Macroscopics¶
This page collects the macroscopic quantities the solver works with and gives each one its discrete moment definition. It is the practical companion to the Chapman-Enskog derivation: that page proved which equations the lattice recovers and why; this page lists what you read off the populations to evaluate them. The kinetic primer set the pattern that every macroscopic is a velocity moment of the distribution; here that pattern becomes finite sums over the discrete populations.
Through a Chapman-Enskog expansion [1] it is possible to recover the following macroscopic balance equations from LBE:
Note
These equations use index (Einstein) notation: a Greek subscript such as \(\alpha\) runs over the spatial directions \(x, y, z\), and any index repeated within a term is implicitly summed over, so \(\partial_\alpha u_\alpha\) means \(\partial_x u_x + \partial_y u_y + \partial_z u_z\). It is a compact way to write vector and tensor equations; see Einstein notation.
such that the fluid’s dynamic viscosity can be related to LBM mesoscopic variables as:
Valid only at low Mach
This recovery holds only at low Mach number, \(\mathrm{Ma} = u_{\mathrm{max}}/c_s\). The compressibility error grows as \(\mathrm{Ma}^2\), so the maximum velocity must be controlled; the practical working threshold used throughout the solver is \(\mathrm{Ma} < 0.1\), derived on the Chapman-Enskog page.
Each of the four macroscopics below plays a distinct role, and each is a moment of the populations, in the exact sense introduced in the kinetic primer:
Density \(\rho\) is the zeroth moment. It also fixes the pressure through the ideal-gas relation \(p = \rho c_s^2\) recovered in the Chapman-Enskog analysis, so there is no separate pressure variable to solve for.
Velocity \(u_\alpha\) is the first moment, corrected by half a force step (the Guo correction) so that the second-order accuracy of the scheme is preserved when a body force is present.
Non-equilibrium stress \(\Pi_{\alpha\beta}^{\mathrm{neq}}\) is the second moment of the non-equilibrium part of the populations. The Chapman-Enskog analysis identified this object as the carrier of the viscous stress (it is the \(\Pi_{\alpha\beta}^{(1)}\) of (9)).
Rate-of-strain \(S_{\alpha\beta}\) is obtained algebraically from \(\Pi_{\alpha\beta}^{\mathrm{neq}}\), with no finite differences. It is the input the Smagorinsky subgrid model needs, which is why an LBM-LES can compute its turbulent viscosity locally and cheaply.
Macroscopics such as the density \(\rho\) and velocity are recovered from the population moments:
We also define the second-order moment of non-equilibrium populations:
and the rate-of-strain is computed from the second-order moment of non-equilibrium populations:
Note
The rate-of-strain tensor \(S_{\alpha\beta}\) measures how fast a fluid element is deforming: it is the symmetric part of the velocity-gradient tensor, \(S_{\alpha\beta} = \tfrac{1}{2}(\partial_\alpha u_\beta + \partial_\beta u_\alpha)\). Its magnitude is what drives the Smagorinsky subgrid viscosity in the LES model. See strain-rate tensor.
In return, the second-order moment of non-equilibrium populations \(\Pi_{\alpha\beta}^{\mathrm{neq}}\) can be calculated from the rate-of-strain tensor with:
This last relation is the bridge back to the LES model: the Smagorinsky subgrid viscosity is computed from \(S_{\alpha\beta}\), and the modified relaxation it implies is fed back through \(\Pi_{\alpha\beta}^{\mathrm{neq}}\).
Next steps¶
Everything so far has been written with \(\Delta x = \Delta t = 1\). The next page makes that convention explicit and shows how to convert between these lattice units and physical metres and seconds for a real simulation.