Bulletin of the American Physical Society
75th Annual Meeting of the Division of Fluid Dynamics
Volume 67, Number 19
Sunday–Tuesday, November 20–22, 2022; Indiana Convention Center, Indianapolis, Indiana.
Session G24: Boundary Layers: LES Wall Modeling |
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Chair: Adrian Lozano-Duran, MIT Room: 232 |
Sunday, November 20, 2022 3:00PM - 3:13PM |
G24.00001: Eddy-viscosity wall boundary condition for wall-modeled large-eddy simulation in a finite-difference framework H. Jane Bae, Adrian Lozano-Duran We study the effect of wall boundary conditions on the statistics in a wall-modeled large-eddy simulation (WMLES) of turbulent channel flows. We consider a no-slip condition at the wall in which the wall stress is imposed by adjusting the value of the eddy viscosity at the wall and compare it with the widely used Neumann boundary condition. The results show that the type of boundary condition utilized has an impact on the statistics (e.g., mean velocity profile and turbulence intensities) in the vicinity of the wall, especially at the first off-wall grid point. Augmenting the eddy viscosity at the wall results in improved predictions of statistics in the near-wall region, which should allow the use of information from the first off-wall grid point for wall models without additional spatial or temporal filtering. This boundary condition is easy to implement and provides a simple solution to the well-known log-layer mismatch in WMLES. |
Sunday, November 20, 2022 3:13PM - 3:26PM |
G24.00002: Solution Enrichment Wall-Modeled LES in the Spectral Element Method Framework Steven R Brill, Pinaki Pal, Muhsin Ameen, Chao Xu, Matthias Ihme We developed an enriched wall-model for the spectral element method (SEM) based wall-modeled large-eddy |
Sunday, November 20, 2022 3:26PM - 3:39PM |
G24.00003: Wall model for large-eddy simulation of compressible turbulent flows Kevin P Griffin, Lin Fu, Parviz Moin We propose a wall model for large-eddy simulation of compressible wall-bounded turbulent flows. The model is based on an assumed incompressible velocity profile and the inverse of a recent compressible velocity transformation [Griffin et al. PNAS. 118: 34, 2021]. The model is tested in turbulent boundary layers, channel flows, and a flow with spatially varying streamwise pressure gradients. Compared with state-of-the-art models, the present model provides improved predictions of velocity and temperature profiles, skin friction, and heat flux without significant increased computational cost. The largest improvements in accuracy are achieved in cases with strong heat transfer. |
Sunday, November 20, 2022 3:39PM - 3:52PM |
G24.00004: On the grid convergence in wall-modeled large-eddy simulation Xiaohan Hu, George I Park Wall modeled large-eddy simulation (WMLES) has become a popular computational tool for high Reynolds number turbulent flows, as it achieves a balance between cost and accuracy. However, grid convergence in WMLES has not been well understood due to the conflicting objectives of grid convergence and use of increasingly coarse near-wall grids for wall modeling. In this work, we propose that the extent of the wall-modeled region critically affects the convergence behavior of WMLES. For a fixed extent of the wall-modeled region, once the turbulence scales are well resolved at the location where the LES data are sampled, the wall-model input is likely unchanged with further grid refinements, leading to the converged model output (wall stress) and therefore convergence in WMLES. This proposition is being examined in a turbulent channel flow and a three dimensional turbulent boundary layer with a rotating freestream velocity vector. We suspect that the extent of the wall-modeled region plays a role similar to the filter size in the explicitly filtered LES, dictating the resolution at which the WMLES converges and the error therein. In this scenario, WMLES would tend to converge at a coarser grid resolution when the wall-model region extends further away from the wall. |
Sunday, November 20, 2022 3:52PM - 4:05PM |
G24.00005: Wall-modeling of turbulent flows over a periodic hill using multi-agent reinforcement learning Di Zhou, Jane Bae A wall model for large-eddy simulation (LES) that can adapt to various pressure-gradient effects is developed for turbulent flow over a periodic hill using multi-agent reinforcement learning. A finite-volume unstructured incompressible LES solver is coupled with an open-source reinforcement learning tool to enable reinforcement learning in flow over complex geometries. In the training process, we simulate low-Reynolds-number flow over a periodic hill with reinforcement learning agents distributed on the wall of periodic hill, along the computational grid points. Each agent receives states based on local instantaneous flow quantities and a reward based on the estimated wall-shear stress, then provides local actions to update the wall boundary condition at each time step. The agents infer a single optimized policy through their repeated interactions with the flow field to maximize its cumulative long-term reward. The trained wall model is validated in higher-Reynolds-number simulations of the periodic hill configuration, and the results show the robustness of the model on flow with pressure gradients. |
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