Reynolds-Averaged Navier Stokes Equations

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The Reynolds-averaged Navier–Stokes (RANS) equations are time-averaged (1) equations of motion for fluid flow. They are primarily used while dealing with turbulent flows. These equations can be used with approximations based on knowledge of the properties of flow turbulence to give approximate averaged solutions to the Navier–Stokes equations. For a stationary, incompressible flow of Newtonian fluid, these equations can be written in Einstein notation as:

\rho \frac{\partial \bar{u}_j \bar{u}_i }{\partial x_j}
= \rho \bar{f}_i
+ \frac{\partial}{\partial x_j} 
\left[ - \bar{p}\delta_{ij} 
+ \mu \left( \frac{\partial \bar{u}_i}{\partial x_j} + \frac{\partial \bar{u}_j}{\partial x_i} \right)
- \rho \overline{u_i^\prime u_j^\prime} \right ].

The left hand side of this equation represents the change in mean momentum of fluid element owing to the unsteadiness in the mean flow and the convection by the mean flow. This change is balanced by the mean body force, the isotropic stress owing to the mean pressure field, the viscous stresses, and apparent stress  \left( - \rho \overline{u_i^\prime u_j^\prime} \right) owing to the fluctuating velocity field, generally referred to as the Reynolds stress. This nonlinear Reynolds stress term requires additional modeling to close the RANS equation for solving, and has led to the creation of many different turbulence models.


(1) The true time average ( \bar{X} ) of a variable (x) is defined by

 \bar{X} = \lim_{T \to \infty}\frac{1}{T}\int_{t_0}^{t_0+T} x\, dt.

For this to be a well-defined term, the limit ( \bar{X} ) must be independent of the initial condition at t0. In the case of a chaotic dynamical system, which the equations under turbulent conditions are thought to be, this means that the system can have only one strange attractor, a result that has yet to be proved for the Navier-Stokes equations. However, assuming the limit exists (which it does for any bounded system, which fluid velocities certainly are), there exists some T such that integration from t0 to T is arbitrarily close to the average. This means that given transient data over a sufficiently large time, the average can be numerically computed within some small error. However, there is no analytical way to obtain an upper bound on T.