Validation of RANS-calibrated engineering models and ANN-based surrogate for wind farm flow simulation and layout optimization

Abstract. Accurate yet efficient wake modeling is essential for wind farm layout optimization (WFLO). Wind turbine wakes are disturbed regions of flow behind a wind turbine, characterized by lower mean wind speeds and higher turbulence, which reduce downstream power production and increase structural loading. This study compares an Artificial Neural Network (ANN)-based surrogate trained on Reynolds Averaged Navier-Stokes (RANS) data with two representative engineering wake models based on the TurbOPark and Super-Gaussian formulations. The work includes recalibration of the engineering models, a systematic flow simulation study across varying turbine counts and spacings, and WFLO benchmarks validated against RANS-based Annual Energy Production (AEP). Results show that the ANN surrogate achieves the lowest RMSE and MAPE across all scenarios in flow estimation, albeit at a higher computational cost. In WFLO, the TurbOPark-based model produced the highest RANS-validated AEP layouts, despite having lower predictive accuracy, suggesting that optimization complexity influences outcomes. Blockage modeling increased computational cost without improving accuracy.