bases using proper orthogonal decomposition for capturing wind turbine wake aerodynamics

The Proper Orthogonal Decomposition (POD) can be applied by combining several datasets, i.e. using data obtained from simulations with different governing parameters. The resulting global POD basis is intended to represent all flows within the parameter space, and the convergence and efficiency of such global POD bases are evaluated here for the case of wind turbine wake aerodynamics in large wind farms. The analysis shows that the global POD bases have better performance across the parameter space than the optimal POD basis computed from a single dataset. The error associated with using a global basis across parameter space of reconstructions decreases and converges as more datasets are included, and there is a low sensitivity as to which datasets to include. It is also shown how this error is an order of magnitude smaller than the truncation error for 100 modes. Finally, the global basis applied to the highly turbulent flow within a wind farm has the advantage of providing consistent physical interpretability.