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In this work, a data-driven wall model for turbulent flows over periodichills is developed using the feedforward neural network (FNN) and wall-resolvedLES (WRLES) data. To develop a wall model applicable to different flow regimes,the flow data in the near wall region at all streamwise locations are groupedtogether as the training dataset. In the developed FNN wall models, we employthe wall-normal distance, near-wall velocities and pressure gradients as inputfeatures and the wall shear stresses as output labels, respectively. Theprediction accuracy and generalization capacity of the trained FNN wall modelare examined by comparing the predicted wall shear stresses with the WRLESdata. For the instantaneous wall shear stress, the FNN predictions show anoverall good agreement with the WRLES data with some discrepancies observed atlocations near the crest of the hill. The correlation coefficients between theFNN predictions and WRLES predictions are larger than 0.7 at most streamwiselocations. For the mean wall shear stress, the FNN predictions agree very wellwith WRLES data. More importantly, overall good performance of the FNN wallmodel is observed for different Reynolds numbers, demonstrating its goodgeneralization capacity.

A wall model based on neural networks for LES of turbulent flows over periodic hills