Navier–Stokes equations
From Wikipedia, the free encyclopedia
The equations are useful because they describe the physics of many things of scientific and engineering interest. They may be used to model the weather, ocean currents, water flow in a pipe and air flow around a wing. The Navier–Stokes equations in their full and simplified forms help with the design of aircraft and cars, the study of blood flow, the design of power stations, the analysis of pollution, and many other things.
Coupled with Maxwell's equations they can be used to model and study magnetohydrodynamics.
The Navier–Stokes equations are also of great interest in a purely mathematical sense. Somewhat surprisingly, given their wide range of practical uses, it has not yet been proven that in three dimensions solutions always exist (existence), or that if they do exist, then they do not contain any singularity (they are smooth). These are called the Navier–Stokes existence and smoothness problems. The Clay Mathematics Institute has called this one of the seven most important open problems in mathematics and has offered a US$1,000,000 prize for a solution or a counter-example.[1]
Velocity field
The solution of the Navier–Stokes equations is a velocity not position. It is called a velocity field or flow field, which is a description of the velocity of the fluid at a given point in space and time. Once the velocity field is solved for, other quantities of interest, such as flow rate or drag force, may be found. This is different from what one normally sees in classical mechanics, where solutions are typically trajectories of position of a particle or deflection of a continuum. Studying velocity instead of position makes more sense for a fluid; however for visualization purposes one can compute various trajectories.Properties
Nonlinearity
The Navier–Stokes equations are nonlinear partial differential equations in almost every real situation.[2][3] In some cases, such as one-dimensional flow and Stokes flow (or creeping flow), the equations can be simplified to linear equations. The nonlinearity makes most problems difficult or impossible to solve and is the main contributor to the turbulence that the equations model.The nonlinearity is due to convective acceleration, which is an acceleration associated with the change in velocity over position. Hence, any convective flow, whether turbulent or not, will involve nonlinearity. An example of convective but laminar (nonturbulent) flow would be the passage of a viscous fluid (for example, oil) through a small converging nozzle. Such flows, whether exactly solvable or not, can often be thoroughly studied and understood.[4]
Turbulence
Turbulence is the time-dependent chaotic behavior seen in many fluid flows. It is generally believed that it is due to the inertia of the fluid as a whole: the culmination of time-dependent and convective acceleration; hence flows where inertial effects are small tend to be laminar (the Reynolds number quantifies how much the flow is affected by inertia). It is believed, though not known with certainty, that the Navier–Stokes equations describe turbulence properly.[5]The numerical solution of the Navier–Stokes equations for turbulent flow is extremely difficult, and due to the significantly different mixing-length scales that are involved in turbulent flow, the stable solution of this requires such a fine mesh resolution that the computational time becomes significantly infeasible for calculation or direct numerical simulation. Attempts to solve turbulent flow using a laminar solver typically result in a time-unsteady solution, which fails to converge appropriately. To counter this, time-averaged equations such as the Reynolds-averaged Navier–Stokes equations (RANS), supplemented with turbulence models, are used in practical computational fluid dynamics (CFD) applications when modeling turbulent flows. Some models include the Spalart-Allmaras, k-ω (k-omega), k-ε (k-epsilon), and SST models, which add a variety of additional equations to bring closure to the RANS equations. Large eddy simulation (LES) can also be used to solve these equations numerically. This approach is computationally more expensive-in time and in computer memory-than RANS, but produces better results because it explicitly resolves the larger turbulent scales.
Applicability
Together with supplemental equations (for example, conservation of mass) and well formulated boundary conditions, the Navier–Stokes equations seem to model fluid motion accurately; even turbulent flows seem (on average) to agree with real world observations.The Navier–Stokes equations assume that the fluid being studied is a continuum (it is infinitely divisible and not composed of particles such as atoms or molecules), and is not moving at relativistic velocities. At very small scales or under extreme conditions, real fluids made out of discrete molecules will produce results different from the continuous fluids modeled by the Navier–Stokes equations. Depending on the Knudsen number of the problem, statistical mechanics or possibly even molecular dynamics may be a more appropriate approach.
Another limitation is simply the complicated nature of the equations. Time-tested formulations exist for common fluid families, but the application of the Navier–Stokes equations to less common families tends to result in very complicated formulations and often to open research problems. For this reason, these equations are usually rewritten for Newtonian fluids where the viscosity model is linear; truly general models for the flow of other kinds of fluids (such as blood) do not, as of 2012, exist .[citation needed]
Derivation and description
The derivation of the Navier–Stokes equations begins with an application of Newton's second law: conservation of momentum (often alongside mass and energy conservation) being written for an arbitrary portion of the fluid. In an inertial frame of reference, the general form of the equations of fluid motion is:[6]Navier–Stokes equations (general)
is the flow velocity,
is the fluid density,
is the pressure,
is the (deviatoric) component of the total stress tensor, which has order two,
represents body forces (per unit volume) acting on the fluid,
is the del operator.
This equation is often written using the material derivative Dv/Dt, making it more apparent that this is a statement of Newton's second law:
Convective acceleration
An example of convection. Though the flow may be steady
(time-independent), the fluid decelerates as it moves down the diverging
duct (assuming incompressible or subsonic compressible flow), hence
there is an acceleration happening over position.
A significant feature of the Navier–Stokes equations is the presence of convective acceleration: the effect of time-independent acceleration of a fluid with respect to space. While individual fluid particles indeed experience time dependent-acceleration, the convective acceleration of the flow field is a spatial effect, one example being fluid speeding up in a nozzle.
Regardless of what kind of fluid is being dealt with, convective acceleration is a nonlinear effect. Convective acceleration is present in most flows (exceptions include one-dimensional incompressible flow), but its dynamic effect is disregarded in creeping flow (also called Stokes flow). Convective acceleration is represented by the nonlinear quantity:
or as 
with 
the tensor derivative of the velocity vector 
Both interpretations give the same result, independent of the coordinate system — provided is interpreted as the covariant derivative.[7]Interpretation as (v·∇)v
The convection term is often written as
is used. Usually this representation is preferred as it is simpler than the one in terms of the tensor derivative 
[7]Interpretation as v·(∇v)
Here
is the tensor derivative of the velocity vector, equal in Cartesian
coordinates to the component-by-component gradient. Note that the
gradient of a vector is being defined as ![\left[\nabla \mathbf{v}\right]_{mi}=\partial_m v_i](https://lh3.googleusercontent.com/blogger_img_proxy/AEn0k_sdRGoD9o6VJUf8170i8MuTbDqIG8SxYLRUEZUcIe3h2CWAiHjUxdMvtUTr6Kz8YexiahCaXJx1hO15F6Jn1YflRJG8oN2WkAv31qFbNp8p1v2rMasDmqAhKg_P3OWD3ku42w9IU1BTJcB0QpsbEQ=s0-d)
, so that ![\left[\mathbf{v}\cdot\left(\nabla \mathbf{v}\right)\right]_i=v_m \partial_m v_i=\left[(\mathbf{v}\cdot\nabla)\mathbf{v}\right]_i](https://lh3.googleusercontent.com/blogger_img_proxy/AEn0k_spu0Sdq6MwHuinkvzhr8DF4cXQT66PxTl5FmSwYkDJTMg5pGzYjIRRP5e6NpAf-UCvWxVilm38RRq9z7c_K4Tgg60rqVpwODNGVuSIMnAXGqwSyMS7XCQW6kJhfOnq5f263Oc8BS2tCKqCMZ-dMA=s0-d)
.In irrotational flow
The convection term may, by a vector calculus identity, be expressed without a tensor derivative:[8][9]
is equal to zero. Therefore, this reduces to only
Stresses
The effect of stress in the fluid is represented by the
and 
terms; these are gradients of surface forces, analogous to stresses in a solid. Here is called the pressure gradient and arises from the isotropic part of the Cauchy stress tensor, which has order two. This part is given by normal stresses that turn up in almost all situations, dynamic or not. The anisotropic part of the stress tensor gives rise to , which conventionally describes viscous forces; for incompressible flow, this is only a shear effect. Thus, is the deviatoric stress tensor, and the stress tensor is equal to:[10]
is the 3×3 identity matrix. In the Navier–Stokes equations, only the gradient
of pressure matters, not the pressure itself. The effect of the
pressure gradient on the flow is to accelerate the fluid in the
direction from high pressure to low pressure.The stress terms p and
are yet unknown, so the general form of the equations of motion is not
usable to solve problems. Besides the equations of motion—Newton's
second law—a force model is needed relating the stresses to the fluid
motion.[11]
For this reason, assumptions based on natural observations are often
applied to specify the stresses in terms of the other flow variables,
such as velocity and density.The Navier–Stokes equations result from the following assumptions on the deviatoric stress tensor
:[12]- the deviatoric stress vanishes for a fluid at rest, and, by Galilean invariance, also does not depend directly on the flow velocity, but only on spatial derivatives of the flow velocity
- the deviatoric stress is expressed as the double-dot product of the tensor gradient
, i.e.,
- the fluid is assumed to be isotropic, as with gases and simple liquids, and consequently
where
is the rate-of-strain tensor and
is the rate of expansion of the flow
- the deviatoric stress tensor has zero trace, so for a three-dimensional flow 2μ + 3μ" = 0
The dynamic viscosity μ need not be constant – in general, it depends on conditions like temperature and pressure, and, in turbulence modeling, eddy viscosity approximates the average deviatoric stress.The pressure p is modeled by an equation of state.[13] For the special case of an incompressible flow, the pressure constrains the flow so that the volume of fluid elements is constant: isochoric flow resulting in a solenoidal velocity field with
[14]Other forces
The vector field represents body forces. Typically, these consist of only gravity
forces, but may include others, such as electromagnetic forces. In
non-inertial coordinate frames, other "forces" associated with rotating coordinates may arise.Often, these forces may be represented as the gradient of some scalar quantity
, with 
in which case they are called conservative forces. Gravity in the z direction, for example, is the gradient of 
. Because pressure from such gravitation arises only as a gradient, we may include it in the pressure term as a body force 
The pressure and force terms on the right-hand side of the Navier–Stokes equation become
Other equations
The Navier–Stokes equations are strictly a statement of the conservation of momentum. To fully describe fluid flow, more information is needed, how much depending on the assumptions made. This additional information may include boundary data (no-slip, capillary surface, etc.), conservation of mass, conservation of energy, and/or an equation of state.Continuity equation
Regardless of the flow assumptions, a statement of the conservation of mass is generally necessary. This is achieved through the mass continuity equation, given in its most general form as:
Incompressible flow of Newtonian fluids
The equations are simplified in the case of incompressible flow of a Newtonian fluid. Incompressibility rules out sound propagation or shock waves, so this simplification is not useful if these phenomena are of interest. The incompressible flow assumption typically holds well even with a "compressible" fluid — such as air at room temperature — at low Mach numbers up to about Mach 0.3. With incompressible flow and constant viscosity, the Navier–Stokes equations read[15]Navier–Stokes equations (Incompressible flow)
Navier–Stokes equations (Incompressible flow)
becomes 
, where 
is the vector Laplacian.[16]It's well worth observing the meaning of each term (compare to the Cauchy momentum equation):
Another important observation is that viscosity is represented by the vector Laplacian of the velocity field, interpreted here as the difference between the velocity at a point and the mean velocity in a small surrounding volume. This implies that – for a Newtonian fluid – viscosity operates as a diffusion of momentum, in much the same way as the diffusion of heat in the heat equation, also expressed with the Laplacian.
If temperature effects are also neglected, the only "other" equation (apart from initial/boundary conditions) needed is the mass continuity equation. Under the assumption of incompressibility, the density of a fluid parcel is constant, and when using the substantive derivative it follows easily that the continuity equation simplifies to:
These equations are commonly used in 3 coordinates systems: Cartesian, cylindrical, and spherical. While the Cartesian equations seem to follow directly from the vector equation above, the vector form of the Navier–Stokes equation involves some tensor calculus which means that writing it in other coordinate systems is not as simple as doing so for scalar equations (such as the heat equation).
Cartesian coordinates
With the velocity vector expanded as
, we may write the vector equation explicitly,
The continuity equation reads:
does not change for any fluid parcel, and its material derivative vanishes:
. The continuity equation is reduced to:
Cylindrical coordinates
A change of variables on the Cartesian equations will yield[15] the following momentum equations for r,
, and z:![\begin{align}
r:\ &\rho \left(\frac{\partial u_r}{\partial t} + u_r \frac{\partial u_r}{\partial r} +
\frac{u_{\phi}}{r} \frac{\partial u_r}{\partial \phi} + u_z \frac{\partial u_r}{\partial z} - \frac{u_{\phi}^2}{r}\right) = {}\\
&-\frac{\partial p}{\partial r} + \mu \left[\frac{1}{r}\frac{\partial}{\partial r}\left(r \frac{\partial u_r}{\partial r}\right) +
\frac{1}{r^2}\frac{\partial^2 u_r}{\partial \phi^2} + \frac{\partial^2 u_r}{\partial z^2} - \frac{u_r}{r^2} -
\frac{2}{r^2}\frac{\partial u_\phi}{\partial \phi} \right] + \rho g_r \\
\phi:\ &\rho \left(\frac{\partial u_{\phi}}{\partial t} + u_r \frac{\partial u_{\phi}}{\partial r} +
\frac{u_{\phi}}{r} \frac{\partial u_{\phi}}{\partial \phi} + u_z \frac{\partial u_{\phi}}{\partial z} + \frac{u_r u_{\phi}}{r}\right) = {}\\
&-\frac{1}{r}\frac{\partial p}{\partial \phi} + \mu \left[\frac{1}{r}\frac{\partial}{\partial r}\left(r \frac{\partial u_{\phi}}{\partial r}\right) +
\frac{1}{r^2}\frac{\partial^2 u_{\phi}}{\partial \phi^2} + \frac{\partial^2 u_{\phi}}{\partial z^2} + \frac{2}{r^2}\frac{\partial u_r}{\partial \phi} -
\frac{u_{\phi}}{r^2}\right] + \rho g_{\phi} \\
z:\ &\rho \left(\frac{\partial u_z}{\partial t} + u_r \frac{\partial u_z}{\partial r} + \frac{u_{\phi}}{r} \frac{\partial u_z}{\partial \phi} +
u_z \frac{\partial u_z}{\partial z}\right) = {}\\
&-\frac{\partial p}{\partial z} + \mu \left[\frac{1}{r}\frac{\partial}{\partial r}\left(r \frac{\partial u_z}{\partial r}\right) +
\frac{1}{r^2}\frac{\partial^2 u_z}{\partial \phi^2} + \frac{\partial^2 u_z}{\partial z^2}\right] + \rho g_z.
\end{align}](https://lh3.googleusercontent.com/blogger_img_proxy/AEn0k_vJSP-zKxoklA1gaQ0dSDYWffo0rSRlotlo81Hdl6yAz_7gadWmzDkpyW63OYO6ocUv5N1ehLFsiwnsnsgl-sFvK8AVS000rE6FzPrqMoojdhxgj3oX4Nih81tnSKquf1mj3KrlaNTL-o7qjdHWSw=s0-d)
), and the remaining quantities are independent of :![\begin{align}
\rho \left(\frac{\partial u_r}{\partial t} + u_r \frac{\partial u_r}{\partial r} + u_z \frac{\partial u_r}{\partial z}\right)
&= -\frac{\partial p}{\partial r} + \mu \left[\frac{1}{r}\frac{\partial}{\partial r}\left(r \frac{\partial u_r}{\partial r}\right) +
\frac{\partial^2 u_r}{\partial z^2} - \frac{u_r}{r^2}\right] + \rho g_r \\
\rho \left(\frac{\partial u_z}{\partial t} + u_r \frac{\partial u_z}{\partial r} + u_z \frac{\partial u_z}{\partial z}\right)
&= -\frac{\partial p}{\partial z} + \mu \left[\frac{1}{r}\frac{\partial}{\partial r}\left(r \frac{\partial u_z}{\partial r}\right) +
\frac{\partial^2 u_z}{\partial z^2}\right] + \rho g_z \\
\frac{1}{r}\frac{\partial}{\partial r}\left(r u_r\right) + \frac{\partial u_z}{\partial z} &= 0.
\end{align}](https://lh3.googleusercontent.com/blogger_img_proxy/AEn0k_txSph0Zy-QbsTQEdD-31-UbxDSN7mJ_xCBOHkizpPz7ZG3LiCYkmpb3u9vnPT7MzHfTsUlOE6LNW1Zw5vU2AV8u23BEKKoUaXK8497UoNh9A3yHFATdavO_puP9lybZP9OXZbLPwPVE6TGu4_c=s0-d)
Spherical coordinates
In spherical coordinates, the r, ϕ, and θ momentum equations are[15] (note the convention used: θ is polar angle, or colatitude,[17] 0 ≤ θ ≤ π):![\begin{align}
r:\ &\rho \left(\frac{\partial u_r}{\partial t} + u_r \frac{\partial u_r}{\partial r} + \frac{u_{\phi}}{r \sin(\theta)} \frac{\partial u_r}{\partial \phi} +
\frac{u_{\theta}}{r} \frac{\partial u_r}{\partial \theta} - \frac{u_{\phi}^2 + u_{\theta}^2}{r}\right) =
-\frac{\partial p}{\partial r} + \rho g_r + \\
&\mu \left[\frac{1}{r^2} \frac{\partial}{\partial r}\left(r^2 \frac{\partial u_r}{\partial r}\right) +
\frac{1}{r^2 \sin(\theta)^2} \frac{\partial^2 u_r}{\partial \phi^2} +
\frac{1}{r^2 \sin(\theta)} \frac{\partial}{\partial \theta}\left(\sin(\theta) \frac{\partial u_r}{\partial \theta}\right) - 2\frac{u_r +
\frac{\partial u_{\theta}}{\partial \theta} + u_{\theta} \cot(\theta)}{r^2} - \frac{2}{r^2 \sin(\theta)} \frac{\partial u_{\phi}}{\partial \phi}
\right] \\
\phi:\ &\rho \left(\frac{\partial u_{\phi}}{\partial t} + u_r \frac{\partial u_{\phi}}{\partial r} +
\frac{u_{\phi}}{r \sin(\theta)} \frac{\partial u_{\phi}}{\partial \phi} + \frac{u_{\theta}}{r} \frac{\partial u_{\phi}}{\partial \theta} +
\frac{u_r u_{\phi} + u_{\phi} u_{\theta} \cot(\theta)}{r}\right) =
-\frac{1}{r \sin(\theta)} \frac{\partial p}{\partial \phi} + \rho g_{\phi} + \\
&\mu \left[\frac{1}{r^2} \frac{\partial}{\partial r}\left(r^2 \frac{\partial u_{\phi}}{\partial r}\right) +
\frac{1}{r^2 \sin(\theta)^2} \frac{\partial^2 u_{\phi}}{\partial \phi^2} +
\frac{1}{r^2 \sin(\theta)} \frac{\partial}{\partial \theta}\left(\sin(\theta) \frac{\partial u_{\phi}}{\partial \theta}\right) +
\frac{2 \sin(\theta) \frac{\partial u_r}{\partial \phi} + 2 \cos(\theta) \frac{\partial u_{\theta}}{\partial \phi} -
u_{\phi}}{r^2 \sin(\theta)^2}
\right] \\
\theta:\ &\rho \left(\frac{\partial u_{\theta}}{\partial t} + u_r \frac{\partial u_{\theta}}{\partial r} +
\frac{u_{\phi}}{r \sin(\theta)} \frac{\partial u_{\theta}}{\partial \phi} +
\frac{u_{\theta}}{r} \frac{\partial u_{\theta}}{\partial \theta} + \frac{u_r u_{\theta} - u_{\phi}^2 \cot(\theta)}{r}\right) =
-\frac{1}{r} \frac{\partial p}{\partial \theta} + \rho g_{\theta} + \\
&\mu \left[\frac{1}{r^2} \frac{\partial}{\partial r}\left(r^2 \frac{\partial u_{\theta}}{\partial r}\right) +
\frac{1}{r^2 \sin(\theta)^2} \frac{\partial^2 u_{\theta}}{\partial \phi^2} +
\frac{1}{r^2 \sin(\theta)} \frac{\partial}{\partial \theta}\left(\sin(\theta) \frac{\partial u_{\theta}}{\partial \theta}\right) +
\frac{2}{r^2} \frac{\partial u_r}{\partial \theta} - \frac{u_{\theta} +
2 \cos(\theta) \frac{\partial u_{\phi}}{\partial \phi}}{r^2 \sin(\theta)^2}
\right].
\end{align}](https://lh3.googleusercontent.com/blogger_img_proxy/AEn0k_srt4_yqO7n9XVRTG1Uqkxq9PxGpMu5s4AK3li8hynduKhiwHBCQA-RPYCVTkJfbP0lT4PTFLNW3vEBrRdkmOLwH5PgS-FZM5M7W_QvHVN5YeO7ZrzsKG6YRBbq02T6A8X5qi8bxtppwDgnvUgINg=s0-d)
from the viscous terms. However, doing so would undesirably alter the structure of the Laplacian and other quantities.Stream function formulation
Taking the curl of the Navier–Stokes equation results in the elimination of pressure. This is especially easy to see if 2D Cartesian flow is assumed (
and no dependence of anything on z), where the equations reduce to:
through
is the (2D) biharmonic operator and 
is the kinematic viscosity, 
. We can also express this compactly using the Jacobian determinant:
In axisymmetric flow another stream function formulation, called the Stokes stream function, can be used to describe the velocity components of an incompressible flow with one scalar function.
Pressure-free velocity formulation
The incompressible Navier–Stokes equation is a differential algebraic equation, having the inconvenient feature that there is no explicit mechanism for advancing the pressure in time. Consequently, much effort has been expended to eliminate the pressure from all or part of the computational process. The stream function formulation above eliminates the pressure (in 2D) at the expense of introducing higher derivatives and elimination of the velocity, which is the primary variable of interest.The incompressible Navier–Stokes equation is composite, the sum of two orthogonal equations,
and 
are solenoidal and irrotational projection operators satisfying 
and 
and 
are the non-conservative and conservative parts of the body force. This result follows from the Helmholtz Theorem
(also known as the fundamental theorem of vector calculus). The first
equation is a pressureless governing equation for the velocity, while
the second equation for the pressure is a functional of the velocity and
is related to the pressure Poisson equation.The explicit functional form of the projection operator in 3D is found from the Helmholtz Theorem
An equivalent weak or variational form of the equation, proved to produce the same velocity solution as the Navier–Stokes equation,[18] is given by,
satisfying appropriate boundary conditions. Here, the projections are
accomplished by the orthogonality of the solenoidal and irrotational
function spaces. The discrete form of this is imminently suited to
finite element computation of divergence-free flow, as we shall see in
the next section. There we will be able to address the question, "How
does one specify pressure-driven (Poiseuille) problems with a
pressureless governing equation?"The absence of pressure forces from the governing velocity equation demonstrates that the equation is not a dynamic one, but rather a kinematic equation where the divergence-free condition serves the role of a conservation law. This all would seem to refute the frequent statements that the incompressible pressure enforces the divergence-free condition.
Discrete velocity
With partitioning of the problem domain and defining basis functions on the partitioned domain, the discrete form of the governing equation is,
We further restrict discussion to continuous Hermite finite elements which have at least first-derivative degrees-of-freedom. With this, one can draw a large number of candidate triangular and rectangular elements from the plate-bending literature. These elements have derivatives as components of the gradient. In 2D, the gradient and curl of a scalar are clearly orthogonal, given by the expressions,
![\nabla\phi = \left[\frac{\partial \phi}{\partial x},\,\frac{\partial \phi}{\partial y}\right]^\mathrm{T}, \quad
\nabla\times\phi = \left[\frac{\partial \phi}{\partial y},\,-\frac{\partial \phi}{\partial x}\right]^\mathrm{T}.](https://lh3.googleusercontent.com/blogger_img_proxy/AEn0k_tEyv3ypwzz5-R_aETO8SNIIEK63x7cTI4lChqj55pgWdYFdxNzQfUmEBohz5-bFv6BQzzX89joGJHDdtZtr-m4IRi0M7OUXF-G7VY19VQwx5l8QQHyhmvni4Hc8_6S4vbes_qmuSeS8obgpKSSUw=s0-d)
Taking the curl of the scalar stream function elements gives divergence-free velocity elements.[19][20] The requirement that the stream function elements be continuous assures that the normal component of the velocity is continuous across element interfaces, all that is necessary for vanishing divergence on these interfaces.
Boundary conditions are simple to apply. The stream function is constant on no-flow surfaces, with no-slip velocity conditions on surfaces. Stream function differences across open channels determine the flow. No boundary conditions are necessary on open boundaries, though consistent values may be used with some problems. These are all Dirichlet conditions.
The algebraic equations to be solved are simple to set up, but of course are non-linear, requiring iteration of the linearized equations.
Similar considerations apply to three-dimensions, but extension from 2D is not immediate because of the vector nature of the potential, and there exists no simple relation between the gradient and the curl as was the case in 2D.
Pressure recovery
Recovering pressure from the velocity field is easy. The discrete weak equation for the pressure gradient is,
one would choose the irrotational vector elements obtained from the gradient of the pressure element.Compressible flow of Newtonian fluids
There are some phenomena that are closely linked with fluid compressibility. One of the obvious examples is sound. Description of such phenomena requires more general presentation of the Navier–Stokes equation that takes into account fluid compressibility. If viscosity is assumed a constant, one additional term appears, as shown here:[21][22]
is the second viscosity.Application to specific problems
The Navier–Stokes equations, even when written explicitly for specific fluids, are rather generic in nature and their proper application to specific problems can be very diverse. This is partly because there is an enormous variety of problems that may be modeled, ranging from as simple as the distribution of static pressure to as complicated as multiphase flow driven by surface tension.Generally, application to specific problems begins with some flow assumptions and initial/boundary condition formulation, this may be followed by scale analysis to further simplify the problem.
Visualization of a) parallel flow and b) radial flow.
a) Assume steady, parallel, one dimensional, non-convective pressure-driven flow between parallel plates, the resulting scaled (dimensionless) boundary value problem is:
b) Difficulties may arise when the problem becomes slightly more complicated. A seemingly modest twist on the parallel flow above would be the radial flow between parallel plates; this involves convection and thus non-linearity. The velocity field may be represented by a function
that must satisfy:
Some exact solutions to the Navier–Stokes equations exist. Examples of degenerate cases — with the non-linear terms in the Navier–Stokes equations equal to zero — are Poiseuille flow, Couette flow and the oscillatory Stokes boundary layer. But also more interesting examples, solutions to the full non-linear equations, exist; for example the Taylor–Green vortex.[24][25][26] Note that the existence of these exact solutions does not imply they are stable: turbulence may develop at higher Reynolds numbers.
A nice steady-state example with no singularities comes from considering the flow along the lines of a Hopf fibration. Let r be a constant radius to the inner coil. One set of solutions is given by:[28]
is a constant, neither does it deal with the uniqueness of the Navier–Stokes equations with respect to any turbulence properties.) It is also worth pointing out that the components of the velocity vector are exactly those from the Pythagorean quadruple parametrization. Other choices of density and pressure are possible with the same velocity field:Wyld diagrams
Wyld diagrams are bookkeeping graphs that correspond to the Navier–Stokes equations via a perturbation expansion of the fundamental continuum mechanics. Similar to the Feynman diagrams in quantum field theory, these diagrams are an extension of Keldysh's technique for nonequilibrium processes in fluid dynamics. In other words, these diagrams assign graphs to the (often) turbulent phenomena in turbulent fluids by allowing correlated and interacting fluid particles to obey stochastic processes associated to pseudo-random functions in probability distributions.[29]
The Navier–Stokes equations are used extensively in video games in order to model a wide variety of natural phenomena. Simulations of small-scale gaseous fluids, such as fire and smoke are often based on the seminal paper "Real-Time Fluid Dynamics for Games"[30] by Jos Stam, which elaborates one of the methods proposed in Stam's earlier, more famous paper "Stable Fluids"[31] from 1999. Stam proposes stable fluid simulation using a Navier–Stokes solution method from 1968, coupled with an unconditionally stable semi-Lagrangian advection scheme, as first proposed in 1992.
More recent implementations based upon this work run on the GPU as opposed to the CPU and achieve a much higher degree of performance.[32][33] Many improvements have been proposed to Stam's original work, which suffers inherently from high numerical dissipation in both velocity and mass.
An introduction to interactive fluid simulation can be found in the 2007 ACM SIGGRAPH course, Fluid Simulation for Computer Animation.[34]
