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This paper studies the large-eddy simulation (LES) of isothermal turbulent channel flows. We investigate zero-equation algebraic models without wall function or wall model: functional models, structural models, and mixed models. In addition to models from the literature, new models are proposed and their relevance is examined. Dynamic versions of each type of model are also analyzed. The performance of the subgrid-scale models is assessed using the same finite difference numerical method and physical configuration. The friction Reynolds number of the simulations is 180. Three different mesh resolutions are used. The predictions of large-eddy simulations are compared to those of a direct numerical simulation filtered at the resolution of the LES meshes. The results are more accurate than those of a simulation without model. The predictions of functional eddy-viscosity models can be improved using constant-parameter or dynamic tensorial methods. [ABSTRACT FROM AUTHOR]

Spatial and spectral energy exchanges associated with the turbulence kinetic energy per unit mass, or the half-trace of the velocity covariance tensor, are studied in an anisothermal low Mach number turbulent channel flow. The temperatures of the two channel walls are 293 K and 586 K. This generates a strong temperature gradient in the wall-normal direction. The effect of the temperature gradient on the energy exchanges is investigated using two direct numerical simulations of the channel, at the mean friction Reynolds numbers 180 and 395. The temperature gradient creates an asymmetry between the energy exchanges at the hot and cold sides due to the variations of the local fluid properties and low Reynolds number effects. The low Reynolds number effects are smaller at higher Reynolds numbers, reducing the asymmetry between the hot and cold sides. We also decomposed the energy exchanges in order to study separately the mean-property terms, as found in the constant-property isothermal case, and the thermal terms, specific to flows with variable fluid properties. The significant thermal terms have a similar effect on the flow. Besides, low Reynolds number effects have a negligible impact on thermal terms and only affect mean-property terms. [ABSTRACT FROM AUTHOR]

The large-eddy simulation of wall-bounded turbulent flows at high Reynolds numbers is made more efficient by the use of wall models that predict the wall shear stress, allowing coarser cell sizes at the wall. In this paper, a data-driven approach for the modeling of the wall shear stress is examined using filtered high-fidelity numerical data from two fully developed turbulent channel flows and two turbulent flows with separated regions: a three-dimensional diffuser and a backward-facing step. The model is a multilayer perceptron based on the flow information in the vicinity, given by the distance to the wall and the velocity components at a given number of grid points above the wall. The model is Mach number equivariant at the quasi-incompressible limit, Galilean invariant, statistically rotational invariant and can extrapolate to flow conditions unseen in the training dataset. The relevance of the machine-learning procedure is verified a priori using the filtered numerical data and a posteriori by performing wall-modeled large-eddy simulations implementing the model. The results show that the model is able to leverage the local spatial information to discriminate developed wall turbulence and separated regions in flow configurations not included in the training dataset. • A data-driven procedure can learn a shear stress model for turbulent flows with separation. • The wall model can generalise to a flow configuration not seen during training. • The wall model can leverage the local spatial information to discriminate separated regions. • The wall model can be operate a posteriori in a wall-modelled large-eddy simulation. [ABSTRACT FROM AUTHOR]