108 research outputs found
Sensitivity analysis of wall-modeled large-eddy simulation for separated turbulent flow
In this study, we conduct a parametric analysis to evaluate the sensitivities
of wall-modeled large-eddy simulation (LES) with respect to subgrid-scale (SGS)
models, mesh resolution, wall boundary conditions and mesh anisotropy. While
such investigations have been conducted for attached/flat-plate flow
configurations, systematic studies specifically targeting turbulent flows with
separation are notably sparse. To bridge this gap, our study focuses on the
flow over a two-dimensional Gaussian-shaped bump at a moderately high Reynolds
number, which involves smooth-body separation of a turbulent boundary layer
under pressure-gradient and surface-curvature effects. In the simulations, the
no-slip condition at the wall is replaced by three different forms of boundary
condition based on the thin boundary layer equations and the mean wall-shear
stress from high-fidelity numerical simulation to avoid the additional
complexity of modeling the wall-shear stress. Various statistics, including the
mean separation bubble size, mean velocity profile, and eddy viscosity from SGS
model, are compared and analyzed. The results reveal that capturing the
separation bubble strongly depends on the choice of SGS model. While grid
convergence can be achieved at a resolution comparable to wall-resolved LES
mesh, above this limit, the LES predictions exhibit intricate sensitivities to
mesh resolution. Furthermore, both wall boundary conditions and the anisotropy
of mesh cells exert discernible impacts on the turbulent flow predictions, yet
the magnitudes of these impacts vary based on the specific SGS model chosen for
the simulation
Scientific multi-agent reinforcement learning for wall-models of turbulent flows
The predictive capabilities of turbulent flow simulations, critical for
aerodynamic design and weather prediction, hinge on the choice of turbulence
models. The abundance of data from experiments and simulations and the advent
of machine learning have provided a boost to these modeling efforts. However,
simulations of turbulent flows remain hindered by the inability of heuristics
and supervised learning to model the near-wall dynamics. We address this
challenge by introducing scientific multi-agent reinforcement learning
(SciMARL) for the discovery of wall models for large-eddy simulations (LES). In
SciMARL, discretization points act also as cooperating agents that learn to
supply the LES closure model. The agents self-learn using limited data and
generalize to extreme Reynolds numbers and previously unseen geometries. The
present simulations reduce by several orders of magnitude the computational
cost over fully-resolved simulations while reproducing key flow quantities. We
believe that SciMARL creates new capabilities for the simulation of turbulent
flows
Wall Modeling of Turbulent Flows with Various Pressure Gradients Using Multi-Agent Reinforcement Learning
We propose a framework for developing wall models for large-eddy simulation
that is able to capture pressure-gradient effects using multi-agent
reinforcement learning. Within this framework, the distributed reinforcement
learning agents receive off-wall environmental states including pressure
gradient and turbulence strain rate, ensuring adaptability to a wide range of
flows characterized by pressure-gradient effects and separations. Based on
these states, the agents determine an action to adjust the wall eddy viscosity,
and consequently the wall-shear stress. The model training is in-situ with
wall-modeled large-eddy simulation grid resolutions and does not rely on the
instantaneous velocity fields from high-fidelity simulations. Throughout the
training, the agents compute rewards from the relative error in the estimated
wall-shear stress, which allows the agents to refine an optimal control policy
that minimizes prediction errors. Employing this framework, wall models are
trained for two distinct subgrid-scale models using low-Reynolds-number flow
over periodic hills. These models are validated through simulations of flows
over periodic hills at higher Reynolds numbers and flow over the Boeing
Gaussian bump. The developed wall models successfully capture the acceleration
and deceleration of wall-bounded turbulent flows under pressure gradients and
outperform the equilibrium wall model in predicting skin friction.Comment: arXiv admin note: substantial text overlap with arXiv:2211.1642
Characterization of vortex regeneration mechanism in the self-sustaining process of wall-bounded flows using resolvent analysis
The regeneration mechanism of streamwise vortical structures in the self-sustaining process of wall-bounded turbulence is investigated. Resolvent analysis [1] is used to identify the principal forcing mode which produces the maximum amplification of the response modes in the minimal channel for the buffer [2] and logarithmic layer [3]. The identified mode is then projected out from the nonlinear term of the Navier-Stokes equations at each time step from the direct numerical simulations (DNS) of the corresponding minimal channel. The results show that the removal of the principal forcing mode is able to significantly inhibit turbulence for the buffer and logarithmic layer while removing the subsequent modes instead of the principal one only marginally affects the flow. Analysis of the dyadic interactions in the nonlinear term shows that the contributions toward the principal forcing mode come from a limited number of wavenumber interactions. Using conditional averaging, the flow structures that are responsible for generating the principal forcing mode, and thus the nonlinear interaction to self-sustain turbulence, are identified to be spanwise rolls interacting with meandering streaks
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