Reynolds-averaged Navier–Stokes equations

Template:Short description Template:More citations needed The Reynolds-averaged Navier–Stokes equations (RANS equations) are time-averagedTemplate:Efn equations of motion for fluid flow. The idea behind the equations is Reynolds decomposition, whereby an instantaneous quantity is decomposed into its time-averaged and fluctuating quantities, an idea first proposed by Osborne Reynolds.<ref>Template:Cite journal</ref> The RANS equations are primarily used to describe turbulent flows. These equations can be used with approximations based on knowledge of the properties of flow turbulence to give approximate time-averaged solutions to the Navier–Stokes equations. For a stationary flow of an incompressible Newtonian fluid, these equations can be written in Einstein notation in Cartesian coordinates as: <math display="block">\rho\bar{u}_j \frac{\partial \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 ]. </math>

The left hand side of this equation represents the change in mean momentum of a 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 <math> \left( - \rho \overline{u_i^\prime u_j^\prime} \right)</math> 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. The time-average operator <math>\overline{.}</math> is a Reynolds operator.

Derivation of RANS equations

The basic tool required for the derivation of the RANS equations from the instantaneous Navier–Stokes equations is the Reynolds decomposition. Reynolds decomposition refers to separation of the flow variable (like velocity <math>u</math>) into the mean (time-averaged) component (<math>\overline{u}</math>) and the fluctuating component (<math>u^{\prime}</math>). Because the mean operator is a Reynolds operator, it has a set of properties. One of these properties is that the mean of the fluctuating quantity is equal to zero <math>(\bar{u'})</math> <ref>Template:Cite book</ref> Some authors prefer using <math>U</math> instead of <math> \bar{u} </math> for the mean term (since an overbar is sometimes used to represent a vector). In this case, the fluctuating term <math>u^\prime</math> is represented instead by <math>u</math>. This is possible because the two terms do not appear simultaneously in the same equation. To avoid confusion, the notation <math> u</math>, <math>\bar{u}</math>, and <math>u' </math> will be used to represent the instantaneous, mean, and fluctuating terms, respectively.

The properties of Reynolds operators are useful in the derivation of the RANS equations. Using these properties, the Navier–Stokes equations of motion, expressed in tensor notation, are (for an incompressible Newtonian fluid): <math display="block"> \frac{\partial u_i}{\partial x_i} = 0 </math> <math display="block"> \frac{\partial u_i}{\partial t} + u_j \frac{\partial u_i}{\partial x_j} = f_i - \frac{1}{\rho} \frac{\partial p}{\partial x_i} + \nu \frac{\partial^2 u_i}{\partial x_j \partial x_j} </math> where <math>f_i</math> is a vector representing external forces.

Next, each instantaneous quantity can be split into time-averaged and fluctuating components, and the resulting equation time-averaged, Template:Efn = 0 </math> <math display="block"> \overline{\frac{\partial \left( \bar{u}_i + u_i^\prime\right)}{\partial t}} + \overline{\left( \bar{u}_j + u_j^\prime\right) \frac{\partial \left( \bar{u_i} + u_i^\prime\right)}{\partial x_j}} = \overline{\left( \bar{f}_i + f_i^\prime\right)} - \frac{1}{\rho} \overline{\frac{\partial \left(\bar{p} + p^\prime\right)}{\partial x_i}} + \nu \overline{\frac{\partial^2 \left( \bar{u}_i + u_i^\prime\right)}{\partial x_i \partial x_j}}. </math>

Note that the nonlinear terms (like <math> \overline{u_i u_j} </math>) can be simplified to <math display="block"> \overline{u_i u_j} = \overline{\left( \bar{u}_i + u_i^\prime \right)\left( \bar{u_j} + u_j^\prime \right) } = \overline{\bar{u_i}\bar{u}_j + \bar{u}_i u_j^\prime + u_i^\prime\bar{u}_j + u_i^\prime u_j^\prime} = \bar{u}_i\bar{u}_j + \overline{u_i^\prime u_j^\prime} </math> }} to yield:

<math display="block"> \frac{\partial \bar{u}_i}{\partial x_i} = 0</math> <math display="block"> \frac{\partial \bar{u}_i}{\partial t} + \bar{u}_j\frac{\partial \bar{u}_i }{\partial x_j} + \overline{u_j^\prime \frac{\partial u_i^\prime }{\partial x_j}} = \bar{f}_i - \frac{1}{\rho}\frac{\partial \bar{p}}{\partial x_i} + \nu \frac{\partial^2 \bar{u}_i}{\partial x_j \partial x_j}. </math>

The momentum equation can also be written as, Template:Efn

<math display="block"> \frac{\partial \bar{u}_i}{\partial t} + \bar{u}_j\frac{\partial \bar{u}_i }{\partial x_j} = \bar{f}_i - \frac{1}{\rho}\frac{\partial \bar{p}}{\partial x_i} + \nu \frac{\partial^2 \bar{u}_i}{\partial x_j \partial x_j} - \frac{\partial \overline{u_i^\prime u_j^\prime }}{\partial x_j}. </math> On further manipulations this yields, <math display="block">\rho \frac{\partial \bar{u}_i}{\partial t} + \rho \bar{u}_j \frac{\partial \bar{u}_i }{\partial x_j} = \rho \bar{f}_i + \frac{\partial}{\partial x_j} \left[ - \bar{p}\delta_{ij} + 2\mu \bar{S}_{ij} - \rho \overline{u_i^\prime u_j^\prime} \right] </math>

where, <math> \bar{S}_{ij} = \frac{1}{2}\left( \frac{\partial \bar{u}_i}{\partial x_j} + \frac{\partial \bar{u}_j}{\partial x_i} \right) </math> is the mean rate of strain tensor.

Finally, since integration in time removes the time dependence of the resultant terms, the time derivative must be eliminated, leaving: <math display="block">\rho \bar{u}_j\frac{\partial \bar{u}_i }{\partial x_j} = \rho \bar{f_i} + \frac{\partial}{\partial x_j} \left[ - \bar{p}\delta_{ij} + 2\mu \bar{S}_{ij} - \rho \overline{u_i^\prime u_j^\prime} \right]. </math>

Equations of Reynolds stress

The time evolution equation of Reynolds stress is given by:<ref>Template:Cite journal</ref> <math display="block"> \frac{\partial \overline{u_i^\prime u_j^\prime}}{\partial t}

 + \bar{u}_k \frac{\partial \overline{u_i^\prime u_j^\prime}}{\partial x_k} =
 -\overline{u_i^\prime u_k^\prime}\frac{\partial \bar{u}_j}{\partial x_k}
 -\overline{u_j^\prime u_k^\prime}\frac{\partial \bar{u}_i}{\partial x_k}
 +\overline{ \frac{p^\prime}{\rho}\left( \frac{\partial u_i^\prime}{\partial x_j}
        +\frac{\partial u_j^\prime}{\partial x_i} \right) }
 - \frac{\partial }{\partial x_k} \left( \overline{u_i^\prime u_j^\prime u_k^\prime}
      + \frac{\overline{p^\prime u_i^\prime } }{\rho} \delta_{jk}
      + \frac{\overline{p^\prime u_j^\prime } }{\rho} \delta_{ik}
      - \nu \frac{\partial \overline{u_i^\prime u_j^\prime}}{\partial x_k} \right)
 -2 \nu \overline{\frac{\partial u_i^\prime}{\partial x_k} \frac{\partial u_j^\prime}{\partial x_k}}

</math> This equation is very complicated. If <math>\overline{u_i^\prime u_j^\prime}</math> is traced, turbulence kinetic energy is obtained. The last term <math>\nu \overline{\frac{\partial u_i^\prime}{\partial x_k} \frac{\partial u_j^\prime}{\partial x_k}}</math> is turbulent dissipation rate. All RANS models are based on the above equation.

Applications (RANS modelling)

• A model for testing performance was determined that, when combined with the vortex lattice (VLM) or boundary element method (BEM), RANS was found useful for modelling the flow of water between two contrary rotation propellers, where VLM or BEM are applied to the propellers and RANS is used for the dynamically fluxing inter-propeller state.<ref>{{#invoke:citation/CS1|citation

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• The RANS equations have been widely utilized as a model for determining flow characteristics and assessing wind comfort in urban environments. This computational approach can be executed through direct calculations involving the solution of the RANS equations, or through an indirect method involving the training of machine learning algorithms using the RANS equations as a basis. The direct approach is more accurate than the indirect approach but it requires expertise in numerical methods and computational fluid dynamics (CFD), as well as substantial computational resources to handle the complexity of the equations.<ref>Template:Cite journal</ref>

See also

• Favre averaging