Machine-learning wall-model large-eddy simulation accounting for isotropic roughness under local equilibrium

Abstract

We introduce a wall model (WM) for large-eddy simulation (LES) applicable to rough surfaces with Gaussian and non-Gaussian distributions for both transitionally and fully rough regimes. The model is applicable to arbitrary complex geometries where roughness elements are assumed to be underresolved. The wall model is implemented using a feedforward neural network, with the geometric properties of the roughness topology and near-wall flow quantities serving as input. The optimal set of non-dimensional input features is identified using information theory, selecting variables that maximize information about the output while minimizing redundancy among inputs. The model incorporates a confidence score based on Gaussian process modeling, enabling the detection of low model performance for unseen rough surfaces. The model is trained using a direct numerical simulation roughness database comprising approximately 200 cases. The roughness geometries for the database are selected from a large repository through active learning. This approach ensures that the rough surfaces incorporated into the database are the most informative. The model performance is evaluated both a-priori and a-posteriori in WMLES of turbulent channel flows with rough walls. Over 120 channel flow cases are considered, including untrained roughness geometries, roughness Reynolds numbers, and grid resolutions for both transitionally- and fully-rough regimes. The results show that the rough-wall model predicts the wall shear stress within 15% accuracy. The model is also assessed on a high-pressure turbine blade with two different rough surfaces. The WM predicts the skin friction and the mean velocity deficit within 10% accuracy except the region with shock waves.