Artificial neural network-based wall-modeled large-eddy simulations of turbulent channel and separated boundary layer flows

Wall-models in a large-eddy simulation (LES) are essential to alleviate the large near-wall resolution requirements for high-Reynolds-number turbulent flow simulations. Among the existing wall-models for a LES, an equilibrium wall-stress model has the highest computational efficiency. Because this model has limitations, such as a lack of non-equilibrium effects and the assumption of a particular law of the wall in the mean velocity, we propose artificial neural network-based wall-stress models (AWMs). The input variables for the AWMs are extracted from the decomposition of the skin-friction coefficient proposed by Fukagata et al. [1], and the AWMs are shown to be able to predict the wall-shear stress in complex flows accurately. The performance of the AWMs is tested for two types of flows, a fully developed turbulent channel flow and a separated turbulent boundary layer flow. A direct comparison of the turbulence statistics with those obtained by previous wall-models (i.e., a log-law-based wall-stress model and a non-equilibrium wall-stress model) shows that better predictions are achieved using the AWMs for both flows, even with untrained Reynolds numbers. When using a coarse grid along the wall-normal direction in wall-modeled LESs (WMLESs) with the AWMs, an upward shift of the mean velocity profile (positive log-layer mismatch, LLM) compared to direct numerical simulation data is found, consistent with previous studies. However, this LLM problem can be overcome by imposing a filtered wall-normal velocity at the wall that is dynamically determined based on the continuity equation and the Taylor series expansion within wall-adjacent cells.(c) 2022 Elsevier Masson SAS. All rights reserved.

Automated summary

Wall-models are crucial in large-eddy simulations (LES) to reduce the computational requirements for resolving near-wall regions in high-Reynolds-number turbulent flows. The proposed artificial neural network-based wall-stress models (AWMs) overcome limitations of the traditional equilibrium models, and demonstrate accurate predictions of wall-shear stress in complex flows. Comparative studies show that AWMs outperform previous wall-models in terms of turbulence statistics for both fully developed turbulent channel flow and separated turbulent boundary layer flow even at untrained Reynolds numbers. Additionally, the issue of log-law mismatch in wall-modeled LESs (WMLESs) with AWMs can be resolved by implementing a dynamically determined filtered wall-normal velocity at the wall based on continuity equation and Taylor series expansion in wall-adjacent cells.